X-ray observations of a sample of clusters selected from eFEDS

This case study searches for X-ray observations of the eFEDS sample of galaxy clusters, and is intended to show how DAXA missions can be used to create multi-mission archives of data for large samples of objects; this case study would apply just as well to any type of X-ray source. The clusters we use will all have eFEDS observations, as they were selected from that survey, but many should also have serendipitous XMM and Chandra observations.

Import Statements

[1]:

import numpy as np
import pandas as pd
from astropy.coordinates import SkyCoord

from daxa.mission import XMMPointed, Chandra, eROSITACalPV
from daxa.archive import Archive

Other Tutorials

These case studies are meant to be highly specific examples of how you might acquire data for a particular science case, they do not provide general instruction on how to use DAXA missions or archives. We instead direct you to:

  • Using DAXA missions - Here we explain what DAXA mission classes are and how to use them to select only the data you need.

  • Creating a DAXA archive - This explains how to create an archive, load an existing archive, and the various properties and features of DAXA archives.

  • Processing telescope data - The processing tutorials for different missions are presented here, though there may not yet be processing support for all missions.

Reading through these should give you a good understanding of how DAXA can be used to acquire, organise, and process multi-mission X-ray datasets for your specific use case.

Sample

We read in the sample of galaxy clusters we’ll be searching for observations of - they were selected from the eROSITA Final-Equatorial Depth Survey (eFEDS), and many should have XMM and Chandra observations:

[2]:

samp = pd.read_csv("samp_files/efeds_xray_cluster_candidates.csv")
samp.head(5)

[2]:
ID ID_SRC RA DEC EXT_LIKE DET_LIKE z z_type T_300kpc T_300kpc_L ... L_500kpc_L L_500kpc_U Lbol_500kpc Lbol_500kpc_L Lbol_500kpc_U F_500kpc F_500kpc_L F_500kpc_U SNR_MAX R_SNR_MAX_ARCMIN
0 eFEDS J082626.6-003429 28993 126.610799 -0.574787 8.486203 5.029723 0.161110 0 -1.000000 -1.000000 ... -1.000000e+00 3.924900e+42 -1.000000e+00 -1.000000e+00 1.782000e+43 -1.000000e+00 -1.000000e+00 6.040400e-14 1.32 0.8011
1 eFEDS J082751.8-002853 11248 126.965471 -0.481638 12.791595 27.865910 0.257160 0 -1.000000 -1.000000 ... -1.000000e+00 1.061100e+43 -1.000000e+00 -1.000000e+00 1.796300e+43 -1.000000e+00 -1.000000e+00 4.903300e-14 3.07 0.7393
2 eFEDS J082808.8-001003 4800 127.036645 -0.167715 28.492811 62.512480 0.076155 0 0.885294 0.786329 ... 2.339100e+42 3.424700e+42 4.402300e+42 3.640200e+42 5.339000e+42 1.976100e-13 1.624400e-13 2.382600e-13 8.78 2.6312
3 eFEDS J082820.6-000721 4169 127.085556 -0.122752 42.376125 81.378350 0.844900 0 -1.000000 -1.000000 ... 1.890600e+44 2.762200e+44 6.207600e+44 5.089800e+44 8.013600e+44 7.796600e-14 6.555100e-14 8.961400e-14 7.30 1.4667
4 eFEDS J082840.6-000500 7991 127.169202 -0.083552 18.438711 37.515427 0.319705 0 -1.000000 -1.000000 ... 1.419400e+43 2.065400e+43 3.993600e+43 3.175500e+43 4.931700e+43 5.510200e-14 4.443600e-14 6.466500e-14 5.61 1.3388

5 rows × 34 columns

[3]:

coords = SkyCoord(samp['RA'].values, samp['DEC'].values, unit='deg')

Defining missions

[4]:

er = eROSITACalPV()
xm = XMMPointed()
ch = Chandra()

/Users/dt237/code/DAXA/daxa/mission/xmm.py:83: UserWarning: 140 of the 17697 observations located for this mission have been removed due to NaN RA or Dec values
  self._fetch_obs_info()

Searching for observations

We search for observations around the coordinates of our cluster sample - it is worth noting that we are using the default FoV radius/half-width multiplied by a factor of 1.2 as a search radius. You may also set this value yourself, for each instrument individually (for missions like Chandra) or for all instruments, using the search_distance argument and an astropy quantity in units convertible to degrees.

