{ "cells": [ { "cell_type": "markdown", "id": "a13b8653", "metadata": {}, "source": [ "# X-ray observations of a sample of clusters selected from eFEDS" ] }, { "cell_type": "markdown", "id": "2453f437", "metadata": {}, "source": [ "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." ] }, { "cell_type": "markdown", "id": "5e2a28b1", "metadata": {}, "source": [ "## Import Statements" ] }, { "cell_type": "code", "execution_count": 1, "id": "aa9f16e8", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from astropy.coordinates import SkyCoord\n", "\n", "from daxa.mission import XMMPointed, Chandra, eROSITACalPV\n", "from daxa.archive import Archive" ] }, { "cell_type": "markdown", "id": "fa824356", "metadata": {}, "source": [ "## Other Tutorials" ] }, { "cell_type": "markdown", "id": "cc8b77fc", "metadata": {}, "source": [ "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:\n", "\n", "* [Using DAXA missions](../missions.html) - Here we explain what DAXA mission classes are and how to use them to select only the data you need.\n", "* [Creating a DAXA archive](../archives.html) - This explains how to create an archive, load an existing archive, and the various properties and features of DAXA archives.\n", "* [Processing telescope data](../../../tutorials.process.html) - The processing tutorials for different missions are presented here, though there may not yet be processing support for all missions.\n", "\n", "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." ] }, { "cell_type": "markdown", "id": "d39a0cd0", "metadata": {}, "source": [ "## Sample" ] }, { "cell_type": "markdown", "id": "30377d82", "metadata": {}, "source": [ "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:" ] }, { "cell_type": "code", "execution_count": 2, "id": "78c35ab9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDID_SRCRADECEXT_LIKEDET_LIKEzz_typeT_300kpcT_300kpc_L...L_500kpc_LL_500kpc_ULbol_500kpcLbol_500kpc_LLbol_500kpc_UF_500kpcF_500kpc_LF_500kpc_USNR_MAXR_SNR_MAX_ARCMIN
0eFEDS J082626.6-00342928993126.610799-0.5747878.4862035.0297230.1611100-1.000000-1.000000...-1.000000e+003.924900e+42-1.000000e+00-1.000000e+001.782000e+43-1.000000e+00-1.000000e+006.040400e-141.320.8011
1eFEDS J082751.8-00285311248126.965471-0.48163812.79159527.8659100.2571600-1.000000-1.000000...-1.000000e+001.061100e+43-1.000000e+00-1.000000e+001.796300e+43-1.000000e+00-1.000000e+004.903300e-143.070.7393
2eFEDS J082808.8-0010034800127.036645-0.16771528.49281162.5124800.07615500.8852940.786329...2.339100e+423.424700e+424.402300e+423.640200e+425.339000e+421.976100e-131.624400e-132.382600e-138.782.6312
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" ], "text/plain": [ " ID ID_SRC RA DEC EXT_LIKE DET_LIKE \\\n", "0 eFEDS J082626.6-003429 28993 126.610799 -0.574787 8.486203 5.029723 \n", "1 eFEDS J082751.8-002853 11248 126.965471 -0.481638 12.791595 27.865910 \n", "2 eFEDS J082808.8-001003 4800 127.036645 -0.167715 28.492811 62.512480 \n", "3 eFEDS J082820.6-000721 4169 127.085556 -0.122752 42.376125 81.378350 \n", "4 eFEDS J082840.6-000500 7991 127.169202 -0.083552 18.438711 37.515427 \n", "\n", " z z_type T_300kpc T_300kpc_L ... L_500kpc_L L_500kpc_U \\\n", "0 0.161110 0 -1.000000 -1.000000 ... -1.000000e+00 3.924900e+42 \n", "1 0.257160 0 -1.000000 -1.000000 ... -1.000000e+00 1.061100e+43 \n", "2 0.076155 0 0.885294 0.786329 ... 2.339100e+42 3.424700e+42 \n", "3 0.844900 0 -1.000000 -1.000000 ... 1.890600e+44 2.762200e+44 \n", "4 0.319705 0 -1.000000 -1.000000 ... 