_images/ALminer_logo_header.jpg

ALminer: ALMA Archive Mining & Visualization Toolkit#

alminer is a Python-based code to effectively query, analyse, and visualize the ALMA science archive. It also allows users to directly download ALMA data products and/or raw data for further image processing.

Installation#

The easiest way to install alminer is with pip:

pip install alminer

To obtain the most recent version of the code from GitHub:

pip install https://github.com/emerge-erc/ALminer/archive/refs/heads/main.zip

Or clone and install from source:

# If you have a Github account:
git clone git@github.com:emerge-erc/ALminer.git
# If you do not:
git clone https://github.com/emerge-erc/ALminer.git

# After cloning:
cd ALminer
pip install .

Note that depending on your setup, you may need to use pip3.

Dependencies#

The dependencies are numpy, matplotlib, pandas, pyvo, astropy version 3.1.2 or higher, and astroquery version 0.4.2.dev6649 or higher. We only use the astroquery package for downloading data from the ALMA archive. The strict requirement to have its newest version is due to recent changes made to the ALMA archive. alminer works in Python 3.

Getting started#

We have created an extensive tutorial Jupyter Notebook where all alminer features have been highlighted. This is an excellent starting point to get familiar with all the possibilities; a glossary of all functions is provided at the bottom of this notebook. We highly recommend working in a Jupyter notebook environment in order to make use of alminer’s visualization tools. We aim to keep adding new notebooks relevant for various sub-fields in the future.

Note that the Jupyter notebooks may be outdated. The most up-to-date information can be found on this documentation page.

Note

To work with the tutorial notebook interactively badge_svg

1. Query tools#

This Section introduces three methods to query the ALMA archive:

General notes about the querying functions:

  • All querying functions search the ALMA archive for public data by default. To include both public and proprietary data in the search, set public=None. Similarly, to search for only propietary data, set public=False.

  • All querying functions search the ALMA archive for both published and unpublished data. To include only unpublished data, set published=False.

  • The querying functions will by default print a summary of the observations, including a list of target names. For large queries, it is useful to turn this feature off in order to not have a long list of targets printed to screen. To turn off this feature, simply set print_targets=False.

  • The queried archive service is by default the Europeran one (ESO). Other services can be specified through the tap_service argument. Options are: ESO, NRAO, or NAOJ.

  • The queries return all possible observations in PANDAS DataFrame format that can be used to further narrow down your search as demonstrated in Section 2.

Load libraries

[1]:

import alminer
import pandas
from astropy.io import ascii

1.1 Query by target name#

The alminer.target function allows one to query objects by name. This function uses the Astropy SESAME resolver which searches multiple databases (Simbad, NED, VizieR) to obtain the coordinates of the object of interest, and then queries the ALMA archive for all observations that contain those coordinates (corresponding to the case of point=True which is the default). When point=False, the function will return all observations that overlap with a cone extending the position of interest and a search radius around it. The search radius is by default 1.0 arcminute, but can be modified using the search_radius keyword (in arcmin units).

Example 1.1.1: query two sources by name#
[2]:

myquery = alminer.target(['Orion KL', "AB Aur"])

================================
alminer.target results
================================
Target = Orion KL
--------------------------------
Number of projects = 23
Number of observations = 38
Number of unique subbands = 129
Total number of subbands = 160
18 target(s) with ALMA data = ['OMC1_NW', 'Orion-KL', 'orion_kl', 'BN-KL', 'OMC1_SE', 'Orion_KL_Field_3_North-west_Clump', 'BN', 'OrionKL', 'Orion', 'Orion_KL', 'orion-IRc2', 'OMC-1', 'ONC', 'Orion1', 'OMC-1_Region2', 'ONC_Mosaic', 'Orion_Source_I', 'Orion_BNKL_source_I']
--------------------------------
Target = AB Aur
--------------------------------
Number of projects = 3
Number of observations = 3
Number of unique subbands = 17
Total number of subbands = 17
3 target(s) with ALMA data = ['AB_Auriga', 'AB_Aur', 'ab_aurigae']
--------------------------------
Example 1.1.2: query a list of objects by name#

First create a catalog or a list of object names. In this example, the catalog Sample_cat.dat has the following content:

 Name     RA      DEC
------ -------- --------
AB_Aur  73.9412  30.5511
AK_Sco 253.6867 -36.8886
AS_310 278.3383  -4.9683
AS_470 324.0592  57.3586
AS_477 328.1421  47.2289

Note that the column that is used is the Name column and the coordinates are ignored in this example.

[3]:

mylist = ascii.read("Sample_cat.dat", header_start=0, data_start=1)
myquery = alminer.target(mylist['Name'])

================================
alminer.target results
================================
Target = AB_Aur
--------------------------------
Number of projects = 3
Number of observations = 3
Number of unique subbands = 17
Total number of subbands = 17
3 target(s) with ALMA data = ['AB_Auriga', 'AB_Aur', 'ab_aurigae']
--------------------------------
Target = AK_Sco
--------------------------------
Number of projects = 3
Number of observations = 3
Number of unique subbands = 12
Total number of subbands = 12
2 target(s) with ALMA data = ['AK_Sco', 'HIP_82747']
--------------------------------
Target = AS_310
--------------------------------
No observations found.
--------------------------------
Target = AS_470
--------------------------------
No observations found.
--------------------------------
Target = AS_477
--------------------------------
No observations found.
--------------------------------
Example 1.1.3: include proprietary data#
[4]:

myquery = alminer.target(mylist['Name'], public=None)

================================
alminer.target results
================================
Target = AB_Aur
--------------------------------
Number of projects = 6
Number of observations = 10
Number of unique subbands = 56
Total number of subbands = 62
3 target(s) with ALMA data = ['AB_Aur', 'AB_Auriga', 'ab_aurigae']
--------------------------------
Target = AK_Sco
--------------------------------
Number of projects = 4
Number of observations = 4
Number of unique subbands = 16
Total number of subbands = 16
2 target(s) with ALMA data = ['AK_Sco', 'HIP_82747']
--------------------------------
Target = AS_310
--------------------------------
No observations found.
--------------------------------
Target = AS_470
--------------------------------
No observations found.
--------------------------------
Target = AS_477
--------------------------------
No observations found.
--------------------------------
Example 1.1.4: account for mosaics#

The alminer.target function will by default search whether any ALMA observations contain the target of interest’s position. To search whether any ALMA observations overlap with a larger region of interest, one can set the argument point=False and provide a search radius in arcminutes using the search_radius argument. The search radius is by default 1.0 arcminute.

[5]:

myquery = alminer.target(['Orion KL', "AB Aur"], point=False, search_radius=2.0)

================================
alminer.target results
================================
Target = Orion KL
--------------------------------
Number of projects = 37
Number of observations = 125
Number of unique subbands = 312
Total number of subbands = 576
57 target(s) with ALMA data = ['OrionKL', 'Orion H2O maser outburst', 'OrionField1-1', 'OrionField2', 'OrionField1-2', 'f1', 'f7', 'f5', 'f8', 'f4', 'f3', 'orion_kl', 'Orion_Source_I', 'BN', 'f13', 'f11', 'f12', 'f10', 'f9', 'f15', 'f14', 'orion-IRc2', 'Orion_KL', 'OMC1_SE', 'BN-KL', 'OMC1_NW', 'f23', 'f16', 'OMC-1S', 'OrionKL-SV', 'OMC-1', 'OrionBullets', 'ONC', 'Orion_BNKL_source_I', 'OMC-1_Region5', 'OMC-1_Region1', 'Orion', 'OMC-1_Region2', 'HC602_HC606_HC608', 'GEMS28', 'HC672', 'ONC_Mosaic', 'OMC-1_Region3', 'OMC-1_Region4', 'Orion_KL_Field_1_Orion_Hot_Core', 'Orion_KL_Field_2_SMA1', 'Orion_KL_Field_3_North-west_Clump', 'Orion KL', 'Orion1', '101', '32', '104', '107', '71', 'Orion-KL', 'ORS-8', 'ORS-4']
--------------------------------
Target = AB Aur
--------------------------------
Number of projects = 3
Number of observations = 3
Number of unique subbands = 17
Total number of subbands = 17
3 target(s) with ALMA data = ['ab_aurigae', 'AB_Aur', 'AB_Auriga']
--------------------------------

1.2 Query by position#

The alminer.conesearch and alminer.catalog functions can be used to directly query the ALMA archive by positions in the sky and a search radius around them. The right ascension and declinations must be given in units of degrees (ICRS). You can use the Astropy coordinates package to convert your desired coordinates to degrees.

Example 1.2.1: query an object by its coordinates (RA, Dec)#
[6]:

myquery = alminer.conesearch(ra=201.365063, dec=-43.019112, point=False, search_radius=10.0)

--------------------------------
Number of projects = 24
Number of observations = 77
Number of unique subbands = 192
Total number of subbands = 312
9 target(s) with ALMA data = ['J1325-430', 'Centaurus_A', 'J1325-4301', 'CenA', 'Centaurus_a', '3FGL_J1325.4-4301', 'Centaurus A', 'NGC_5128', 'Cen_A']
--------------------------------
Example 1.2.2: query a catalog of objects by their coordinates (RA, Dec)#

Let’s first import a catalog, for example the catalog of Spitzer YSOs in Orion from Megeath et al. (2009), and create a PANDAS DataFrame using rows 866 to 869 of this catalog. Then use the alminer.catalog function to query the ALMA archive for each target in the DataFrame.

[7]:

Spitzer = ascii.read("Spitzer_sample.dat", header_start=0, data_start=866, data_end=869)

mycat =  {"Name": Spitzer["Seq"],
          "RAJ2000" : Spitzer["RA2000"],
          "DEJ2000" : Spitzer["DEC2000"]}

mycat = pandas.DataFrame(mycat)

myquery = alminer.catalog(mycat)

================================
alminer.catalog results
================================
Target = 866
--------------------------------
Number of projects = 1
Number of observations = 1
Number of unique subbands = 4
Total number of subbands = 4
1 target(s) with ALMA data = ['M12_866']
--------------------------------
Target = 867
--------------------------------
Number of projects = 1
Number of observations = 1
Number of unique subbands = 4
Total number of subbands = 4
1 target(s) with ALMA data = ['M12_867']
--------------------------------
Target = 868
--------------------------------
Number of projects = 2
Number of observations = 2
Number of unique subbands = 8
Total number of subbands = 8
1 target(s) with ALMA data = ['HOPS-172']
--------------------------------

1.3 Query by ALMA keywords#

Query the ALMA archive for any (string-type) keywords defined in ALMA TAP system using the alminer.keysearch function.

The power of this function is in combining keywords. When multiple keywords are provided, they are queried using ‘AND’ logic, but when multiple values are provided for a given keyword, they are queried using ‘OR’ logic. If a given value contains spaces, its constituents are queried using ‘AND’ logic. Words encapsulated in quotation marks (either ‘ or “) are queried as phrases. For example,

  • alminer.keysearch({"proposal_abstract": ["high-mass star formation outflow disk"]}) will query the archive for projects with the words “high-mass” AND “star” AND “formation” AND “outflow” AND “disk” in their proposal abstracts.

  • alminer.keysearch({"proposal_abstract": ["high-mass", "star", "formation", "outflow", "disk"]}) will query the archive for projects with the words “high-mass” OR “star” OR “formation” OR “outflow” OR “disk” in their proposal abstracts.

  • alminer.keysearch({"proposal_abstract": ["'high-mass star formation' outflow disk"]}) will query the archive for projects with the phrase “high-mass star formation” AND the words “outflow” AND “disk” in their proposal abstracts.

  • alminer.keysearch({"proposal_abstract": ["star formation"], "scientific_category":['Galaxy evolution']}) will query the archive for projects with the words “star” AND “formation” in their proposal abstracts AND projects that are within the scientific_category of ‘Galaxy evolution’.

Note

Example 1.3.1: query a list of ALMA target names that may not be in SIMBAD/NED/VizieR#
[8]:

myquery = alminer.keysearch({'target_name': ['GRB021004','SPT0319-47', 'G345']})

================================
alminer.keysearch results
================================
--------------------------------
Number of projects = 17
Number of observations = 31
Number of unique subbands = 121
Total number of subbands = 191
12 target(s) with ALMA data = ['GRB021004', 'G345.5', 'SPT0319-47', 'G345.5043+00.3480', 'G345.49+1.47', 'G345.50+0.35', 'G345.6487+0.0089', 'G345.01', 'G345.11', 'G345.0029-0.2241', 'G345.5+1.5', 'G345.144-00.216']
--------------------------------
Example 1.3.2: query a list of ALMA projects by their proposal IDs#
[9]:

myquery = alminer.keysearch({'proposal_id': ['2015.1.00664.S', '2016.1.00204.S']})

================================
alminer.keysearch results
================================
--------------------------------
Number of projects = 2
Number of observations = 16
Number of unique subbands = 16
Total number of subbands = 64
16 target(s) with ALMA data = ['KMOS3DCOS4-24763', 'KMOS3DGS4-25151', 'KMOS3DCOS4-13701', 'KMOS3DCOS4-10347', 'KMOS3DCOS4-13174', 'KMOS3DCOS4-19680', 'KMOS3DCOS4-15813', 'KMOS3DCOS4-15820', 'KMOS3DU4-34138', 'KMOS3DU4-22227', 'KMOS3DU4-32147', 'KMOS3DU4-20547', 'KMOS3DGS4-11016', 'KMOS3DGS4-24110', 'KMOS3DGS4-27882', 'AK_Sco']
--------------------------------
Example 1.3.3: query by words in the proposal abstract#

Query the ALMA archive for proposals that have the phrase ‘high-mass star formation’ AND the words ‘outflow’ AND ‘disk’, OR the phrase ‘massive star formation’ AND the words ‘outflow’ AND ‘disk’ - and do not print the long list of target names in the summary.