Also, if return_obs_info is set to True, a dataframe is returned from the method to allow the user to link specific ObsIDs to particular entries in our original sample table. The dataframe contains a ‘pos_ind’ column, which contains indexes corresponding to the input positions (i.e. the 4th entry of pos would have index 3), it also contains ObsIDs matched to that coordinate.

[5]:

er_assoc = er.filter_on_positions(coords, return_pos_obs_info=True)
xm_assoc = xm.filter_on_positions(coords, return_pos_obs_info=True)
ch_assoc = ch.filter_on_positions(coords, return_pos_obs_info=True)

/Users/dt237/code/DAXA/daxa/mission/base.py:1095: UserWarning: A field-of-view cannot be easily defined for eROSITACalPV and this number is the approximate half-length of an eFEDS section, the worst case separation - this is unnecessarily large for pointed observations, and you should make your own judgement on a search distance.
  fov = self.fov
/Users/dt237/code/DAXA/daxa/mission/base.py:1095: UserWarning: Chandra FoV are difficult to define, as they can be strongly dependant on observation mode; as such take these as very approximate.
  fov = self.fov

Exploring the selected data

We can examine the filtered_obs_info property (see the missions tutorial for a fuller explanation):

[6]:

er.filtered_obs_info

[6]:
ra dec ObsID science_usable start end duration Field_Name Field_Type
0 129.55 1.5 300007 True 2019-11-03 02:42:50 2019-11-04 03:36:37 89627.0 EFEDS SURVEY
1 133.86 1.5 300008 True 2019-11-04 03:49:16 2019-11-05 05:16:39 91643.0 EFEDS SURVEY
2 138.14 1.5 300009 True 2019-11-05 05:29:18 2019-11-06 06:40:06 90648.0 EFEDS SURVEY
3 142.45 1.5 300010 True 2019-11-06 07:24:46 2019-11-07 08:20:08 89722.0 EFEDS SURVEY 
[7]:

xm.filtered_obs_info

[7]:
ra dec ObsID start science_usable duration proprietary_end_date revolution proprietary_usable end
3629 130.198335 0.763056 0202940101 2004-05-08 16:32:31 True 0 days 08:03:28 2006-03-04 808 True 2004-05-09 00:35:59
3630 130.198335 0.763056 0202940201 2004-10-26 17:36:30 True 0 days 06:28:36 2006-03-04 894 True 2004-10-27 00:05:06
5137 136.602495 0.965556 0402780801 2007-04-20 15:52:25 True 0 days 07:33:33 2008-05-20 1348 True 2007-04-20 23:25:58
7355 138.022125 0.483667 0602340201 2009-12-01 01:27:07 True 0 days 07:11:13 2010-12-17 1827 True 2009-12-01 08:38:20
7385 138.723585 4.442889 0602830401 2009-11-16 01:38:06 True 0 days 02:17:00 2010-12-01 1820 True 2009-11-16 03:55:06
... ... ... ... ... ... ... ... ... ... ...
17163 140.539000 3.775000 0920001301 2023-10-27 11:23:39 True 0 days 03:50:00 2024-11-13 4374 False 2023-10-27 15:13:39
17164 140.539000 3.775000 0920002501 2023-10-27 07:26:59 True 0 days 03:56:40 2024-11-13 4374 False 2023-10-27 11:23:39
17166 129.890833 -1.679000 0920000901 2023-10-29 11:14:11 True 0 days 04:46:40 2024-11-27 4375 False 2023-10-29 16:00:51
17167 129.890833 -1.679000 0920002601 2023-10-29 07:19:11 True 0 days 03:55:00 2024-11-27 4375 False 2023-10-29 11:14:11
17285 139.106000 1.385000 0920980301 2023-11-30 05:12:14 True 0 days 12:55:00 2024-12-28 4391 False 2023-11-30 18:07:14