1.419400e+43 2.065400e+43 \n", "\n", " Lbol_500kpc Lbol_500kpc_L Lbol_500kpc_U F_500kpc F_500kpc_L \\\n", "0 -1.000000e+00 -1.000000e+00 1.782000e+43 -1.000000e+00 -1.000000e+00 \n", "1 -1.000000e+00 -1.000000e+00 1.796300e+43 -1.000000e+00 -1.000000e+00 \n", "2 4.402300e+42 3.640200e+42 5.339000e+42 1.976100e-13 1.624400e-13 \n", "3 6.207600e+44 5.089800e+44 8.013600e+44 7.796600e-14 6.555100e-14 \n", "4 3.993600e+43 3.175500e+43 4.931700e+43 5.510200e-14 4.443600e-14 \n", "\n", " F_500kpc_U SNR_MAX R_SNR_MAX_ARCMIN \n", "0 6.040400e-14 1.32 0.8011 \n", "1 4.903300e-14 3.07 0.7393 \n", "2 2.382600e-13 8.78 2.6312 \n", "3 8.961400e-14 7.30 1.4667 \n", "4 6.466500e-14 5.61 1.3388 \n", "\n", "[5 rows x 34 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "samp = pd.read_csv(\"samp_files/efeds_xray_cluster_candidates.csv\")\n", "samp.head(5)" ] }, { "cell_type": "markdown", "id": "6517f551", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 3, "id": "549a84d7", "metadata": {}, "outputs": [], "source": [ "coords = SkyCoord(samp['RA'].values, samp['DEC'].values, unit='deg')" ] }, { "cell_type": "markdown", "id": "cda7ed3b", "metadata": {}, "source": [ "## Defining missions" ] }, { "cell_type": "markdown", "id": "1db78091", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 4, "id": "032436b4", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/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\n", " self._fetch_obs_info()\n" ] } ], "source": [ "er = eROSITACalPV()\n", "xm = XMMPointed()\n", "ch = Chandra()" ] }, { "cell_type": "markdown", "id": "c7dda794", "metadata": {}, "source": [ "## Searching for observations" ] }, { "cell_type": "markdown", "id": "df78c594", "metadata": {}, "source": [ "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.\n", "\n", "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." ] }, { "cell_type": "code", "execution_count": 5, "id": "2e622e5a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/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.\n", " fov = self.fov\n", "/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.\n", " fov = self.fov\n" ] } ], "source": [ "er_assoc = er.filter_on_positions(coords, return_pos_obs_info=True)\n", "xm_assoc = xm.filter_on_positions(coords, return_pos_obs_info=True)\n", "ch_assoc = ch.filter_on_positions(coords, return_pos_obs_info=True)" ] }, { "cell_type": "markdown", "id": "67bdb383", "metadata": {}, "source": [ "## Exploring the selected data" ] }, { "cell_type": "markdown", "id": "95261f89", "metadata": {}, "source": [ "We can examine the `filtered_obs_info` property (see the missions tutorial for a fuller explanation):" ] }, { "cell_type": "code", "execution_count": 6, "id": "0b3690ed", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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21409130.278333.2018913347TrueTrue2011-12-11 12:21:05.0000002011-12-11 12:47:05.0000000 days 00:26:002012-12-12AGNACIS-SNONETE_0076A
21559137.350000.036395703TrueTrue2004-12-28 09:24:24.9999992004-12-28 09:46:54.9999990 days 00:22:302005-12-29AGNACIS-SNONETE_006B0
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90 rows × 13 columns

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" ], "text/plain": [ " ra dec ObsID science_usable proprietary_usable \\\n", "1702 136.91667 -0.69994 17084 True True \n", "2148 144.43375 2.76094 20348 True True \n", "3684 140.94625 4.04850 23835 True True \n", "3885 137.31625 3.91186 23162 True True \n", "4605 137.86458 5.84778 14958 True True \n", "... ... ... ... ... ... \n", "20574 145.23833 3.40033 11451 True True \n", "20890 135.83750 4.96056 11448 True True \n", "21229 143.81125 3.59603 5705 True True \n", "21409 130.27833 3.20189 13347 True True \n", "21559 137.35000 0.03639 5703 True True \n", "\n", " start end duration \\\n", "1702 2015-01-10 00:32:52.999996 2015-01-10 16:07:22.999996 0 days 15:34:30 \n", "2148 2018-01-22 17:22:56.000004 2018-01-23 07:18:06.000004 0 days 13:55:10 \n", "3684 2022-05-16 13:22:45.999995 2022-05-17 00:28:45.999995 0 days 11:06:00 \n", "3885 2020-02-20 03:27:14.999999 2020-02-20 14:10:44.999999 0 days 10:43:30 \n", "4605 2012-12-30 10:27:00.999997 2012-12-30 