Observations within the XXL-North region

This case study demonstrates how we can use a DAXA filtering method to identify all observations whose coordinate falls within a rectangular region - we apply this to the task of selecting all the XMM and Chandra observations within the XXL-North (XXL is a project utilising the largest contiguous region observed by XMM, one in the north and one in the south).

The filtering method we demonstrate here could just as easily be applied to other wide regions of interest (the Lockman hole for example), and will work with any DAXA mission, not just XMM and Chandra.

Import Statements

from astropy.coordinates import SkyCoord
from astropy.units import hourangle

from daxa.mission import XMMPointed, Chandra

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.

Defining missions

The XXL project used XMM, so we knows there will be plenty of observations of the XXL region with XMM, but we will also search for any Chandra observations that fall within the same region:

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 are going to use a DAXA filtering method that allows for the selection of all observations whose central coordinate falls within a rectangle - for that search we need to define the lower-left and upper-right coordinates of the rectangular region. We define these coordinates with convention that RA increases from right-to-left (i.e. the upper-right RA is greater than the lower-left RA), but the search method can also handle rectangles that have RA increasing from left-to-right.

The coordinates we’ve created broadly cover the XXL-North region:

ll = SkyCoord("2h36m0s", -8., unit=(hourangle, 'deg'))
ur = SkyCoord("2h00m0s", -2., unit=(hourangle, 'deg'))

The filter_on_rect_region method is used, and we simply need to pass the corner coordinates we have already defined:

xm.filter_on_rect_region(ll, ur)
ch.filter_on_rect_region(ll, ur)

/Users/dt237/code/DAXA/daxa/mission/base.py:107: UserWarning: The passed corner coordinates are defined with RA increasing from right to left (upper-right RA is less than lower-left; we reversed this.
  any_ret = change_func(*args, **kwargs)

Examining the available observations

As expected, we have selected many XMM observations - and there is also a not-insignificant number of Chandra observations.

xm.filtered_obs_info

	ra	dec	ObsID	start	science_usable	duration	proprietary_end_date	revolution	proprietary_usable	end
234	35.666670	-3.833333	0037980101	2002-01-11 04:21:02	True	0 days 04:12:52	2003-02-13	383	True	2002-01-11 08:33:54
235	36.000000	-3.833333	0037980201	2002-01-11 14:20:11	True	0 days 03:52:55	2003-02-13	383	True	2002-01-11 18:13:06
236	36.333330	-3.833333	0037980301	2002-01-11 23:37:59	True	0 days 03:54:31	2003-02-08	383	True	2002-01-12 03:32:30
237	36.666660	-3.833333	0037980401	2002-01-26 02:24:12	True	0 days 04:23:51	2003-02-08	390	True	2002-01-26 06:48:03
238	37.000005	-3.833333	0037980501	2002-01-25 20:51:08	True	0 days 04:53:35	2003-02-08	390	True	2002-01-26 01:44:43
...	...	...	...	...	...	...	...	...	...	...
14759	31.332917	-2.551611	0870850101	2020-07-04 21:25:41	True	0 days 10:06:40	2021-07-27	3767	True	2020-07-05 07:32:21
14778	38.402083	-5.507694	0862670101	2020-07-10 03:03:35	True	1 days 04:03:20	2021-09-17	3770	True	2020-07-11 07:06:55
14846	38.402083	-5.507722	0862670201	2020-08-16 15:57:07	True	1 days 00:03:19	2021-09-17	3789	True	2020-08-17 16:00:26
14847	38.402083	-5.507722	0862670401	2020-08-16 13:39:53	True	0 days 02:17:14	2021-09-17	3789	True	2020-08-16 15:57:07
17409	36.930375	-6.091694	0920220301	2024-01-25 15:58:15	True	0 days 06:23:20	2025-02-08	4419	False	2024-01-25 22:21:35

422 rows × 10 columns

ch.filtered_obs_info

	ra	dec	ObsID	science_usable	proprietary_usable	start	end	duration	proprietary_end_date	target_category	instrument	grating	data_mode
77	31.18436	-5.09273	4129	True	True	2003-06-13 01:01:52.000003	2003-06-14 22:01:52.000003	1 days 21:00:00	2004-06-27	AGN	ACIS-I	NONE	TE_002AC
711	34.58875	-5.17417	12882	True	True	2010-09-27 04:39:01.999996	2010-09-28 04:25:31.999996	0 days 23:46:30	2011-09-29	GCL	ACIS-S	NONE	TE_0046E
886	35.04167	-6.04167	14972	True	True	2013-09-26 12:25:33.000004	2013-09-27 09:50:03.000004	0 days 21:24:30	2014-10-02	AGN	ACIS-S	NONE	TE_00AD8
891	35.02417	-5.14100	13374	True	True	2011-10-07 05:41:46.000003	2011-10-08 02:59:26.000003	0 days 21:17:40	2012-10-11	GCL	ACIS-I	NONE	TE_00AB4
913	36.68333	-4.69583	9368	True	True	2007-11-23 16:55:21.999996	2007-11-24 14:01:11.999996	0 days 21:05:50	2008-11-26	GCL	ACIS-S	NONE	TE_0046E
...	...	...	...	...	...	...	...	...	...	...	...	...	...
17733	33.34208	-6.09800	16574	True	True	2015-08-05 17:22:51.000001	2015-08-05 18:47:21.000001	0 days 01:24:30	2016-08-06	GCL	ACIS-S	NONE	TE_0046E
17885	31.54917	-6.19300	16575	True	True	2015-06-24 05:19:51.999997	2015-06-24 06:44:01.999997	0 days 01:24:10	2016-06-25	GCL	ACIS-S	NONE	TE_0046E
18900	37.90667	-7.48181	3030	True	True	2002-09-27 23:17:28.000003	2002-09-28 00:28:18.000003	0 days 01:10:50	2003-10-03	AGN	ACIS-S	NONE	TE_002A2
19149	33.62208	-5.29569	4767	True	True	2003-11-26 16:45:20.999998	2003-11-26 17:53:30.999998	0 days 01:08:10	2004-12-01	AGN	ACIS-S	NONE	TE_002A2
20831	35.27350	-4.68375	23741	True	True	2020-12-06 12:06:00.000003	2020-12-06 12:40:20.000003	0 days 00:34:20	2021-12-07	AGN	ACIS-S	NONE	TE_004A6

84 rows × 13 columns