[10]:

myquery = alminer.keysearch({'proposal_abstract': ['"high-mass star formation" outflow disk',
                                                   '"massive star formation" outflow disk']},
                            print_targets=False)

================================
alminer.keysearch results
================================
--------------------------------
Number of projects = 14
Number of observations = 59
Number of unique subbands = 206
Total number of subbands = 423
Total number of targets with ALMA data = 29
--------------------------------
Example 1.3.4: query by combination of keywords#

Query the ALMA archive for proposals that have the phrase ‘star formation’ in their abstracts and correspond to the scientific category of ‘Galaxy evolution’.

[11]:

myquery = alminer.keysearch({'proposal_abstract': ['"star formation"'],
                             'scientific_category':['Galaxy evolution']}, print_targets=False)

================================
alminer.keysearch results
================================
--------------------------------
Number of projects = 245
Number of observations = 2839
Number of unique subbands = 3908
Total number of subbands = 11687
Total number of targets with ALMA data = 1763
--------------------------------
Example 1.3.5: query for full polarization data#
[ ]:

myquery = alminer.keysearch({'science_keyword':['"disks around low-mass stars"'],
                             'pol_states':['XY', 'YX']}, print_targets=False)

================================
alminer.keysearch results
================================

2. Filter & explore results#

The querying functions presented in the previous section return a PANDAS DataFrame that can be used to further narrow down your search. This section presents some examples of how you can further filter and explore the results of your queries:

Load libraries & create a query

To explore these options, we will first query the archive using one of the methods presented in the previous section and use the results in the remainder of this tutorial.

[1]:

import alminer

observations = alminer.keysearch({'science_keyword':['Galaxy chemistry']},
                                 print_targets=False)

================================
alminer.keysearch results
================================
--------------------------------
Number of projects = 48
Number of observations = 341
Number of unique subbands = 1166
Total number of subbands = 1368
Total number of targets with ALMA data = 64
--------------------------------

2.1 Explore results#

You can simply display the DataFrame table returned by the query functions using the name you gave it (in this case observations), but often there are limits to how many rows and columns are presented. With the alminer.explore function, you can control whether or not you want to display all rows (allrows=True/False) and/or all columns (allcols=True/False). By default, only the 18 most useful columns are shown and the number of rows is truncated.

Example 2.1.1: View the queried observations as a table (shortened)#
[2]:

alminer.explore(observations)

[2]:
Obs project_code ALMA_source_name RAJ2000 DEJ2000 ang_res_arcsec min_freq_GHz max_freq_GHz central_freq_GHz bandwidth_GHz freq_res_kHz vel_res_kms LAS_arcsec FoV_arcsec cont_sens_bandwidth line_sens_10kms line_sens_native MOUS_id
0 1 2011.0.00268.S LESS J0332-2756 53.122042 -27.938694 1.264 252.85 254.73 253.79 1.875 976.56 1.149 11.242 23.564 79.299 1675.07 112.74 uid://A002/X303d22/X7b
1 2 2011.0.00268.S LESS J0332-2756 53.122042 -27.938694 1.264 239.50 241.37 240.43 1.875 976.56 1.213 11.242 23.564 79.299 1708.54 111.94 uid://A002/X303d22/X7b
2 3 2011.0.00405.S PKS1830-211 278.416330 -21.061080 0.458 281.64 283.51 282.57 1.874 976.56 1.033 4.088 20.113 60.429 1677.23 119.14 uid://A002/X36d874/X7a
3 4 2011.0.00405.S PKS1830-211 278.416330 -21.061080 0.458 293.63 295.51 294.57 1.874 976.56 0.991 4.088 20.113 60.429 1655.25 120.04 uid://A002/X36d874/X7a
4 5 2011.0.00405.S PKS1830-211 278.416330 -21.061080 0.458 283.51 285.39 284.45 1.874 976.56 1.026 4.088 20.113 60.429 1673.64 119.28 uid://A002/X36d874/X7a
5 6 2011.0.00405.S PKS1830-211 278.416330 -21.061080 1.571 103.20 105.08 104.14 1.878 976.56 2.786 16.995 58.774 29.824 1381.77 59.69 uid://A002/X36d874/X80
6 7 2011.0.00405.S PKS1830-211 278.416330 -21.061080 1.571 93.07 94.95 94.01 1.878 976.56 3.083 16.995 58.774 29.824 1440.54 59.16 uid://A002/X36d874/X80
7 8 2011.0.00405.S PKS1830-211 278.416330 -21.061080 1.571 91.20 93.08 92.14 1.878 976.56 3.145 16.995 58.774 29.824 1500.60 61.02 uid://A002/X36d874/X80
8 9 2011.0.00405.S PKS1830-211 278.416330 -21.061080 1.571 105.07 106.95 106.01 1.878 976.56 2.737 16.995 58.774 29.824 1370.09 59.71 uid://A002/X36d874/X80
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1359 1360 2019.1.00130.S NGC1068 40.669879 -0.013289 1.681 89.72 91.59 90.65 1.873 1938.48 6.345 22.359 59.730 9.858 494.60 19.97 uid://A001/X1465/X3890
1360 1361 2019.1.00130.S NGC4418 186.727617 -0.877642 1.431 108.29 110.16 109.23 1.873 1938.48 5.366 27.391 55.903 10.485 468.79 20.59 uid://A001/X1465/X389c
1361 1362 2019.1.00130.S NGC4418 186.727617 -0.877642 1.431 110.17 112.05 111.11 1.873 1938.48 5.274 27.391 55.903 10.485 465.01 20.60 uid://A001/X1465/X389c
1362 1363 2019.1.00130.S NGC4418 186.727617 -0.877642 1.431 96.28 98.15 97.21 1.873 1938.48 5.920 27.391 55.903 10.485 511.67 21.39 uid://A001/X1465/X389c
1363 1364 2019.1.00130.S NGC4418 186.727617 -0.877642 1.431 97.97 99.84 98.90 1.873 1938.48 5.820 27.391 55.903 10.485 507.47 21.40 uid://A001/X1465/X389c
1364 1365 2019.1.00130.S NGC4418 186.727617 -0.877642 2.690 101.24 103.11 102.18 1.873 1938.48 5.740 32.285 59.922 9.343 437.00 18.56 uid://A001/X1465/X3898
1365 1366 2019.1.00130.S NGC4418 186.727617 -0.877642 2.690 103.05 104.92 103.99 1.873 1938.48 5.639 32.285 59.922 9.343 436.95 18.72 uid://A001/X1465/X3898
1366 1367 2019.1.00130.S NGC4418 186.727617 -0.877642 2.690 89.43 91.30 90.36 1.873 1938.48 6.364 32.285 59.922 9.343 466.81 18.82 uid://A001/X1465/X3898
1367 1368 2019.1.00130.S NGC4418 186.727617 -0.877642 2.690 91.20 93.07 92.13 1.873 1938.48 6.243 32.285 59.922 9.343 459.55 18.71 uid://A001/X1465/X3898

1368 rows × 18 columns

Example 2.1.2: View the queried observations as a table and show all columns#
[3]:

alminer.explore(observations, allcols=True, allrows=False)
# you can also set allrows=True to see all rows in the table
# but this may be slow for very large queries