19:54:40.999997 0 days 09:27:40 \n", "... ... ... ... \n", "20574 2010-01-04 14:03:12.999998 2010-01-04 14:39:12.999998 0 days 00:36:00 \n", "20890 2010-01-04 15:01:54.999999 2010-01-04 15:35:44.999999 0 days 00:33:50 \n", "21229 2005-03-07 04:46:24.000001 2005-03-07 05:16:24.000001 0 days 00:30:00 \n", "21409 2011-12-11 12:21:05.000000 2011-12-11 12:47:05.000000 0 days 00:26:00 \n", "21559 2004-12-28 09:24:24.999999 2004-12-28 09:46:54.999999 0 days 00:22:30 \n", "\n", " proprietary_end_date target_category instrument grating data_mode \n", "1702 2016-01-12 AGN ACIS-S NONE TE_006C8 \n", "2148 2019-01-23 NGS ACIS-S NONE TE_005C6 \n", "3684 2023-05-17 AGN ACIS-S NONE TE_007F2 \n", "3885 2021-02-20 AGN ACIS-S NONE TE_0065E \n", "4605 2013-12-31 AGN ACIS-S NONE TE_009C8 \n", "... ... ... ... ... ... \n", "20574 2011-01-05 AGN ACIS-S NONE TE_008FC \n", "20890 2011-01-05 AGN ACIS-S NONE TE_008FC \n", "21229 2006-03-08 AGN ACIS-S NONE TE_006B0 \n", "21409 2012-12-12 AGN ACIS-S NONE TE_0076A \n", "21559 2005-12-29 AGN ACIS-S NONE TE_006B0 \n", "\n", "[90 rows x 13 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ch.filtered_obs_info" ] }, { "cell_type": "markdown", "id": "8c5826ef", "metadata": {}, "source": [ "We can also use the tables that were returned from the search methods to match observations to specific objects:" ] }, { "cell_type": "code", "execution_count": 9, "id": "d8c3cbba", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " pos_ind pos_ra pos_dec ObsIDs \\\n", "0 45 129.3486854874346 1.40366630348438 0903700101 \n", "1 47 129.48802147587307 -1.7049345640737046 0920001601,0920002401 \n", "2 52 129.5393896036211 -2.0807673161720084 0920001601,0920002401 \n", "3 54 129.54957579257595 -1.9929684395330654 0920001601,0920002401 \n", "4 56 129.5732303438282 -2.284546510019436 0920001601,0920002401 \n", ".. ... ... ... ... \n", "110 469 141.93603300405417 4.941812984556808 0901870201,0901871201 \n", "111 491 142.47325179257808 0.4670133405850062 0802220601 \n", "112 521 143.75324046077222 0.9047856577567157 0920000801,0920002301 \n", "113 522 143.80457579850201 0.7993807870395315 0920000801,0920002301 \n", "114 523 143.8362603912554 0.5801534818576921 0920000801,0920002301 \n", "\n", " name \n", "0 eFEDS J083723.7+012413 \n", "1 eFEDS J083757.2-014217 \n", "2 eFEDS J083809.5-020450 \n", "3 eFEDS J083812.0-015934 \n", "4 eFEDS J083817.6-021704 \n", ".. ... \n", "110 eFEDS J092744.6+045631 \n", "111 eFEDS J092953.6+002801 \n", "112 eFEDS J093500.8+005417 \n", "113 eFEDS J093513.1+004757 \n", "114 eFEDS J093520.7+003448 \n", "\n", "[115 rows x 5 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xm_assoc['name'] = samp.loc[xm_assoc['pos_ind'].values.astype(int), 'ID'].values\n", "xm_assoc" ] }, { "cell_type": "code", "execution_count": 10, "id": "da32be0e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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187537144.434057056832162.76004554593990920348eFEDS J093744.2+024536
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192 rows × 5 columns

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" ], "text/plain": [ " pos_ind pos_ra pos_dec ObsIDs \\\n", "0 9 127.79401199149834 1.9378392793445995 19734 \n", "1 12 127.85804170868693 1.925897930055847 19734 \n", "2 16 127.97336088473476 1.4252588642845447 19734 \n", "3 18 128.11694107938197 -0.1156644071035969 23614 \n", "4 22 128.31476848695075 0.106508629638464 23614 \n", ".. ... ... ... ... \n", "187 537 144.43405705683216 2.760045545939909 20348 \n", "188 538 144.6271718639683 4.256395791583636 18099 \n", "189 539 144.909595641361 4.371716376951857 26035 \n", "190 540 145.0245934774472 3.224725957866028 22270,11451 \n", "191 541 145.03072779231115 3.965204479523988 26035,22270 \n", "\n", " name \n", "0 eFEDS J083110.6+015616 \n", "1 eFEDS J083125.9+015533 \n", "2 