113 rows × 10 columns

[8]:

ch.filtered_obs_info

[8]:
ra dec ObsID science_usable proprietary_usable start end duration proprietary_end_date target_category instrument grating data_mode
1702 136.91667 -0.69994 17084 True True 2015-01-10 00:32:52.999996 2015-01-10 16:07:22.999996 0 days 15:34:30 2016-01-12 AGN ACIS-S NONE TE_006C8
2148 144.43375 2.76094 20348 True True 2018-01-22 17:22:56.000004 2018-01-23 07:18:06.000004 0 days 13:55:10 2019-01-23 NGS ACIS-S NONE TE_005C6
3684 140.94625 4.04850 23835 True True 2022-05-16 13:22:45.999995 2022-05-17 00:28:45.999995 0 days 11:06:00 2023-05-17 AGN ACIS-S NONE TE_007F2
3885 137.31625 3.91186 23162 True True 2020-02-20 03:27:14.999999 2020-02-20 14:10:44.999999 0 days 10:43:30 2021-02-20 AGN ACIS-S NONE TE_0065E
4605 137.86458 5.84778 14958 True True 2012-12-30 10:27:00.999997 2012-12-30 19:54:40.999997 0 days 09:27:40 2013-12-31 AGN ACIS-S NONE TE_009C8
... ... ... ... ... ... ... ... ... ... ... ... ... ...
20574 145.23833 3.40033 11451 True True 2010-01-04 14:03:12.999998 2010-01-04 14:39:12.999998 0 days 00:36:00 2011-01-05 AGN ACIS-S NONE TE_008FC
20890 135.83750 4.96056 11448 True True 2010-01-04 15:01:54.999999 2010-01-04 15:35:44.999999 0 days 00:33:50 2011-01-05 AGN ACIS-S NONE TE_008FC
21229 143.81125 3.59603 5705 True True 2005-03-07 04:46:24.000001 2005-03-07 05:16:24.000001 0 days 00:30:00 2006-03-08 AGN ACIS-S NONE TE_006B0
21409 130.27833 3.20189 13347 True True 2011-12-11 12:21:05.000000 2011-12-11 12:47:05.000000 0 days 00:26:00 2012-12-12 AGN ACIS-S NONE TE_0076A
21559 137.35000 0.03639 5703 True True 2004-12-28 09:24:24.999999 2004-12-28 09:46:54.999999 0 days 00:22:30 2005-12-29 AGN ACIS-S NONE TE_006B0

90 rows × 13 columns

We can also use the tables that were returned from the search methods to match observations to specific objects:

[9]:

xm_assoc['name'] = samp.loc[xm_assoc['pos_ind'].values.astype(int), 'ID'].values
xm_assoc

[9]:
pos_ind pos_ra pos_dec ObsIDs name
0 45 129.3486854874346 1.40366630348438 0903700101 eFEDS J083723.7+012413
1 47 129.48802147587307 -1.7049345640737046 0920001601,0920002401 eFEDS J083757.2-014217
2 52 129.5393896036211 -2.0807673161720084 0920001601,0920002401 eFEDS J083809.5-020450
3 54 129.54957579257595 -1.9929684395330654 0920001601,0920002401 eFEDS J083812.0-015934
4 56 129.5732303438282 -2.284546510019436 0920001601,0920002401 eFEDS J083817.6-021704
... ... ... ... ... ...
110 469 141.93603300405417 4.941812984556808 0901870201,0901871201 eFEDS J092744.6+045631
111 491 142.47325179257808 0.4670133405850062 0802220601 eFEDS J092953.6+002801
112 521 143.75324046077222 0.9047856577567157 0920000801,0920002301 eFEDS J093500.8+005417
113 522 143.80457579850201 0.7993807870395315 0920000801,0920002301 eFEDS J093513.1+004757
114 523 143.8362603912554 0.5801534818576921 0920000801,0920002301 eFEDS J093520.7+003448