[3]:
Obs project_code ALMA_source_name RAJ2000 DEJ2000 ang_res_arcsec min_freq_GHz max_freq_GHz central_freq_GHz bandwidth_GHz freq_res_kHz vel_res_kms LAS_arcsec FoV_arcsec cont_sens_bandwidth line_sens_10kms line_sens_native MOUS_id access_url access_format proposal_id data_rights gal_longitude gal_latitude obs_publisher_did obs_collection facility_name instrument_name obs_id dataproduct_type calib_level target_name s_ra s_dec s_fov s_region s_resolution t_min t_max t_exptime ... em_min em_max em_res_power pol_states o_ucd band_list em_resolution authors pub_abstract publication_year proposal_abstract schedblock_name proposal_authors sensitivity_10kms cont_sensitivity_bandwidth pwv group_ous_uid member_ous_uid asdm_uid obs_title type scan_intent science_observation spatial_scale_max bandwidth antenna_arrays is_mosaic obs_release_date spatial_resolution frequency_support frequency velocity_resolution obs_creator_name pub_title first_author qa2_passed bib_reference science_keyword scientific_category lastModified
0 1 2011.0.00268.S LESS J0332-2756 53.122042 -27.938694 1.264 252.85 254.73 253.79 1.875 976.56 1.149 11.242 23.564 79.299 1675.07 112.74 uid://A002/X303d22/X7b https://almascience.org/datalink/sync?ID=uid:/... applicati 2011.0.00268.S Public 223.796062 -54.451922 ADS/JAO.ALMA#2011.0.00268.S ALMA JAO ALMA uid://A002/X303d22/X7b cube 2 LESS J0332-2756 53.122042 -27.938694 0.006546 Circle ICRS 53.122042 -27.938694 0.003273 1.264027 55936.926044 55939.192641 4536.00 ... 0.001177 0.001186 260809.621360 /XX/YY/ phot.flux.density;phys.polarization 6 2.928016e+14 Leon, S.; Cortes, P.C.; Guerard, M.; Villard, ... Aims: We aim at analyzing the (sub-)millimeter... 2012 Metallicity of galaxies and its redshift evolu... LESS J0332-2756 De Breuck, Carlos; Hatsukade, Bunyo; Maiolino,... 1.675071 0.079299 1.459508 uid://A002/X303d22/X7b uid://A002/X36a70f/X318 Metallicity of a Submillimeter Galaxy at z=5 S TARGET WVR T 11.241926 1.875000e+09 A002:PM02 A004:DV04 A009:DA43 A011:DV12 A013:D... F 2013-02-09T01:45:00.000 1.264027 [239.50..241.37GHz,976.56kHz,1.7mJy/beam@10km/... 253.790770 1149.468553 Nagao, Tohru ALMA reveals a chemically evolved submillimete... Leon, S. Nagao, T. Ono, Yoshiaki T 2012A&A...542L..34N 2014ApJ...795....5O 2016A&... Galaxy structure & evolution, Galaxy chemistry Galaxy evolution 2022-03-16T15:26:21.801
1 2 2011.0.00268.S LESS J0332-2756 53.122042 -27.938694 1.264 239.50 241.37 240.43 1.875 976.56 1.213 11.242 23.564 79.299 1708.54 111.94 uid://A002/X303d22/X7b https://almascience.org/datalink/sync?ID=uid:/... applicati 2011.0.00268.S Public 223.796062 -54.451922 ADS/JAO.ALMA#2011.0.00268.S ALMA JAO ALMA uid://A002/X303d22/X7b cube 2 LESS J0332-2756 53.122042 -27.938694 0.006546 Circle ICRS 53.122042 -27.938694 0.003273 1.264027 55936.926044 55939.192641 4536.00 ... 0.001242 0.001252 247134.408560 /XX/YY/ phot.flux.density;phys.polarization 6 2.928016e+14 Leon, S.; Cortes, P.C.; Guerard, M.; Villard, ... Aims: We aim at analyzing the (sub-)millimeter... 2012 Metallicity of galaxies and its redshift evolu... LESS J0332-2756 De Breuck, Carlos; Hatsukade, Bunyo; Maiolino,... 1.708538 0.079299 1.459508 uid://A002/X303d22/X7b uid://A002/X36a70f/X318 Metallicity of a Submillimeter Galaxy at z=5 S TARGET WVR T 11.241926 1.875000e+09 A002:PM02 A004:DV04 A009:DA43 A011:DV12 A013:D... F 2013-02-09T01:45:00.000 1.264027 [239.50..241.37GHz,976.56kHz,1.7mJy/beam@10km/... 240.434432 1149.468553 Nagao, Tohru ALMA reveals a chemically evolved submillimete... Leon, S. Nagao, T. Ono, Yoshiaki T 2012A&A...542L..34N 2014ApJ...795....5O 2016A&... Galaxy structure & evolution, Galaxy chemistry Galaxy evolution 2022-03-16T15:26:21.801
2 3 2011.0.00405.S PKS1830-211 278.416330 -21.061080 0.458 281.64 283.51 282.57 1.874 976.56 1.033 4.088 20.113 60.429 1677.23 119.14 uid://A002/X36d874/X7a https://almascience.org/datalink/sync?ID=uid:/... applicati 2011.0.00405.S Public 12.165817 -5.711677 ADS/JAO.ALMA#2011.0.00405.S ALMA JAO ALMA uid://A002/X36d874/X7a cube 2 PKS1830-211 278.416330 -21.061080 0.005587 Circle ICRS 278.416330 -21.061080 0.002793 0.458195 56028.254136 56028.361572 3144.96 ... 0.001057 0.001064 290354.950000 /XX/YY/ phot.flux.density;phys.polarization 7 2.927253e+14 Bagdonaite, J.; Daprà, M.; Jansen, P.; Bethlem... A limit on a possible cosmological variation o... 2013 The z=0.89 (sub)mm molecular absorber toward t... PKS1830_CH_and_H2O_b7_run_x2 Bethlem, Hendrick; Gerin, Maryvonne; Bottinell... 1.677226 0.060429 0.701340 uid://A002/X36d874/X7a uid://A002/X3cb0a0/X9e8 A survey of strong absorption lines at z=0.89 ... S TARGET T 4.087964 1.875000e+09 A003:DA41 A011:DV12 A025:DV14 A037:PM01 A045:D... F 2013-05-22T07:31:59.000 0.458195 [281.64..283.51GHz,976.56kHz,1.7mJy/beam@10km/... 282.573239 984.336598 Muller, Sebastien A strong magnetic field in the jet base of a s... Bagdonaite, J. Martí-Vidal, I. Martí-Vidal, Iv... T 2013A&A...558A.123M 2013PhRvL.111w1101B 2014A&... Galaxy structure & evolution, Galaxy chemistry Galaxy evolution 2022-03-16T15:26:21.801
3 4 2011.0.00405.S PKS1830-211 278.416330 -21.061080 0.458 293.63 295.51 294.57 1.874 976.56 0.991 4.088 20.113 60.429 1655.25 120.04 uid://A002/X36d874/X7a https://almascience.org/datalink/sync?ID=uid:/... applicati 2011.0.00405.S Public 12.165817 -5.711677 ADS/JAO.ALMA#2011.0.00405.S ALMA JAO ALMA uid://A002/X36d874/X7a cube 2 PKS1830-211 278.416330 -21.061080 0.005587 Circle ICRS 278.416330 -21.061080 0.002793 0.458195 56028.254136 56028.361572 3144.96 ... 0.001014 0.001021 302642.950000 /XX/YY/ phot.flux.density;phys.polarization 7 2.927253e+14 Bagdonaite, J.; Daprà, M.; Jansen, P.; Bethlem... A limit on a possible cosmological variation o... 2013 The z=0.89 (sub)mm molecular absorber toward t... PKS1830_CH_and_H2O_b7_run_x2 Bethlem, Hendrick; Gerin, Maryvonne; Bottinell... 1.655253 0.060429 0.701340 uid://A002/X36d874/X7a uid://A002/X3cb0a0/X9e8 A survey of strong absorption lines at z=0.89 ... S TARGET T 4.087964 1.875000e+09 A003:DA41 A011:DV12 A025:DV14 A037:PM01 A045:D... F 2013-05-22T07:31:59.000 0.458195 [281.64..283.51GHz,976.56kHz,1.7mJy/beam@10km/... 294.571572 984.336598 Muller, Sebastien A strong magnetic field in the jet base of a s... Bagdonaite, J. Martí-Vidal, I. Martí-Vidal, Iv... T 2013A&A...558A.123M 2013PhRvL.111w1101B 2014A&... Galaxy structure & evolution, Galaxy chemistry Galaxy evolution 2022-03-16T15:26:21.801
4 5 2011.0.00405.S PKS1830-211 278.416330 -21.061080 0.458 283.51 285.39 284.45 1.874 976.56 1.026 4.088 20.113 60.429 1673.64 119.28 uid://A002/X36d874/X7a https://almascience.org/datalink/sync?ID=uid:/... applicati 2011.0.00405.S Public 12.165817 -5.711677 ADS/JAO.ALMA#2011.0.00405.S ALMA JAO ALMA uid://A002/X36d874/X7a cube 2 PKS1830-211 278.416330 -21.061080 0.005587 Circle ICRS 278.416330 -21.061080 0.002793 0.458195 56028.254136 56028.361572 3144.96 ... 0.001050 0.001057 292274.950000 /XX/YY/ phot.flux.density;phys.polarization 7 2.927253e+14 Bagdonaite, J.; Daprà, M.; Jansen, P.; Bethlem... A limit on a possible cosmological variation o... 2013 The z=0.89 (sub)mm molecular absorber toward t... PKS1830_CH_and_H2O_b7_run_x2 Bethlem, Hendrick; Gerin, Maryvonne; Bottinell... 1.673643 0.060429 0.701340 uid://A002/X36d874/X7a uid://A002/X3cb0a0/X9e8 A survey of strong absorption lines at z=0.89 ... S TARGET T 4.087964 1.875000e+09 A003:DA41 A011:DV12 A025:DV14 A037:PM01 A045:D... F 2013-05-22T07:31:59.000 0.458195 [281.64..283.51GHz,976.56kHz,1.7mJy/beam@10km/... 284.447979 984.336598 Muller, Sebastien A strong magnetic field in the jet base of a s... Bagdonaite, J. Martí-Vidal, I. Martí-Vidal, Iv... T 2013A&A...558A.123M 2013PhRvL.111w1101B 2014A&... Galaxy structure & evolution, Galaxy chemistry Galaxy evolution 2022-03-16T15:26:21.801
5 6 2011.0.00405.S PKS1830-211 278.416330 -21.061080 1.571 103.20 105.08 104.14 1.878 976.56 2.786 16.995 58.774 29.824 1381.77 59.69 uid://A002/X36d874/X80 https://almascience.org/datalink/sync?ID=uid:/... applicati 2011.0.00405.S Public 12.165817 -5.711677 ADS/JAO.ALMA#2011.0.00405.S ALMA JAO ALMA uid://A002/X36d874/X80 cube 2 PKS1830-211 278.416330 -21.061080 0.016326 Circle ICRS 278.416330 -21.061080 0.008163 1.570624 56069.390944 56093.395812 3719.52 ... 0.002853 0.002905 107605.766000 /XX/YY/ phot.flux.density;phys.polarization 3 2.927369e+14 Bagdonaite, J.; Daprà, M.; Jansen, P.; Bethlem... A limit on a possible cosmological variation o... 2013 The z=0.89 (sub)mm molecular absorber toward t... PKS1830_HCOplus_b3_run_x3 Bethlem, Hendrick; Gerin, Maryvonne; Bottinell... 1.381767 0.029824 0.918540 uid://A002/X36d874/X80 uid://A002/X411025/X441 A survey of strong absorption lines at z=0.89 ... S TARGET T 16.995170 1.875000e+09 A003:DA41 A004:DV04 A008:DV19 A011:DV12 A021:D... F 2013-10-10T14:47:51.000 1.570624 [91.20..93.08GHz,976.56kHz,1.5mJy/beam@10km/s,... 104.138014 2737.212182 Muller, Sebastien A strong magnetic field in the jet base of a s... Bagdonaite, J. Martí-Vidal, I. Martí-Vidal, Iv... T 2013A&A...558A.123M 2013PhRvL.111w1101B 2014A&... Galaxy structure & evolution, Galaxy chemistry Galaxy evolution 2022-03-16T15:26:21.801
6 7 2011.0.00405.S PKS1830-211 278.416330 -21.061080 1.571 93.07 94.95 94.01 1.878 976.56 3.083 16.995 58.774 29.824 1440.54 59.16 uid://A002/X36d874/X80 https://almascience.org/datalink/sync?ID=uid:/... applicati 2011.0.00405.S Public 12.165817 -5.711677 ADS/JAO.ALMA#2011.0.00405.S ALMA JAO ALMA uid://A002/X36d874/X80 cube 2 PKS1830-211 278.416330 -21.061080 0.016326 Circle ICRS 278.416330 -21.061080 0.008163 1.570624 56069.390944 56093.395812 3719.52 ... 0.003157 0.003221 97236.742000 /XX/YY/ phot.flux.density;phys.polarization 3 2.927369e+14 Bagdonaite, J.; Daprà, M.; Jansen, P.; Bethlem... A limit on a possible cosmological variation o... 2013 The z=0.89 (sub)mm molecular absorber toward t... PKS1830_HCOplus_b3_run_x3 Bethlem, Hendrick; Gerin, Maryvonne; Bottinell... 1.440543 0.029824 0.918540 uid://A002/X36d874/X80 uid://A002/X411025/X441 A survey of strong absorption lines at z=0.89 ... S TARGET T 16.995170 1.875000e+09 A003:DA41 A004:DV04 A008:DV19 A011:DV12 A021:D... F 2013-10-10T14:47:51.000 1.570624 [91.20..93.08GHz,976.56kHz,1.5mJy/beam@10km/s,... 94.012841 2737.212182 Muller, Sebastien A strong magnetic field in the jet base of a s... Bagdonaite, J. Martí-Vidal, I. Martí-Vidal, Iv... T 2013A&A...558A.123M 2013PhRvL.111w1101B 2014A&... Galaxy structure & evolution, Galaxy chemistry Galaxy evolution 2022-03-16T15:26:21.801
7 8 2011.0.00405.S PKS1830-211 278.416330 -21.061080 1.571 91.20 93.08 92.14 1.878 976.56 3.145 16.995 58.774 29.824 1500.60 61.02 uid://A002/X36d874/X80 https://almascience.org/datalink/sync?ID=uid:/... applicati 2011.0.00405.S Public 12.165817 -5.711677 ADS/JAO.ALMA#2011.0.00405.S ALMA JAO ALMA uid://A002/X36d874/X80 cube 2 PKS1830-211 278.416330 -21.061080 0.016326 Circle ICRS 278.416330 -21.061080 0.008163 1.570624 56069.390944 56093.395812 3719.52 ... 0.003221 0.003287 95317.766000 /XX/YY/ phot.flux.density;phys.polarization 3 2.927369e+14 Bagdonaite, J.; Daprà, M.; Jansen, P.; Bethlem... A limit on a possible cosmological variation o... 2013 The z=0.89 (sub)mm molecular absorber toward t... PKS1830_HCOplus_b3_run_x3 Bethlem, Hendrick; Gerin, Maryvonne; Bottinell... 1.500603 0.029824 0.918540 uid://A002/X36d874/X80 uid://A002/X411025/X441 A survey of strong absorption lines at z=0.89 ... S TARGET T 16.995170 1.875000e+09 A003:DA41 A004:DV04 A008:DV19 A011:DV12 A021:D... F 2013-10-10T14:47:51.000 1.570624 [91.20..93.08GHz,976.56kHz,1.5mJy/beam@10km/s,... 92.138994 2737.212182 Muller, Sebastien A strong magnetic field in the jet base of a s... Bagdonaite, J. Martí-Vidal, I. Martí-Vidal, Iv... T 2013A&A...558A.123M 2013PhRvL.111w1101B 2014A&... Galaxy structure & evolution, Galaxy chemistry Galaxy evolution 2022-03-16T15:26:21.801
8 9 2011.0.00405.S PKS1830-211 278.416330 -21.061080 1.571 105.07 106.95 106.01 1.878 976.56 2.737 16.995 58.774 29.824 1370.09 59.71 uid://A002/X36d874/X80 https://almascience.org/datalink/sync?ID=uid:/... applicati 2011.0.00405.S Public 12.165817 -5.711677 ADS/JAO.ALMA#2011.0.00405.S ALMA JAO ALMA uid://A002/X36d874/X80 cube 2 PKS1830-211 278.416330 -21.061080 0.016326 Circle ICRS 278.416330 -21.061080 0.008163 1.570624 56069.390944 56093.395812 3719.52 ... 0.002803 0.002853 109524.742000 /XX/YY/ phot.flux.density;phys.polarization 3 2.927369e+14 Bagdonaite, J.; Daprà, M.; Jansen, P.; Bethlem... A limit on a possible cosmological variation o... 2013 The z=0.89 (sub)mm molecular absorber toward t... PKS1830_HCOplus_b3_run_x3 Bethlem, Hendrick; Gerin, Maryvonne; Bottinell... 1.370093 0.029824 0.918540 uid://A002/X36d874/X80 uid://A002/X411025/X441 A survey of strong absorption lines at z=0.89 ... S TARGET T 16.995170 1.875000e+09 A003:DA41 A004:DV04 A008:DV19 A011:DV12 A021:D... F 2013-10-10T14:47:51.000 1.570624 [91.20..93.08GHz,976.56kHz,1.5mJy/beam@10km/s,... 106.011861 2737.212182 Muller, Sebastien A strong magnetic field in the jet base of a s... Bagdonaite, J. Martí-Vidal, I. Martí-Vidal, Iv... T 2013A&A...558A.123M 2013PhRvL.111w1101B 2014A&... Galaxy structure & evolution, Galaxy chemistry Galaxy evolution 2022-03-16T15:26:21.801
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1359 1360 2019.1.00130.S NGC1068 40.669879 -0.013289 1.681 89.72 91.59 90.65 1.873 1938.48 6.345 22.359 59.730 9.858 494.60 19.97 uid://A001/X1465/X3890 https://almascience.org/datalink/sync?ID=uid:/... applicati 2019.1.00130.S Public 172.103977 -51.933584 ADS/JAO.ALMA#2019.1.00130.S ALMA JAO ALMA uid://A001/X1465/X3890 cube 2 NGC1068 40.669879 -0.013289 0.016592 Polygon ICRS 40.669879 -0.021585 40.668325 -0.... 1.680615 58790.235849 58802.117722 9918.72 ... 0.003273 0.003342 47246.711574 /XX/YY/ phot.flux.density;phys.polarization 3 5.811652e+14 <NA> The 12C/13C isotopic ratio is a good tracer of... NGC1068_a_03_TM1 Krips, Melanie; Garcia-Burillo, Santiago; Mueh... 0.494603 0.009858 2.740881 uid://A001/X1465/X388f uid://A001/X1465/X3890 uid://A002/Xe2eefc/X31df A decisive answer on the carbon isotopic ratio... S TARGET T 22.359373 1.875000e+09 A001:DA48 A002:DA44 A003:DV23 A004:DA56 A005:D... F 2021-03-10T20:24:07.000 1.680615 [89.72..91.59GHz,1938.48kHz,494.6uJy/beam@10km... 90.653871 5621.370692 Martin, Sergio T Starbursts, star formation, Galaxy chemistry Active galaxies 2022-03-16T15:26:21.801
1360 1361 2019.1.00130.S NGC4418 186.727617 -0.877642 1.431 108.29 110.16 109.23 1.873 1938.48 5.366 27.391 55.903 10.485 468.79 20.59 uid://A001/X1465/X389c https://almascience.org/datalink/sync?ID=uid:/... applicati 2019.1.00130.S Public 290.048542 61.379017 ADS/JAO.ALMA#2019.1.00130.S ALMA JAO ALMA uid://A001/X1465/X389c cube 2 NGC4418 186.727617 -0.877642 0.015529 Polygon ICRS 186.727617 -0.869878 186.729072 -... 1.431197 58787.586472 58825.599501 4898.88 ... 0.002721 0.002768 55868.832936 /XX/YY/ phot.flux.density;phys.polarization 3 5.810830e+14 <NA> The 12C/13C isotopic ratio is a good tracer of... NGC4418_b_03_TM1 Krips, Melanie; Garcia-Burillo, Santiago; Mueh... 0.468785 0.010485 2.337607 uid://A001/X1465/X389b uid://A001/X1465/X389c uid://A002/Xe2ada9/X15596 A decisive answer on the carbon isotopic ratio... S TARGET T 27.390750 1.875000e+09 A001:DA48 A002:DA44 A003:DV23 A004:DA56 A005:D... F 2021-03-17T19:10:59.000 1.431197 [96.28..98.15GHz,1938.48kHz,511.7uJy/beam@10km... 109.226108 5274.300699 Martin, Sergio T Starbursts, star formation, Galaxy chemistry Active galaxies 2022-03-16T15:26:21.801
1361 1362 2019.1.00130.S NGC4418 186.727617 -0.877642 1.431 110.17 112.05 111.11 1.873 1938.48 5.274 27.391 55.903 10.485 465.01 20.60 uid://A001/X1465/X389c https://almascience.org/datalink/sync?ID=uid:/... applicati 2019.1.00130.S Public 290.048542 61.379017 ADS/JAO.ALMA#2019.1.00130.S ALMA JAO ALMA uid://A001/X1465/X389c cube 2 NGC4418 186.727617 -0.877642 0.015529 Polygon ICRS 186.727617 -0.869878 186.729072 -... 1.431197 58787.586472 58825.599501 4898.88 ... 0.002676 0.002721 56840.228703 /XX/YY/ phot.flux.density;phys.polarization 3 5.810830e+14 <NA> The 12C/13C isotopic ratio is a good tracer of... NGC4418_b_03_TM1 Krips, Melanie; Garcia-Burillo, Santiago; Mueh... 0.465008 0.010485 2.337607 uid://A001/X1465/X389b uid://A001/X1465/X389c uid://A002/Xe2ada9/X15596 A decisive answer on the carbon isotopic ratio... S TARGET T 27.390750 1.875000e+09 A001:DA48 A002:DA44 A003:DV23 A004:DA56 A005:D... F 2021-03-17T19:10:59.000 1.431197 [96.28..98.15GHz,1938.48kHz,511.7uJy/beam@10km... 111.109110 5274.300699 Martin, Sergio T Starbursts, star formation, Galaxy chemistry Active galaxies 2022-03-16T15:26:21.801
1362 1363 2019.1.00130.S NGC4418 186.727617 -0.877642 1.431 96.28 98.15 97.21 1.873 1938.48 5.920 27.391 55.903 10.485 511.67 21.39 uid://A001/X1465/X389c https://almascience.org/datalink/sync?ID=uid:/... applicati 2019.1.00130.S Public 290.048542 61.379017 ADS/JAO.ALMA#2019.1.00130.S ALMA JAO ALMA uid://A001/X1465/X389c cube 2 NGC4418 186.727617 -0.877642 0.015529 Polygon ICRS 186.727617 -0.869878 186.729072 -... 1.431197 58787.586472 58825.599501 4898.88 ... 0.003054 0.003114 50638.217620 /XX/YY/ phot.flux.density;phys.polarization 3 5.810830e+14 <NA> The 12C/13C isotopic ratio is a good tracer of... NGC4418_b_03_TM1 Krips, Melanie; Garcia-Burillo, Santiago; Mueh... 0.511673 0.010485 2.337607 uid://A001/X1465/X389b uid://A001/X1465/X389c uid://A002/Xe2ada9/X15596 A decisive answer on the carbon isotopic ratio... S TARGET T 27.390750 1.875000e+09 A001:DA48 A002:DA44 A003:DV23 A004:DA56 A005:D... F 2021-03-17T19:10:59.000 1.431197 [96.28..98.15GHz,1938.48kHz,511.7uJy/beam@10km... 97.214666 5274.300699 Martin, Sergio T Starbursts, star formation, Galaxy chemistry Active galaxies 2022-03-16T15:26:21.801
1363 1364 2019.1.00130.S NGC4418 186.727617 -0.877642 1.431 97.97 99.84 98.90 1.873 1938.48 5.820 27.391 55.903 10.485 507.47 21.40 uid://A001/X1465/X389c https://almascience.org/datalink/sync?ID=uid:/... applicati 2019.1.00130.S Public 290.048542 61.379017 ADS/JAO.ALMA#2019.1.00130.S ALMA JAO ALMA uid://A001/X1465/X389c cube 2 NGC4418 186.727617 -0.877642 0.015529 Polygon ICRS 186.727617 -0.869878 186.729072 -... 1.431197 58787.586472 58825.599501 4898.88 ... 0.003003 0.003060 51508.746588 /XX/YY/ phot.flux.density;phys.polarization 3 5.810830e+14 <NA> The 12C/13C isotopic ratio is a good tracer of... NGC4418_b_03_TM1 Krips, Melanie; Garcia-Burillo, Santiago; Mueh... 0.507473 0.010485 2.337607 uid://A001/X1465/X389b uid://A001/X1465/X389c uid://A002/Xe2ada9/X15596 A decisive answer on the carbon isotopic ratio... S TARGET T 27.390750 1.875000e+09 A001:DA48 A002:DA44 A003:DV23 A004:DA56 A005:D... F 2021-03-17T19:10:59.000 1.431197 [96.28..98.15GHz,1938.48kHz,511.7uJy/beam@10km... 98.902033 5274.300699 Martin, Sergio T Starbursts, star formation, Galaxy chemistry Active galaxies 2022-03-16T15:26:21.801
1364 1365 2019.1.00130.S NGC4418 186.727617 -0.877642 2.690 101.24 103.11 102.18 1.873 1938.48 5.740 32.285 59.922 9.343 437.00 18.56 uid://A001/X1465/X3898 https://almascience.org/datalink/sync?ID=uid:/... applicati 2019.1.00130.S Public 290.048542 61.379017 ADS/JAO.ALMA#2019.1.00130.S ALMA JAO ALMA uid://A001/X1465/X3898 cube 2 NGC4418 186.727617 -0.877642 0.016645 Polygon ICRS 186.727617 -0.869320 186.729177 -... 2.689952 58810.449413 58842.572568 16632.00 ... 0.002907 0.002961 52231.920422 /XX/YY/ phot.flux.density;phys.polarization 3 5.810791e+14 <NA> The 12C/13C isotopic ratio is a good tracer of... NGC4418_a_03_TM1 Krips, Melanie; Garcia-Burillo, Santiago; Mueh... 0.436998 0.009343 2.979321 uid://A001/X1465/X3897 uid://A001/X1465/X3898 uid://A002/Xe3f5bb/X3259 A decisive answer on the carbon isotopic ratio... S TARGET T 32.284968 1.875000e+09 A001:DA48 A002:DA44 A003:DV23 A004:DA56 A005:D... F 2021-04-07T00:23:33.000 2.689952 [89.43..91.30GHz,1938.48kHz,466.8uJy/beam@10km... 102.176058 5638.752118 Martin, Sergio T Starbursts, star formation, Galaxy chemistry Active galaxies 2022-03-16T15:26:21.801
1365 1366 2019.1.00130.S NGC4418 186.727617 -0.877642 2.690 103.05 104.92 103.99 1.873 1938.48 5.639 32.285 59.922 9.343 436.95 18.72 uid://A001/X1465/X3898 https://almascience.org/datalink/sync?ID=uid:/... applicati 2019.1.00130.S Public 290.048542 61.379017 ADS/JAO.ALMA#2019.1.00130.S ALMA JAO ALMA uid://A001/X1465/X3898 cube 2 NGC4418 186.727617 -0.877642 0.016645 Polygon ICRS 186.727617 -0.869320 186.729177 -... 2.689952 58810.449413 58842.572568 16632.00 ... 0.002857 0.002909 53166.454513 /XX/YY/ phot.flux.density;phys.polarization 3 5.810791e+14 <NA> The 12C/13C isotopic ratio is a good tracer of... NGC4418_a_03_TM1 Krips, Melanie; Garcia-Burillo, Santiago; Mueh... 0.436953 0.009343 2.979321 uid://A001/X1465/X3897 uid://A001/X1465/X3898 uid://A002/Xe3f5bb/X3259 A decisive answer on the carbon isotopic ratio... S TARGET T 32.284968 1.875000e+09 A001:DA48 A002:DA44 A003:DV23 A004:DA56 A005:D... F 2021-04-07T00:23:33.000 2.689952 [89.43..91.30GHz,1938.48kHz,466.8uJy/beam@10km... 103.987501 5638.752118 Martin, Sergio T Starbursts, star formation, Galaxy chemistry Active galaxies 2022-03-16T15:26:21.801
1366 1367 2019.1.00130.S NGC4418 186.727617 -0.877642 2.690 89.43 91.30 90.36 1.873 1938.48 6.364 32.285 59.922 9.343 466.81 18.82 uid://A001/X1465/X3898 https://almascience.org/datalink/sync?ID=uid:/... applicati 2019.1.00130.S Public 290.048542 61.379017 ADS/JAO.ALMA#2019.1.00130.S ALMA JAO ALMA uid://A001/X1465/X3898 cube 2 NGC4418 186.727617 -0.877642 0.016645 Polygon ICRS 186.727617 -0.869320 186.729177 -... 2.689952 58810.449413 58842.572568 16632.00 ... 0.003284 0.003352 47103.985999 /XX/YY/ phot.flux.density;phys.polarization 3 5.810791e+14 <NA> The 12C/13C isotopic ratio is a good tracer of... NGC4418_a_03_TM1 Krips, Melanie; Garcia-Burillo, Santiago; Mueh... 0.466810 0.009343 2.979321 uid://A001/X1465/X3897 uid://A001/X1465/X3898 uid://A002/Xe3f5bb/X3259 A decisive answer on the carbon isotopic ratio... S TARGET T 32.284968 1.875000e+09 A001:DA48 A002:DA44 A003:DV23 A004:DA56 A005:D... F 2021-04-07T00:23:33.000 2.689952 [89.43..91.30GHz,1938.48kHz,466.8uJy/beam@10km... 90.363805 5638.752118 Martin, Sergio T Starbursts, star formation, Galaxy chemistry Active galaxies 2022-03-16T15:26:21.801
1367 1368 2019.1.00130.S NGC4418 186.727617 -0.877642 2.690 91.20 93.07 92.13 1.873 1938.48 6.243 32.285 59.922 9.343 459.55 18.71 uid://A001/X1465/X3898 https://almascience.org/datalink/sync?ID=uid:/... applicati 2019.1.00130.S Public 290.048542 61.379017 ADS/JAO.ALMA#2019.1.00130.S ALMA JAO ALMA uid://A001/X1465/X3898 cube 2 NGC4418 186.727617 -0.877642 0.016645 Polygon ICRS 186.727617 -0.869320 186.729177 -... 2.689952 58810.449413 58842.572568 16632.00 ... 0.003221 0.003287 48017.332095 /XX/YY/ phot.flux.density;phys.polarization 3 5.810791e+14 <NA> The 12C/13C isotopic ratio is a good tracer of... NGC4418_a_03_TM1 Krips, Melanie; Garcia-Burillo, Santiago; Mueh... 0.459546 0.009343 2.979321 uid://A001/X1465/X3897 uid://A001/X1465/X3898 uid://A002/Xe3f5bb/X3259 A decisive answer on the carbon isotopic ratio... S TARGET T 32.284968 1.875000e+09 A001:DA48 A002:DA44 A003:DV23 A004:DA56 A005:D... F 2021-04-07T00:23:33.000 2.689952 [89.43..91.30GHz,1938.48kHz,466.8uJy/beam@10km... 92.134134 5638.752118 Martin, Sergio T Starbursts, star formation, Galaxy chemistry Active galaxies 2022-03-16T15:26:21.801