eFEDS J083153.6+012531 \n", "3 eFEDS J083228.1-000656 \n", "4 eFEDS J083315.6+000623 \n", ".. ... \n", "187 eFEDS J093744.2+024536 \n", "188 eFEDS J093830.5+041523 \n", "189 eFEDS J093938.3+042218 \n", "190 eFEDS J094005.9+031329 \n", "191 eFEDS J094007.3+035755 \n", "\n", "[192 rows x 5 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ch_assoc['name'] = samp.loc[ch_assoc['pos_ind'].values.astype(int), 'ID'].values\n", "ch_assoc" ] }, { "cell_type": "code", "execution_count": 11, "id": "cf0bc646", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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542 rows × 5 columns

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" ], "text/plain": [ " pos_ind pos_ra pos_dec ObsIDs \\\n", "0 0 126.61080512258118 -0.5747925179258094 300007 \n", "1 1 126.96547689559516 -0.4816435127681215 300007 \n", "2 2 127.03665115606483 -0.1677211251616217 300007 \n", "3 3 127.08556215338794 -0.1227575917789436 300007 \n", "4 4 127.1692087960137 -0.08355838266078222 300007 \n", ".. ... ... ... ... \n", "537 537 144.43406327204823 2.7600402745537114 300010 \n", "538 538 144.62717799852624 4.256390530427462 300010 \n", "539 539 144.90960176777745 4.3717111308643295 300010 \n", "540 540 145.0245996650002 3.2247207179507202 300010 \n", "541 541 145.03073393985625 3.9651992399385168 300010 \n", "\n", " name \n", "0 eFEDS J082626.6-003429 \n", "1 eFEDS J082751.8-002853 \n", "2 eFEDS J082808.8-001003 \n", "3 eFEDS J082820.6-000721 \n", "4 eFEDS J082840.6-000500 \n", ".. ... \n", "537 eFEDS J093744.2+024536 \n", "538 eFEDS J093830.5+041523 \n", "539 eFEDS J093938.3+042218 \n", "540 eFEDS J094005.9+031329 \n", "541 eFEDS J094007.3+035755 \n", "\n", "[542 rows x 5 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "er_assoc['name'] = samp.loc[er_assoc['pos_ind'].values.astype(int), 'ID'].values\n", "er_assoc" ] }, { "cell_type": "markdown", "id": "ee28c847", "metadata": {}, "source": [ "## Defining an archive" ] }, { "cell_type": "markdown", "id": "fb1215f9", "metadata": {}, "source": [ "The filtered missions can then be used to define an archive containing the selected data:" ] }, { "cell_type": "code", "execution_count": 12, "id": "2335dc34", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/dt237/code/DAXA/daxa/archive/base.py:133: UserWarning: The raw data for this mission have already been downloaded.\n", " mission.download()\n", "/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.\n", " mission.download()\n", "Downloading XMM-Newton Pointed data: 100%|██████████████████████████████████| 100/100 [00:18<00:00, 5.38it/s]\n", "Downloading Chandra data: 100%|███████████████████████████████████████████████| 90/90 [03:01<00:00, 2.02s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "-----------------------------------------------------\n", "Number of missions - 3\n", "Total number of observations - 194\n", "Beginning of earliest observation - 1999-11-02 17:31:43.000001\n", "End of latest observation - 2023-02-26 11:24:55.000001\n", "\n", "-- eROSITACalPV --\n", " Internal DAXA name - erosita_calpv\n", " Chosen instruments - TM1, TM2, TM3, TM4, TM5, TM6, TM7\n", " Number of observations - 4\n", " Fully Processed - False\n", "\n", "-- XMM-Newton Pointed --\n", " Internal DAXA name - xmm_pointed\n", " Chosen instruments - M1, M2, PN\n", " Number of observations - 100\n", " Fully Processed - False\n", "\n", "-- Chandra --\n", " Internal DAXA name - chandra\n", " Chosen instruments - ACIS-I, ACIS-S, HRC-I, HRC-S\n", " Number of observations - 90\n", " Fully Processed - False\n", "-----------------------------------------------------\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "arch = Archive('eFEDS_clusters', [er, xm, ch])\n", "arch.info()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.0" } }, "nbformat": 4, "nbformat_minor": 5 }