115 rows × 5 columns

[10]:

ch_assoc['name'] = samp.loc[ch_assoc['pos_ind'].values.astype(int), 'ID'].values
ch_assoc

[10]:
pos_ind pos_ra pos_dec ObsIDs name
0 9 127.79401199149834 1.9378392793445995 19734 eFEDS J083110.6+015616
1 12 127.85804170868693 1.925897930055847 19734 eFEDS J083125.9+015533
2 16 127.97336088473476 1.4252588642845447 19734 eFEDS J083153.6+012531
3 18 128.11694107938197 -0.1156644071035969 23614 eFEDS J083228.1-000656
4 22 128.31476848695075 0.106508629638464 23614 eFEDS J083315.6+000623
... ... ... ... ... ...
187 537 144.43405705683216 2.760045545939909 20348 eFEDS J093744.2+024536
188 538 144.6271718639683 4.256395791583636 18099 eFEDS J093830.5+041523
189 539 144.909595641361 4.371716376951857 26035 eFEDS J093938.3+042218
190 540 145.0245934774472 3.224725957866028 22270,11451 eFEDS J094005.9+031329
191 541 145.03072779231115 3.965204479523988 26035,22270 eFEDS J094007.3+035755

192 rows × 5 columns

[11]:

er_assoc['name'] = samp.loc[er_assoc['pos_ind'].values.astype(int), 'ID'].values
er_assoc

[11]:
pos_ind pos_ra pos_dec ObsIDs name
0 0 126.61080512258118 -0.5747925179258094 300007 eFEDS J082626.6-003429
1 1 126.96547689559516 -0.4816435127681215 300007 eFEDS J082751.8-002853
2 2 127.03665115606483 -0.1677211251616217 300007 eFEDS J082808.8-001003
3 3 127.08556215338794 -0.1227575917789436 300007 eFEDS J082820.6-000721
4 4 127.1692087960137 -0.08355838266078222 300007 eFEDS J082840.6-000500
... ... ... ... ... ...
537 537 144.43406327204823 2.7600402745537114 300010 eFEDS J093744.2+024536
538 538 144.62717799852624 4.256390530427462 300010 eFEDS J093830.5+041523
539 539 144.90960176777745 4.3717111308643295 300010 eFEDS J093938.3+042218
540 540 145.0245996650002 3.2247207179507202 300010 eFEDS J094005.9+031329
541 541 145.03073393985625 3.9651992399385168 300010 eFEDS J094007.3+035755

542 rows × 5 columns

Defining an archive

The filtered missions can then be used to define an archive containing the selected data:

[12]:

arch = Archive('eFEDS_clusters', [er, xm, ch])
arch.info()

/Users/dt237/code/DAXA/daxa/archive/base.py:133: UserWarning: The raw data for this mission have already been downloaded.
  mission.download()
/Users/dt237/code/DAXA/daxa/archive/base.py:133: UserWarning: Proprietary data have been selected, but no credentials provided; as such the proprietary data have been excluded from download and further processing.
  mission.download()
Downloading XMM-Newton Pointed data: 100%|██████████████████████████████████| 100/100 [00:18<00:00,  5.38it/s]
Downloading Chandra data: 100%|███████████████████████████████████████████████| 90/90 [03:01<00:00,  2.02s/it]


-----------------------------------------------------
Number of missions - 3
Total number of observations - 194
Beginning of earliest observation - 1999-11-02 17:31:43.000001
End of latest observation - 2023-02-26 11:24:55.000001

-- eROSITACalPV --
   Internal DAXA name - erosita_calpv
   Chosen instruments - TM1, TM2, TM3, TM4, TM5, TM6, TM7
   Number of observations - 4
   Fully Processed - False

-- XMM-Newton Pointed --
   Internal DAXA name - xmm_pointed
   Chosen instruments - M1, M2, PN
   Number of observations - 100
   Fully Processed - False

-- Chandra --
   Internal DAXA name - chandra
   Chosen instruments - ACIS-I, ACIS-S, HRC-I, HRC-S
   Number of observations - 90
   Fully Processed - False
-----------------------------------------------------