1368 rows × 81 columns

2.2 Summarize results#

The alminer.summary function will print a summary of the observations. This is done by default when the query is run, but it’s a useful function if the results are filtered further, as shown in the next section.

Example 2.2.1: print the summary of a given query result, including a list of unique ALMA target names#
[4]:

alminer.summary(observations)

--------------------------------
Number of projects = 48
Number of observations = 341
Number of unique subbands = 1166
Total number of subbands = 1368
64 target(s) with ALMA data = ['LESS J0332-2756', 'PKS1830-211', 'ngc4418', 'NGC7469', 'NGC_1097', 'Arp220', 'NGC_5253', 'M83', 'NGC253', 'NGC1266', 'vv114', 'Mystery_Object', 'Sgr_A_star', 'SDSS_J080430.99+360718.1', 'NGC_3256', 'ngc_3256', 'NGC_55', 'ngc3256', 'NGC4418', 'NGC_3627_BE', 'circinus', 'IRAS_F16399-0937', 'ngc6240', 'ngc613', 'n613', 'ngc253', 'N159-W_south', 'N159-E', 'N159-W', 'm83', 'IRAS_13120-5453', 'ngc4945', 'HE0433-1028', 'HE0108-4743', 'HE1353-1917', 'HE1108-2813', 'HE1029-1831', 'Y050355.87-672045.1', 'Y045622.61-663656.9', 'Y054248.90-694446.3', 'Y054826.21-700850.2', 'Y050953.89-685336.7', 'Y052333.40-693712.1', 'Y052343.48-680033.9', 'Y045406.43-664601.4', 'Y054629.32-693514.2', 'ST10', 'Y045358.57-691106.7', 'Y051912.27-690907.2', 'ST6', 'Y044854.41-690948.3', 'Y052423.39-693904.7', 'Y052210.08-673459.6', 'Y053952.11-710930.7', 'Y051344.99-693510.6', 'Y051916.87-693757.5', 'Y045100.16-691934.4', 'ngc4526', 'Cloverleaf', 'ngc7465', 'NGC_253', 'IRAS_22491-1808', 'NGC4945', 'NGC1068']
--------------------------------
Example 2.2.2: print the summary of a given query result WITHOUT the list of target names#

For big tables, it is useful to avoid printing the list of target names in the summary. This can be done by setting print_targets=False.

[5]:

alminer.summary(observations, print_targets=False)

--------------------------------
Number of projects = 48
Number of observations = 341
Number of unique subbands = 1166
Total number of subbands = 1368
Total number of targets with ALMA data = 64
--------------------------------

2.3 Filter results#

The search results can be further narrowed down by using PANDAS DataFrame functions. See also this introduction to data structures. For example, you can use your_query.columns to get a list of all columns in the DataFrame.

To get the description and units of any column use the function alminer.get_info(‘column_name’) where column_name is the name of the column.

Example 2.3.1: simple selection - observations with angular resolutions < 0.5”#

Let’s first check what the description and units of ang_res_arcsec column are:

[6]:

alminer.get_info('ang_res_arcsec')

--------------------------------
Column: ang_res_arcsec
--------------------------------
Description: typical spatial resolution
Units: arcsec
--------------------------------

Now we can do the some further filtering, say to only keep observations with angular resolutions < 0.5”:

[7]:

selected = observations[observations['ang_res_arcsec'] < 0.5]

and print the summary:

[8]:

alminer.summary(selected, print_targets=False)

--------------------------------
Number of projects = 23
Number of observations = 107
Number of unique subbands = 313
Total number of subbands = 427
Total number of targets with ALMA data = 41
--------------------------------
Example 2.3.2: multiple selections - observations with angular resolution < 0.5” & velocity resolution < 1 km/s#
[9]:

selected = observations[(observations['ang_res_arcsec'] < 0.5) &
                        (observations['vel_res_kms'] < 1.0)]
alminer.summary(selected, print_targets=False)

--------------------------------
Number of projects = 10
Number of observations = 50
Number of unique subbands = 78
Total number of subbands = 120
Total number of targets with ALMA data = 27
--------------------------------
Example 2.3.3: observations containing a given frequency#
[10]:

freq = 220.5
selected = observations[(observations["min_freq_GHz"] < freq) &
                        (observations["max_freq_GHz"] > freq)]
alminer.summary(selected, print_targets=False)

--------------------------------
Number of projects = 6
Number of observations = 7
Number of unique subbands = 7
Total number of subbands = 7
Total number of targets with ALMA data = 5
--------------------------------

2.4 Line coverage#

An alternative to the last example in the previous section is the alminer.line_coverage function which determines how many targets were observed at a given frequency with the option to include a redshift for the line of interest to be taken into account. Some notes: * Line frequencies should be given in GHz * Redshift is by default assumed to be 0 * The line_name keyword is the user’s defined name for the frquency provided

Example 2.4.1: search whether a given frequency is covered in the observations#
[11]:

myline_obs = alminer.line_coverage(observations,
                                   line_freq=220.5,
                                   z=0,
                                   line_name="My favourite line",
                                   print_targets=True)

--------------------------------
Summary of 'My favourite line' observations at 220.5 GHz
--------------------------------
Number of projects = 6
Number of observations = 7
Number of unique subbands = 7
Total number of subbands = 7
5 target(s) with ALMA data = ['Arp220', 'ngc_3256', 'IRAS_13120-5453', 'ngc253', 'NGC_253']
--------------------------------
Example 2.4.2: search whether a given frequency is observed for a target at a given redshift#
[12]:

myline_obs = alminer.line_coverage(observations,
                                   line_freq=400.0,
                                   z=0.5,
                                   line_name="My favourite line",
                                   print_targets=True)

--------------------------------
Summary of 'My favourite line' observations at 400.0 GHz (266.667 GHz at z=0.5)
--------------------------------
Number of projects = 8
Number of observations = 10
Number of unique subbands = 11
Total number of subbands = 11
6 target(s) with ALMA data = ['ngc4418', 'Arp220', 'ngc_3256', 'circinus', 'ngc4945', 'ngc253']
--------------------------------

2.5 Coverage of CO, 13CO, and C18O lines#

The alminer.CO_lines function determines how many CO, 13CO, and C18O lines were observed in the provided DataFrame and returns a DataFrame containing all observations of these transitions.

Example 2.5.1: search whether any CO, 13CO, and C18O lines were observed in the query results#
[13]:

CO_obs = alminer.CO_lines(observations, print_targets=False)

--------------------------------
Summary of 'CO (1-0)' observations at 115.271 GHz
--------------------------------
Number of projects = 4
Number of observations = 5
Number of unique subbands = 4
Total number of subbands = 5
Total number of targets with ALMA data = 3
--------------------------------
--------------------------------
Summary of 'CO (2-1)' observations at 230.538 GHz
--------------------------------
Number of projects = 10
Number of observations = 13
Number of unique subbands = 13
Total number of subbands = 13
Total number of targets with ALMA data = 8
--------------------------------
--------------------------------
Summary of 'CO (3-2)' observations at 345.796 GHz
--------------------------------
Number of projects = 3
Number of observations = 5
Number of unique subbands = 5
Total number of subbands = 5
Total number of targets with ALMA data = 4
--------------------------------
--------------------------------
Summary of 'CO (4-3)' observations at 461.041 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'CO (5-4)' observations at 576.268 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'CO (6-5)' observations at 691.473 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'CO (7-6)' observations at 806.652 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'CO (8-7)' observations at 921.8 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of '13CO (1-0)' observations at 110.201 GHz
--------------------------------
Number of projects = 18
Number of observations = 23
Number of unique subbands = 23
Total number of subbands = 25
Total number of targets with ALMA data = 17
--------------------------------
--------------------------------
Summary of '13CO (2-1)' observations at 220.399 GHz
--------------------------------
Number of projects = 6
Number of observations = 7
Number of unique subbands = 7
Total number of subbands = 7
Total number of targets with ALMA data = 5
--------------------------------
--------------------------------
Summary of '13CO (3-2)' observations at 330.588 GHz
--------------------------------
Number of projects = 2
Number of observations = 5
Number of unique subbands = 5
Total number of subbands = 5
Total number of targets with ALMA data = 4
--------------------------------
--------------------------------
Summary of '13CO (4-3)' observations at 440.765 GHz
--------------------------------
Number of projects = 1
Number of observations = 1
Number of unique subbands = 1
Total number of subbands = 1
Total number of targets with ALMA data = 1
--------------------------------
--------------------------------
Summary of '13CO (5-4)' observations at 550.926 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of '13CO (6-5)' observations at 661.067 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of '13CO (7-6)' observations at 771.184 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of '13CO (8-7)' observations at 881.273 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'C18O (1-0)' observations at 109.782 GHz
--------------------------------
Number of projects = 19
Number of observations = 24
Number of unique subbands = 25
Total number of subbands = 27
Total number of targets with ALMA data = 18
--------------------------------
--------------------------------
Summary of 'C18O (2-1)' observations at 219.56 GHz
--------------------------------
Number of projects = 7
Number of observations = 9
Number of unique subbands = 12
Total number of subbands = 12
Total number of targets with ALMA data = 6
--------------------------------
--------------------------------
Summary of 'C18O (3-2)' observations at 329.331 GHz
--------------------------------
Number of projects = 2
Number of observations = 3
Number of unique subbands = 3
Total number of subbands = 3
Total number of targets with ALMA data = 2
--------------------------------
--------------------------------
Summary of 'C18O (4-3)' observations at 439.089 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'C18O (5-4)' observations at 548.831 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'C18O (6-5)' observations at 658.553 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'C18O (7-6)' observations at 768.252 GHz
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'C18O (8-7)' observations at 877.922 GHz
--------------------------------
No observations found.
--------------------------------
Example 2.5.2: search whether any redshifted CO, 13CO, and C18O lines were observed in the query results#
[14]:

CO_obs = alminer.CO_lines(observations, z=1, print_targets=False)

--------------------------------
Summary of 'CO (1-0)' observations at 115.2712018 GHz (57.636 GHz at z=1)
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'CO (2-1)' observations at 230.538 GHz (115.269 GHz at z=1)
--------------------------------
Number of projects = 4
Number of observations = 5
Number of unique subbands = 4
Total number of subbands = 5
Total number of targets with ALMA data = 3
--------------------------------
--------------------------------
Summary of 'CO (3-2)' observations at 345.7959899 GHz (172.898 GHz at z=1)
--------------------------------
Number of projects = 2
Number of observations = 3
Number of unique subbands = 3
Total number of subbands = 3
Total number of targets with ALMA data = 2
--------------------------------
--------------------------------
Summary of 'CO (4-3)' observations at 461.0407682 GHz (230.52 GHz at z=1)
--------------------------------
Number of projects = 10
Number of observations = 13
Number of unique subbands = 13
Total number of subbands = 13
Total number of targets with ALMA data = 8
--------------------------------
--------------------------------
Summary of 'CO (5-4)' observations at 576.2679305 GHz (288.134 GHz at z=1)
--------------------------------
Number of projects = 3
Number of observations = 6
Number of unique subbands = 6
Total number of subbands = 6
Total number of targets with ALMA data = 3
--------------------------------
--------------------------------
Summary of 'CO (6-5)' observations at 691.4730763 GHz (345.737 GHz at z=1)
--------------------------------
Number of projects = 3
Number of observations = 5
Number of unique subbands = 5
Total number of subbands = 5
Total number of targets with ALMA data = 4
--------------------------------
--------------------------------
Summary of 'CO (7-6)' observations at 806.651806 GHz (403.326 GHz at z=1)
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'CO (8-7)' observations at 921.7997 GHz (460.9 GHz at z=1)
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of '13CO (1-0)' observations at 110.2013218 GHz (55.101 GHz at z=1)
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of '13CO (2-1)' observations at 220.3986195 GHz (110.199 GHz at z=1)
--------------------------------
Number of projects = 18
Number of observations = 23
Number of unique subbands = 23
Total number of subbands = 25
Total number of targets with ALMA data = 17
--------------------------------
--------------------------------
Summary of '13CO (3-2)' observations at 330.5878671 GHz (165.294 GHz at z=1)
--------------------------------
Number of projects = 1
Number of observations = 2
Number of unique subbands = 2
Total number of subbands = 2
Total number of targets with ALMA data = 1
--------------------------------
--------------------------------
Summary of '13CO (4-3)' observations at 440.7651735 GHz (220.383 GHz at z=1)
--------------------------------
Number of projects = 6
Number of observations = 7
Number of unique subbands = 7
Total number of subbands = 7
Total number of targets with ALMA data = 5
--------------------------------
--------------------------------
Summary of '13CO (5-4)' observations at 550.9262851 GHz (275.463 GHz at z=1)
--------------------------------
Number of projects = 3
Number of observations = 4
Number of unique subbands = 4
Total number of subbands = 4
Total number of targets with ALMA data = 3
--------------------------------
--------------------------------
Summary of '13CO (6-5)' observations at 661.0672766 GHz (330.534 GHz at z=1)
--------------------------------
Number of projects = 2
Number of observations = 5
Number of unique subbands = 5
Total number of subbands = 5
Total number of targets with ALMA data = 4
--------------------------------
--------------------------------
Summary of '13CO (7-6)' observations at 771.184125 GHz (385.592 GHz at z=1)
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of '13CO (8-7)' observations at 881.272808 GHz (440.636 GHz at z=1)
--------------------------------
Number of projects = 1
Number of observations = 1
Number of unique subbands = 1
Total number of subbands = 1
Total number of targets with ALMA data = 1
--------------------------------
--------------------------------
Summary of 'C18O (1-0)' observations at 109.7821734 GHz (54.891 GHz at z=1)
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'C18O (2-1)' observations at 219.5603541 GHz (109.78 GHz at z=1)
--------------------------------
Number of projects = 19
Number of observations = 24
Number of unique subbands = 25
Total number of subbands = 27
Total number of targets with ALMA data = 18
--------------------------------
--------------------------------
Summary of 'C18O (3-2)' observations at 329.3305525 GHz (164.665 GHz at z=1)
--------------------------------
Number of projects = 1
Number of observations = 2
Number of unique subbands = 2
Total number of subbands = 2
Total number of targets with ALMA data = 1
--------------------------------
--------------------------------
Summary of 'C18O (4-3)' observations at 439.0887658 GHz (219.544 GHz at z=1)
--------------------------------
Number of projects = 7
Number of observations = 9
Number of unique subbands = 12
Total number of subbands = 12
Total number of targets with ALMA data = 6
--------------------------------
--------------------------------
Summary of 'C18O (5-4)' observations at 548.8310055 GHz (274.416 GHz at z=1)
--------------------------------
Number of projects = 2
Number of observations = 3
Number of unique subbands = 3
Total number of subbands = 3
Total number of targets with ALMA data = 2
--------------------------------
--------------------------------
Summary of 'C18O (6-5)' observations at 658.5532782 GHz (329.277 GHz at z=1)
--------------------------------
Number of projects = 2
Number of observations = 3
Number of unique subbands = 3
Total number of subbands = 3
Total number of targets with ALMA data = 2
--------------------------------
--------------------------------
Summary of 'C18O (7-6)' observations at 768.2515933 GHz (384.126 GHz at z=1)
--------------------------------
No observations found.
--------------------------------
--------------------------------
Summary of 'C18O (8-7)' observations at 877.9219553 GHz (438.961 GHz at z=1)
--------------------------------
No observations found.
--------------------------------

3. Plot results#

This section introduces all the plotting functions that help visualise the queried observations:

General notes about the plotting functions:

  • Most of the plotting functions have the option to mark frequencies of CO, 13CO, and C18O lines by toggling mark_CO=True. A redshift can be provided by setting the z parameter that will shift the marked frequencies accordingly.

  • Most of the plotting functions have the option to mark a list of frequencies. A redshift can be provided by setting the z parameter that will shift the marked frequencies accordingly.

  • Plots can be saved in PDF format by setting the savefig parameter to the desired filename. Figures are saved in a subdirectory called ‘reports’ within the current working directory. If the directory doesn’t exist, it will be created.

  • To avoid displaying the plot (for example to create and save plots in a loop), set showfig=False.

Load libraries & create a query

To explore these options, we will first query the archive using one of the methods presented in the previous section and use the results in the remainder of this tutorial.

[1]:

import alminer

observations = alminer.keysearch({'science_keyword':['Galaxy chemistry']},
                                 print_targets=False)

================================
alminer.keysearch results
================================
--------------------------------
Number of projects = 54
Number of observations = 376
Number of unique subbands = 1288
Total number of subbands = 1501
Total number of targets with ALMA data = 79
--------------------------------

3.1 Plot an overview of the observations#

The alminer.plot_overview function plots a summary of the observed frequencies, angular resolution, largest angular scales (LAS), and frequency and velocity resolutions.

Note: The histogram of observed frequencies displays the distribution of central frequencies and also depends on the choice of binning. To get an overview plot for a specific frequency, use alminer.plot_observations and alminer.plot_line_overview (see examples below).

Example 3.1.1: plot an overview of the observations and save the figure#
[2]:

alminer.plot_overview(observations, savefig='alma_galaxy_chemistry')
_images/tutorials_3_plot_results_5_0.png

3.2 Plot an overview of a given line in the observations#

The alminer.plot_line_overview function creates overview plots of observed frequencies, angular resolution, LAS, frequency and velocity resolutions for the input DataFrame, and highlights the observations of a give frequency (redshift if the z parameter is set) specified by the user.

Example 3.2.1: plot an overview of the observations and highlight observations at a particular frequency#
[3]:

alminer.plot_line_overview(observations, line_freq=100.0)
_images/tutorials_3_plot_results_8_0.png
Example 3.2.2: plot an overview of the observations and highlight observations at a redshifted frequency#
[4]:

alminer.plot_line_overview(observations, line_freq=400.0, z=2)
_images/tutorials_3_plot_results_10_0.png

3.3 Plot observed frequencies in each band#

The alminer.plot_bands function creates detailed plots of observed frequencies in each band.

Example 3.3.1: plot observed frequencies in each band and mark redshifted CO lines#
[5]:

alminer.plot_bands(observations, mark_CO=True, z=0.5)
_images/tutorials_3_plot_results_13_0.png
Example 3.3.2: plot observed frequencies in each band and mark frequencies of choice#
[6]:

alminer.plot_bands(observations, mark_freq=[106.5, 245.0, 366.3])
_images/tutorials_3_plot_results_15_0.png

3.4 Plot observed frequencies in each band#

The alminer.plot_observations function creates a detailed plot of observations in each band showing the exact observed frequency ranges. Observation numbers are the input DataFrame’s index values.

Example 3.4.1: plot observed frequencies in each band and mark CO lines#
[7]:

alminer.plot_observations(observations, mark_CO=True)
_images/tutorials_3_plot_results_18_0.png
Example 3.4.2: plot observed frequencies and mark frequencies of choice#
[8]:

alminer.plot_observations(observations, mark_freq=[106.5, 245.0, 366.3])
_images/tutorials_3_plot_results_20_0.png

3.5 Plot sky distribution#

The alminer.plot_sky function creates a plot of the distribution of targets on the sky.

Example 3.5.1: plot sky distribution#
[9]:

alminer.plot_sky(observations)
_images/tutorials_3_plot_results_23_0.png

4. Create reports#

This section introduces different ways to save query results:

Load libraries & create a query

To explore these options, we will first query the archive using one of the methods presented in the previous section and use the results in the remainder of this tutorial.

[1]:

import alminer

observations = alminer.keysearch({'science_keyword':['"Galaxy chemistry"']},
                                 print_targets=False)

================================
alminer.keysearch results
================================
--------------------------------
Number of projects = 48
Number of observations = 341
Number of unique subbands = 1166
Total number of subbands = 1368
Total number of targets with ALMA data = 64
--------------------------------

4.1 Export results as a table#

The alminer.save_table function writes the provided DataFrame to a table in CSV format in the ‘tables’ folder within the current working directory. If the ‘tables’ folder does not exist, it will be created.

Example 4.1.1: save query results as a table#
[2]:

alminer.save_table(observations, filename="galaxy_chemistry")

4.2 Save overview plots#

The alminer.save_source_reports function creates overview plots of observed frequencies, angular resolution, LAS, frequency and velocity resolutions for each source in the provided DataFrame and saves them in PDF format in the ‘reports’ folder in the current working directory. If the ‘reports’ folder does not exist, it will be created. The reports are named after the target names.

Note: Currently, the grouping is done based on ALMA target names, so the same source with a slighly different naming schemes will be treated as separate targets.

Example 4.2.1: save overview plots of each target with CO lines marked#

Let’s first narrow down our large query to a smaller subset to only a range of frequencies (Band 3) and angular resolutions < 0.5”:

[3]:

selected = observations[(observations["min_freq_GHz"] > 80.0) &
                        (observations["max_freq_GHz"] < 115.0) &
                        (observations["ang_res_arcsec"] < 0.5)]
alminer.summary(selected)

--------------------------------
Number of projects = 7
Number of observations = 16
Number of unique subbands = 61
Total number of subbands = 61
7 target(s) with ALMA data = ['NGC1266', 'Arp220', 'ngc6240', 'n613', 'NGC4418', 'NGC7469', 'Cloverleaf']
--------------------------------

Now we can create and save plots for each source, with CO and its isotopologues marked:

[4]:

alminer.save_source_reports(selected, mark_CO=True)

5. Download data#

The alminer.download_data function allows the user to download the data from the archive directly to a location on the local disk.

General notes about the download function:

  • The default download location is the ‘data’ subdirectory in the current working directory. The desired location can be changed by setting the location parameter to the desired path.

  • The archive mirror used for downloading can be specified through the archive_mirror parameter. ESO is the default, and other options are NRAO and NAOJ.

  • To check the amount of disk space needed, the dryrun parameter can be toggled to True which will only stage the data and write to the terminal how much space is required.

  • By default, tar files (including both raw and FITS data products) associated with uids in the provided DataFrame will be downloaded.

  • To download only the FITS data products, the fitsonly parameter can be toggled to True.

  • It is possible to provide a list of strings (to the filename_must_include parameter) that the user wants to be included in the filenames that are downloaded. This is useful to restrict the download further, for example, to data that have been primary beam corrected (‘.pbcor’) or that have the science target (’_sci’ or the ALMA target name). The choice is largely dependent on the cycle and type of reduction that was performed, and data products that exist on the archive as a result.

  • A list of URLs (files) to be downloaded from the archive can be printed to the terminal by setting print_urls=True.

Load libraries & create a query

To explore these options, we will first query the archive using one of the methods presented in the previous section and use the results in the remainder of this tutorial.

[1]:

import alminer

observations = alminer.keysearch({'target_name':['G31.41'], 'proposal_id': ['2018']})

================================
alminer.keysearch results
================================
--------------------------------
Number of projects = 2
Number of observations = 3
Number of unique subbands = 9
Total number of subbands = 12
1 target(s) with ALMA data = ['G31.41+0.31']
--------------------------------

Example 5.1: download all data products (raw + products)#

[2]:

alminer.download_data(observations, fitsonly=False, dryrun=True,
                      location='./data', print_urls=False)

================================
This is a dryrun. To begin download, set dryrun=False.
================================
Download location = ./data
Total number of Member OUSs to download = 3
Selected Member OUSs: ['uid://A001/X133d/X325', 'uid://A001/X133d/X327', 'uid://A001/X133d/X21b4']
Number of files to download = 13
Needed disk space = 450.5 GB
--------------------------------

Example 5.2: download only continuum FITS images for the science target#

[3]:

alminer.download_data(observations, fitsonly=True, dryrun=True, location='./data',
                      filename_must_include=['_sci', '.pbcor', 'cont', 'G31.41'],
                      print_urls=True)

================================
This is a dryrun. To begin download, set dryrun=False.
================================
Download location = ./data
Total number of Member OUSs to download = 3
Selected Member OUSs: ['uid://A001/X133d/X325', 'uid://A001/X133d/X327', 'uid://A001/X133d/X21b4']
Number of files to download = 4
Needed disk space = 48.9 MB
File URLs to download = https://almascience.eso.org/dataPortal/member.uid___A001_X133d_X325._G31.41p0.31__sci.spw25_27_29_31.cont.I.tt0.pbcor.fits
https://almascience.eso.org/dataPortal/member.uid___A001_X133d_X325._G31.41p0.31__sci.spw25_27_29_31.cont.I.tt1.pbcor.fits
https://almascience.eso.org/dataPortal/member.uid___A001_X133d_X327._G31.41p0.31__sci.spw25_27_29_31.cont.I.tt0.pbcor.fits
https://almascience.eso.org/dataPortal/member.uid___A001_X133d_X327._G31.41p0.31__sci.spw25_27_29_31.cont.I.tt1.pbcor.fits
--------------------------------

6. Advanced query features#

This Section introduces:

Load alminer

[2]:

import alminer

6.1 Create and run your own TAP query#

You can use Astronomical Data Query Language (ADQL) to create more complex queries relevant for your work.

The ALminer querying functions provide an option to print the query string that was used to search the ALMA archive for the user by setting print_query parameter to True.

Once you have created the query string of interest, you can run it using the alminer.run_query function.

Example 6.1.1: Retrieve the ADQL query string used in ALminer query functions#
[7]:

obs = alminer.keysearch({'proposal_abstract': ['"planet-forming disk"']},
                        print_targets=False, print_query=True)

================================
alminer.keysearch results
================================
Your query is: SELECT * FROM ivoa.obscore WHERE ((LOWER(proposal_abstract) LIKE '%planet-forming disk%')) AND (LOWER(data_rights) LIKE '%public%') AND (LOWER(scan_intent) LIKE '%target%') ORDER BY proposal_id
--------------------------------
Number of projects = 15
Number of observations = 130
Number of unique subbands = 115
Total number of subbands = 665
Total number of targets with ALMA data = 113
--------------------------------
Example 6.1.2: Modify the query string and run the query#

In the previous example, we searched the ALMA archive for projects with the phrase ‘planet forming disk’ in their abstracts. But let’s say you want to include the filtering options directly through the TAP query, for example to only query observations with angular resolutions less than 0.5”.

You can modify the query string accordingly:

[8]:

query_str = "SELECT * FROM ivoa.obscore WHERE ((LOWER(proposal_abstract) LIKE '%planet-forming disk%')) AND (spatial_resolution < 0.5) AND (LOWER(data_rights) LIKE '%public%') AND (LOWER(scan_intent) LIKE '%target%') ORDER BY proposal_id"

And run the query

[9]:

myquery = alminer.run_query(query_str)

6.2 Convert your own query results into ALminer format#

To make use of other alminer functions, the resulting DataFrame returned from running your query has to be converted to alminer format where a few useful columns are added to the DataFrame. This can be done through the alminer.filter_results function.

Example 6.2.1: Convert query results to ALminer format#
[10]:

myquery_obs = alminer.filter_results(myquery)

--------------------------------
Number of projects = 10
Number of observations = 102
Number of unique subbands = 61
Total number of subbands = 509
100 target(s) with ALMA data = ['HD_163296', 'TW_Hya', 'LupusIII_80', 'LupusIII_115', 'LupusIII_74', 'LupusI_10', 'LupusIII_137', 'LupusIII_73', 'LupusIV_142', 'LupusIII_121', 'LupusIII_72', 'LupusIII_133', 'LupusIII_1004', 'LupusIII_1013', 'LupusIII_70', 'LupusIII_132', 'LupusIII_38', 'LupusIII_103', 'LupusIII_106', 'LupusIII_57', 'LupusIII_1007', 'LupusIV_153', 'LupusIII_18', 'LupusIII_34', 'LupusIII_60', 'LupusIII_1008', 'LupusIII_85', 'LupusI_11', 'LupusIII_43', 'LupusIII_141', 'LupusIII_79', 'LupusIII_1009', 'LupusIII_44', 'LupusIII_42', 'LupusIII_37', 'LupusIII_109', 'LupusI_5', 'LupusIV_144', 'LupusIII_99', 'LupusIII_120', 'LupusIII_89', 'LupusIII_76', 'LupusIII_82', 'LupusIII_28', 'LupusI_14', 'LupusIII_51', 'LupusIII_88', 'LupusIII_75', 'LupusIII_1010', 'LupusIII_68', 'LupusIII_67', 'LupusIII_71', 'LupusIII_26', 'LupusIII_66', 'LupusIII_130', 'LupusIII_53', 'LupusIII_33', 'LupusIII_94', 'LupusIII_40', 'LupusIII_91', 'LupusIV_159', 'LupusIII_65', 'LupusIII_116', 'LupusIII_111', 'LupusIII_19', 'LupusIII_21', 'LupusI_13', 'LupusI_15', 'LupusIII_1005', 'LupusIII_1006', 'LupusIII_1003', 'LupusIII_1002', 'LupusIII_49', 'LupusI_12', 'LupusI_16', 'LupusIII_1001', 'LupusIII_1015', 'LupusIII_50', 'LupusI_4', 'LupusIII_114', 'LupusIV_145', 'LupusIII_30', 'LupusIII_52', 'LupusIV_151', 'LupusIV_150', 'LupusIII_87', 'LupusIV_148', 'LupusIV_147', 'LupusIII_113', 'LupusIII_102', 'LupusIII_1014', 'LupusIII_41', 'Serpens-FIRS1', 'V4046_Sgr', 'DG_Tau', 'AS205A', 'RY_Tau', 'ex_lup', 'EM_star_SR_24_N', 'EM_star_SR_20']
--------------------------------

Now you can use all the analysis and plotting routines presented before on these observations.

Scientific categories#

Below is a table of possible ALMA science categories (as of July 2021) that can be provided to the ‘scientific_category’ keyword in the alminer.keysearch function:

ALMA science category

Active galaxies

Cosmology

Disks and planet formation

Galaxy evolution

ISM and star formation

Local Universe

Solar system

Stars and stellar evolution

Sun

Science keywords#

Below is a table of possible ALMA science categories (as of July 2021) that can be provided to the ‘science_keyword’ keyword in the alminer.keysearch function:

ALMA science keyword

Active Galactic Nuclei (AGN)/Quasars (QSO)

Astrochemistry

Asymptotic Giant Branch (AGB) stars

Black holes

Brown dwarfs

Cataclysmic stars

Cosmic Microwave Background (CMB)/Sunyaev-Zel’dovich Effect (SZE)

Damped Lyman Alpha (DLA) systems

Debris disks

Disks around high-mass stars

Disks around low-mass stars

Dwarf/metal-poor galaxies

Early-type galaxies

Evolved stars - Chemistry

Evolved stars - Shaping/physical structure

Exo-planets

Galactic centres/nuclei

Galaxy Clusters

Galaxy chemistry

Galaxy groups and clusters

Galaxy structure & evolution

Gamma Ray Bursts (GRB)

Giant Molecular Clouds (GMC) properties

Gravitational lenses

HII regions

High-mass star formation

High-z Active Galactic Nuclei (AGN)

Hypergiants

Infra-Red Dark Clouds (IRDC)

Inter-Stellar Medium (ISM)/Molecular clouds

Intermediate-mass star formation

Low-mass star formation

Luminous Blue Variables (LBV)

Luminous and Ultra-Luminous Infra-Red Galaxies (LIRG & ULIRG)

Lyman Alpha Emitters/Blobs (LAE/LAB)

Lyman Break Galaxies (LBG)

Magellanic Clouds

Main sequence stars

Merging and interacting galaxies

Outflows, jets, feedback

Outflows, jets and ionized winds

Photon-Dominated Regions (PDR)/X-Ray Dominated Regions (XDR)

Post-AGB stars

Pre-stellar cores

Pulsars and neutron stars

Solar system - Asteroids

Solar system - Comets

Solar system - Planetary atmospheres

Solar system - Planetary surfaces

Solar system - Trans-Neptunian Objects (TNOs)

Spiral galaxies

Starburst galaxies

Starbursts, star formation

Sub-mm Galaxies (SMG)

Supernovae (SN) ejecta

Surveys of galaxies

The Sun

Transients

White dwarfs

Query keywords#

Below is a table of possible keywords that can be used to query the ALMA Science Archive using the alminer.keysearch function:

ALMA query keyword

Type

Description

access_format

char(9)

Content format of the data

access_url

char(72*)

URL to download the data

antenna_arrays

char(660*)

Blank-separated list of Pad:Antenna pairs, i.e., A109:DV09 J504:DV02 J505:DV05 for antennas DV09, DV02 and DV05 sitting on pads A109, J504, and J505, respectively.

asdm_uid

char(32*)

UID of the ASDM containing this Field.

authors

char(4000*)

Full list of first author and all co-authors

band_list

char(30*)

Space delimited list of bands

bib_reference

char(30*)

Bibliography code

data_rights

char(11)

Access to data.

dataproduct_type

char(5*)

type of product

facility_name

char(3)

telescope name

first_author

char(256*)

The first author as provided by telbib.eso.org.

frequency_support

char(4000*)

All frequency ranges used by the field

group_ous_uid

char(64*)

Group OUS ID

instrument_name

char(4)

instrument name

is_mosaic

char(1)

Flag to indicate if this ASDM represents a mosaic or not.

lastModified

char(*)

Time stamp of last modification of the metadata

member_ous_uid

char(64*) |

Member OUS ID

o_ucd

char(35) |

UCD describing the observable axis (pixel values)

obs_collection

char(4)

short name for the data collection

obs_creator_name |

char(256*) |

case-insensitive partial match over the full PI name. Wildcards can be used

obs_id

char(64*) |

internal dataset identifier

obs_publisher_did

char(33*)

publisher dataset identifier

obs_release_date

char(*)

timestamp of date the data becomes publicly available

obs_title

char(256*)

Case-insensitive search over the project title

pol_states

char(64*)

polarization states present in the data

proposal_abstract

char(4000*)

Text search on the proposal abstract. Only abstracts will be returned which contain the given text. The search is case-insensitive.

proposal_authors

char(2000*)

Full name of CoIs.

proposal_id

char(64*)

Identifier of proposal to which NO observation belongs.

pub_abstract

char(4000*)

Case insensitive text search through the abstract of the publication.

pub_title

char(256*)

Case insensitive search through the title of the publication.

qa2_passed

char(1)

Quality Assessment 2 status: does the Member / Group OUS fulfil the PI’s requirements?

s_region

char(*)

region bounded by observation

scan_intent

char(256*)

Scan intent list for the observed field.

schedblock_name

char(128*)

Name of the Scheduling Block used as a template for executing the ASDM containing this Field.

science_keyword

char(200*)

Chosen by the PI in the observing tool at the time of proposal submission. For an overview, see Appendix D of the ALMA Proposer’s Guide. For a precise list, see a this table of science keywords.

science_observation

char(1)

Flag to indicate whether this is a science observation.

scientific_category

char(200*)

Chosen by the PI in the observing tool at the time of proposal submission. For an overview, see Appendix D of the ALMA Proposer’s Guide. For a precise list, see this table of scientific categories.

target_name

char(256*)

name of intended target

type

char(16*)

Type flags.

API#

Note

This documentation is automatically generated from docstrings in the code. Please open an issue on GitHub if you find some missing documentation or if you have suggestions for improvements.

ALminer: ALMA archive mining and visualization toolkit#

A package for mining the Atacama Large Millimeter/submillimeter Array (ALMA) data archive and visualizing the queried observations.

alminer.catalog(target_df, search_radius=1.0, tap_service='ESO', point=False, public=True, published=None, print_query=False, print_targets=True)#

Query the ALMA archive for a list of coordinates or a catalog of sources based on their coordinates.

Parameters:

  • target_df (pandas.DataFrame) –

    Source names and coordinates.

    Index:

    RangeIndex

    Columns:

    Name: Name, dtype: str, description: target name (can be numbers or dummy names) Name: RAJ2000, dtype: float64, description: right ascension in degrees (ICRS) Name: DEJ2000, dtype: float64, description: declination in degrees (ICRS)

  • search_radius (float, optional) – (Default value = 1. arcmin) Search radius (in arcmin) around the source coordinates.

  • tap_service (str, optional) – (Default value = ‘ESO’) The TAP service to use. Options are: ‘ESO’ for Europe (https://almascience.eso.org/tap), ‘NRAO’ for North America (https://almascience.nrao.edu/tap), or ‘NAOJ’ for East Asia (https://almascience.nao.ac.jp/tap)

  • point (bool, optional) – (Default value = True) Search whether the specified position (ra, dec) is contained within any ALMA observations (point=True) or query all ALMA observations that overlap with a cone centred at the specified position (ra, dec) and extending the search_radius (point=False). In the case of point=True, the search_radius parameter is ignored.

  • public (bool, optional) – (Default value = True) Search for public data (public=True), proprietary data (public=False), or both public and proprietary data (public=None).

  • published (bool, optional) – (Default value = None) Search for published data only (published=True), unpublished data only (published=False), or both published and unpublished data (published=None).

  • print_query (bool, optional) – (Default value = True) Print the ADQL TAP query to the terminal.

  • print_targets (bool, optional) – (Default value = False) Print a list of targets with ALMA data (ALMA source names) to the terminal.

Return type:

pandas.DataFrame containing the query results.

alminer.CO_lines(observations, z=0.0, print_summary=True, print_targets=True)#

Determine how many CO, 13CO, and C18O lines were observed in the provided query DataFrame.

Parameters:

  • observations (pandas.DataFrame) – This is likely the output of e.g. ‘conesearch’, ‘target’, ‘catalog’, & ‘keysearch’ functions.

  • z (float64, optional) – (Default value = 0.) Redshift by which the frequencies should be shifted.

  • print_summary (bool, optional) – (Default value = True) Print a summary of the observations for each (redshifted) CO, 13CO, and C18O line to the terminal.

  • print_targets (bool, optional) – (Default value = True) Print the target names (ALMA source names) with ALMA data for each (redshifted) CO, 13CO, and C18O line to the terminal.

Return type:

pandas.DataFrame containing all observations of (redshifted) CO, 13CO, and C18O lines.

alminer.conesearch(ra, dec, search_radius=1.0, tap_service='ESO', point=False, public=True, published=None, print_targets=True, print_query=False)#

Query the ALMA archive for a given position and radius around it.

Parameters:

  • ra (float) – Right ascension in degrees (ICRS).

  • dec (float) – Declination in degrees (ICRS).

  • search_radius (float, optional) – (Default value = 1. arcmin) Search radius (in arcmin) around the source coordinates.

  • tap_service (str, optional) – (Default value = ‘ESO’) The TAP service to use. Options are: ‘ESO’ for Europe (https://almascience.eso.org/tap), ‘NRAO’ for North America (https://almascience.nrao.edu/tap), or ‘NAOJ’ for East Asia (https://almascience.nao.ac.jp/tap)

  • point (bool, optional) – (Default value = True) Search whether the specified position (ra, dec) is contained within any ALMA observations (point=True) or query all ALMA observations that overlap with a cone centred at the specified position (ra, dec) and extending the search_radius (point=False). In the case of point=True, the search_radius parameter is ignored.

  • public (bool, optional) – (Default value = True) Search for public data (public=True), proprietary data (public=False), or both public and proprietary data (public=None).

  • published (bool, optional) – (Default value = None) Search for published data only (published=True), unpublished data only (published=False), or both published and unpublished data (published=None).

  • print_query (bool, optional) – (Default value = True) Print the ADQL TAP query to the terminal.

  • print_targets (bool, optional) – (Default value = False) Print a list of targets with ALMA data (ALMA source names) to the terminal.

Return type:

pandas.DataFrame containing the query results

alminer.download_data(observations, fitsonly=False, dryrun=False, print_urls=False, filename_must_include='', location='./data', archive_mirror='ESO')#

Download ALMA data from the archive to a location on the local machine.

Parameters:

  • observations (pandas.DataFrame) – This is likely the output of e.g. ‘conesearch’, ‘target’, ‘catalog’, & ‘keysearch’ functions.

  • fitsonly (bool, optional) – (Default value = False) Download individual fits files only (fitsonly=True). This option will not download the raw data (e.g. ‘asdm’ files), weblogs, or README files.

  • dryrun (bool, optional) – (Default value = False) Allow the user to do a test run to check the size and number of files to download without actually downloading the data (dryrun=True). To download the data, set dryrun=False.

  • print_urls (bool, optional) – (Default value = False) Write the list of urls to be downloaded from the archive to the terminal.

  • filename_must_include (list of str, optional) – (Default value = ‘’) A list of strings the user wants to be contained in the url filename. This is useful to restrict the download further, for example, to data that have been primary beam corrected (‘.pbcor’) or that have the science target or calibrators (by including their names). The choice is largely dependent on the cycle and type of reduction that was performed and data products that exist on the archive as a result. In most recent cycles, the science target can be filtered out with the flag ‘_sci’ or its ALMA target name.

  • location (str, optional) – (Default value = ./data) directory where the downloaded data should be placed.

  • archive_mirror (str, optional) – (Default value = ‘ESO’) The archive service to use. Options are: ‘ESO’ for Europe (https://almascience.eso.org), ‘NRAO’ for North America (https://almascience.nrao.edu), or ‘NAOJ’ for East Asia (https://almascience.nao.ac.jp)

alminer.explore(observations, allcols=False, allrows=False)#

Control how much of the pandas.DataFrame with the query results is presented in the displayed table.

Parameters:

  • observations (pandas.DataFrame) – This is likely the output of e.g. ‘conesearch’, ‘target’, ‘catalog’, & ‘keysearch’ functions.

  • allcols (bool, optional) – (Default value = False) Show all 81 columns (allcols=True), or the first 18 columns (allcols=False).

  • allrows (bool, optional) – (Default value = False) Show all rows in the DataFrame (allrows=True), or just a summary (allrows=False).

Return type:

pandas.DataFrame containing the query results displayed to the user interface as specified by the user.

alminer.filter_results(TAP_df, print_targets=True)#

Add a few new useful columns to the pandas.DataFrame with the query results from the PyVO TAP service and return the full query DataFrame and optionally a summary of the results.

Parameters:

  • TAP_df (pandas.DataFrame) – This is likely the output of ‘run_query’ function.

  • print_targets (bool, optional) – (Default value = True) Print a list of targets with ALMA data (ALMA source names) to the terminal.

Return type:

pandas.DataFrame containing the query results.

alminer.get_description(column)#

Print the description of a given column in the query results DataFrame.

alminer.get_units(column)#

Print the units for a given column in the query results DataFrame.

alminer.get_info(column)#

Print the description and units of a given column in the query results DataFrame.

Parameters:

column (str) – A column in the pandas.DataFrame query table.

alminer.keysearch(search_dict, tap_service='ESO', public=True, published=None, print_query=False, print_targets=True)#

Query the ALMA archive for any (string-type) keywords defined in ALMA TAP system.

Parameters:

  • search_dict (dict[str, list of str]) – Dictionary of keywords in the ALMA archive and their values. Values must be formatted as a list. A list of valid keywords are stored in VALID_KEYWORDS_STR variable.

  • tap_service (str, optional) – (Default value = ‘ESO’) The TAP service to use. Options are: ‘ESO’ for Europe (https://almascience.eso.org/tap), ‘NRAO’ for North America (https://almascience.nrao.edu/tap), or ‘NAOJ’ for East Asia (https://almascience.nao.ac.jp/tap)

  • public (bool, optional) – (Default value = True) Search for public data (public=True), proprietary data (public=False), or both public and proprietary data (public=None).

  • published (bool, optional) – (Default value = None) Search for published data only (published=True), unpublished data only (published=False), or both published and unpublished data (published=None).

  • print_query (bool, optional) – (Default value = True) Print the ADQL TAP query to the terminal.

  • print_targets (bool, optional) – (Default value = False) Print a list of targets with ALMA data (ALMA source names) to the terminal.

Return type:

pandas.DataFrame containing the query results.

Notes

The power of this function is in combining keywords. When multiple keywords are provided, they are queried using ‘AND’ logic, but when multiple values are provided for a given keyword, they are queried using ‘OR’ logic. If a given value contains spaces, its constituents are queried using ‘AND’ logic. Words encapsulated

in quotation marks (either ‘ or “) are queried as phrases. Values for the ‘target_name’ keyword are queried with ‘OR’ logic.

Examples

keysearch({“proposal_abstract”: [“high-mass star formation outflow disk”]})

will query the archive for projects with the words “high-mass” AND “star” AND “formation” AND “outflow” AND “disk” in their proposal abstracts.

keysearch({“proposal_abstract”: [“high-mass”, “star”, “formation”, “outflow”, “disk”]})

will query the archive for projects with the words “high-mass” OR “star” OR “formation” OR “outflow” OR “disk” in their proposal abstracts.

keysearch({“proposal_abstract”: [“‘high-mass star formation’ outflow disk”]})

will query the archive for projects with the phrase “high-mass star formation” AND the words “outflow” AND “disk” in their proposal abstracts.

keysearch({“proposal_abstract”: [“‘star formation’”], “scientific_category”:[‘Galaxies’]})

will query the archive for projects with the phrase “star formation” in their proposal abstracts AND projects that are within the scientific_category of ‘Galaxies’.

alminer.line_coverage(observations, line_freq, z=0.0, line_name='', print_summary=True, print_targets=True)#

Determine how many observations were observed at a given frequency (+redshift).

Parameters:

  • observations (pandas.DataFrame) – This is likely the output of e.g. ‘conesearch’, ‘target’, ‘catalog’, & ‘keysearch’ functions.

  • line_freq (float64) – Frequency of the line of interest in GHz.

  • z (float64, optional) – (Default value = 0.) Redshift by which the frequency given in ‘line_freq’ parameter should be shifted.

  • line_name (str, optional) – (Default value = ‘’) Name of the line specified in ‘line_freq’.

  • print_summary (bool, optional) – (Default value = True) Print a summary of the observations to the terminal.

  • print_targets (bool, optional) – (Default value = True) Print a list of targets with ALMA data (ALMA source names) to the terminal.

Return type:

pandas.DataFrame containing all observations of line of interest.

alminer.plot_line_overview(observations, line_freq, z=0.0, line_name='', showfig=True, savefig=None)#

Create overview plots of observed frequencies, angular resolution, LAS, frequency and velocity resolutions, highlighting the observations of a give (redshifted) frequency with hatches on the bar plots.

Parameters:

  • observations (pandas.DataFrame) – This is likely the output of e.g. ‘conesearch’, ‘target’, ‘catalog’, & ‘keysearch’ functions.

  • line_freq (float64) – Frequency of the line of interest in GHz.

  • z (float64, optional) – (Default value = 0.) Redshift by which the frequency given in ‘line_freq’ parameter should be shifted.

  • line_name (str, optional) – (Default value = ‘’) Name of the line specified in ‘line_freq’.

  • showfig (bool, optional) – (Default value = True) Display the plot (showfig=True) or not (showfig=False).

  • savefig (str, optional) – (Default value = None) Filename (without an extension) for the plot to be saved as. Default file extension is PDF. Figure is saved in a subdirectory called ‘reports’ within the current working directory. If the directory doesn’t exist, it will be created. Default quality is dpi=300.

alminer.plot_overview(observations, mark_freq='', z=0.0, mark_CO=False, showfig=True, savefig=None)#

Create overview plots of observed frequencies, angular resolution, LAS, frequency and velocity resolutions.

Parameters:

  • observations (pandas.DataFrame) – This is likely the output of e.g. ‘conesearch’, ‘target’, ‘catalog’, & ‘keysearch’ functions.

  • mark_freq (list of float64, optional) – (Default value = ‘’) A list of frequencies to mark on the plot with dashed lines.

  • z (float64, optional) – (Default value = 0.) Redshift by which the frequencies given in ‘mark_freq’ and ‘mark_CO’ parameters should be shifted. Currently only one redshift can be given for all targets.

  • mark_CO (bool, optional) – (Default value = False) Mark CO, 13CO, and C18O frequencies on the plot with dashed lines.

  • showfig (bool, optional) – (Default value = True) Display the plot (showfig=True) or not (showfig=False).

  • savefig (str, optional) – (Default value = None) Filename (without an extension) for the plot to be saved as. Default file extension is PDF. Figure is saved in a subdirectory called ‘reports’ within the current working directory. If the directory doesn’t exist, it will be created. Default quality is dpi=300.

alminer.plot_observations(observations, mark_freq='', z=0.0, mark_CO=False, showfig=True, savefig=None)#

Create detailed plots of observations in each band. The x-axis displays the observation number ‘Obs’ column in the input DataFrame.

Parameters:

  • observations (pandas.DataFrame) – This is likely the output of e.g. ‘conesearch’, ‘target’, ‘catalog’, & ‘keysearch’ functions.

  • mark_freq (list of float64, optional) – (Default value = ‘’) A list of frequencies to mark on the plot with dashed lines.

  • z (float64, optional) – (Default value = 0.) Redshift by which the frequencies given in ‘mark_freq’ and ‘mark_CO’ parameters should be shifted. Currently only one redshift can be given for all targets.

  • mark_CO (bool, optional) – (Default value = False) Mark CO, 13CO, and C18O frequencies on the plot with dashed lines.

  • showfig (bool, optional) – (Default value = True) Display the plot (showfig=True) or not (showfig=False).

  • savefig (str, optional) – (Default value = None) Filename (without an extension) for the plot to be saved as. Default file extension is PDF. Figure is saved in a subdirectory called ‘reports’ within the current working directory. If the directory doesn’t exist, it will be created. Default quality is dpi=300.

alminer.plot_bands(observations, mark_freq='', z=0.0, mark_CO=False, showfig=True, savefig=None)#

Create overview and detailed plots of observed frequencies in each band.

Parameters:

  • observations (pandas.DataFrame) – This is likely the output of e.g. ‘conesearch’, ‘target’, ‘catalog’, & ‘keysearch’ functions.

  • mark_freq (list of float64, optional) – (Default value = ‘’) A list of frequencies to mark on the plot with dashed lines.

  • z (float64, optional) – (Default value = 0.) Redshift by which the frequencies given in ‘mark_freq’ and ‘mark_CO’ parameters should be shifted. Currently only one redshift can be given for all targets.

  • mark_CO (bool, optional) – (Default value = False) Mark CO, 13CO, and C18O frequencies on the plot with dashed lines.

  • showfig (bool, optional) – (Default value = True) Display the plot (showfig=True) or not (showfig=False).

  • savefig (str, optional) – (Default value = None) Filename (without an extension) for the plot to be saved as. Default file extension is PDF. Figure is saved in a subdirectory called ‘reports’ within the current working directory. If the directory doesn’t exist, it will be created. Default quality is dpi=300.

alminer.plot_sky(observations, showfig=True, savefig=None)#

Plot the distribution of the targets on the sky.

Parameters:

  • observations (pandas.DataFrame) – This is likely the output of e.g. ‘conesearch’, ‘target’, ‘catalog’, & ‘keysearch’ functions.

  • showfig (bool, optional) – (Default value = True) Display the plot (showfig=True) or not (showfig=False).

  • savefig (str, optional) – (Default value = None) Filename (without an extension) for the plot to be saved as. Default file extension is PDF. Figure is saved in a subdirectory called ‘reports’ within the current working directory. If the directory doesn’t exist, it will be created. Default quality is dpi=300.

alminer.run_query(query_str, tap_service='ESO')#

Run the TAP query through PyVO service.

Parameters:
Return type:

pandas.DataFrame containing the query results

alminer.summary(observations, print_targets=True)#

Print a summary of the observations.

Parameters:

  • observations (pandas.DataFrame) – This is likely the output of e.g. ‘conesearch’, ‘target’, ‘catalog’, & ‘keysearch’ functions.

  • print_targets (bool, optional) – (Default value = True) Print a list of targets with ALMA data (ALMA source names) to the terminal.

alminer.target(sources, search_radius=1.0, tap_service='ESO', point=False, public=True, published=None, print_query=False, print_targets=True)#

Query targets by name.

This is done by using the astropy SESAME resolver to get the target’s coordinates and then the ALMA archive is queried for those coordinates and a search_radius around them. The SESAME resolver searches multiple databases (Simbad, NED, VizieR) to parse names commonly found throughout literature and returns their coordinates. If the target is not resolved in any of these databases, consider using the ‘keysearch’ function and query the archive using the ‘target_name’ keyword (e.g. keysearch({‘target_name’: sources})).

Parameters:

  • sources (str or list of str) – list of sources by name. (IMPORTANT: source names must be identified by at least one of Simbad, NED, or Vizier)

  • search_radius (float, optional) – (Default value = 1. arcmin) Search radius (in arcmin) around the source coordinates.

  • tap_service (str, optional) – (Default value = ‘ESO’) The TAP service to use. Options are: ‘ESO’ for Europe (https://almascience.eso.org/tap), ‘NRAO’ for North America (https://almascience.nrao.edu/tap), or ‘NAOJ’ for East Asia (https://almascience.nao.ac.jp/tap)

  • point (bool, optional) – (Default value = True) Search whether the specified position (ra, dec) is contained within any ALMA observations (point=True) or query all ALMA observations that overlap with a cone centred at the specified position (ra, dec) and extending the search_radius (point=False). In the case of point=True, the search_radius parameter is ignored.

  • public (bool, optional) – (Default value = True) Search for public data (public=True), proprietary data (public=False), or both public and proprietary data (public=None).

  • published (bool, optional) – (Default value = None) Search for published data only (published=True), unpublished data only (published=False), or both published and unpublished data (published=None).

  • print_query (bool, optional) – (Default value = True) Print the ADQL TAP query to the terminal.

  • print_targets (bool, optional) – (Default value = False) Print a list of targets with ALMA data (ALMA source names) to the terminal.

Return type:

pandas.DataFrame containing the query results.

See also

keysearch

Query the ALMA archive for any (string-type) keywords defined in ALMA TAP system.

alminer.save_source_reports(observations, mark_freq='', z=0.0, mark_CO=False)#

Create overview plots of observed frequencies, angular resolution, LAS, frequency and velocity resolutions for each source in the provided DataFrame and save them in PDF format in the ‘reports’ subdirectory. If the directory doesn’t exist, it will be created.

Parameters:

  • observations (pandas.DataFrame) – This is likely the output of e.g. ‘conesearch’, ‘target’, ‘catalog’, & ‘keysearch’ functions.

  • mark_freq (list of float64, optional) – (Default value = ‘’) A list of frequencies to mark on the plot with dashed lines.

  • z (float64, optional) – (Default value = 0.) Redshift by which the frequencies given in ‘mark_freq’ and ‘mark_CO’ parameters should be shifted. Currently only one redshift can be given for all targets.

  • mark_CO (bool, optional) – (Default value = False) Mark CO, 13CO, and C18O frequencies on the plot with dashed lines.

Notes

Reports will be grouped by ALMA target names, therefore the same source with many different ALMA names will be treated as individual unique targets (e.g. TW_Hya, TW Hya, twhya).

alminer.save_table(observations, filename='mytable')#

Write the DataFrame with the query results to a table in CSV format.

The table will be saved in the ‘tables’ subdirectory within the current working directory. If the directory doesn’t exist, it will be created.

Parameters:

  • observations (pandas.DataFrame) –

  • filename (str) – (Default value = “mytable”) Name of the table to be saved in the ‘tables’ subdirectory.

What’s new#

  • You can now specify which archive mirror to download data from: [ESO](https://almascience.eso.org/aq) is the default, and other options are [NRAO](https://almascience.nrao.edu/aq) and [NAOJ](https://almascience.nao.ac.jp/aq). This option can be given through the ‘archive_mirror’ parameter in the download_data function.

  • You can now specify which archive service to query: [ESO](https://almascience.eso.org/tap) is the default, and other options are [NRAO](https://almascience.nrao.edu/tap) and [NAOJ](https://almascience.nrao.edu/tap). This option can be given through the ‘tap_service’ parameter to all functions that do the query (e.g. keysearch, target, catalog). For example:

    • alminer.target(["TW Hya", "HL Tau"], tap_service='NRAO')

    • Note that currently the ESO service is not returning all results, hence it is advisable to test your queries with multiple services until further notice.

  • It is now possible to query entire phrases with the keysearch function. For example:

    • alminer.keysearch({'proposal_abstract': ['"high-mass star formation" outflow disk']}) will query the proposal abstracts for the phrase high-mass star formation AND the words outflow AND disk.

    • alminer.keysearch({'proposal_abstract': ['"high-mass star formation" outflow disk', '"massive star formation" outflow disk']}) will query the the proposal abstracts for the phrase high-mass star formation AND the words outflow AND disk OR the phrase massive star formation AND the words outflow AND disk.

Acknowledgements#

alminer has been developed through a collaboration between Allegro, the ALMA Regional Centre in The Netherlands, and the University of Vienna as part of the EMERGE-StG project. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 851435).

If you use alminer as part of your research, please consider citing this ASCL article (ADS reference will be added to the GitHub page when available).

alminer makes use of different routines in Astropy and Astroquery. Please also consider citing the following papers: - Astropy: Astropy Collaboration et al. 2013 - Astroquery: Ginsburg et al. 2019

We also acknowledge the work of Leiden University M.Sc. students, Robin Mentel and David van Dop, who contributed to early versions of this work.

Contact us#

If you encounter issues, please open an issue.

If you have suggestions for improvement or would like to collaborate with us on this project, please e-mail Aida Ahmadi and Alvaro Hacar.