Science User Case - Inspecting a Candidate List

Ogle et al. (2016) mined the NASA/IPAC Extragalactic Database (NED) to identify a new type of galaxy: Superluminous Spiral Galaxies.

Here's the paper: https://ui.adsabs.harvard.edu//#abs/2016ApJ...817..109O/abstract

Table 1 lists the positions of these Super Spirals. Based on those positions, let's create multiwavelength cutouts for each super spiral to see what is unique about this new class of objects.

1. Import the Python modules we'll be using.

# Suppress unimportant warnings.
import warnings
warnings.filterwarnings("ignore", module="astropy.io.votable.*")
warnings.filterwarnings("ignore", module="pyvo.utils.xml.*")
warnings.filterwarnings('ignore', '.*RADECSYS=*', append=True)

import matplotlib.pyplot as plt
import numpy as np

# For downloading files
from astropy.utils.data import download_file

from astropy.coordinates import SkyCoord
from astropy.io import fits
from astropy.nddata import Cutout2D
import astropy.visualization as vis
from astropy.wcs import WCS
from astroquery.ned import Ned

import pyvo as vo

The next cell prepares the notebook to display our visualizations.

%matplotlib inline  

2. Search NED for objects in this paper.

Insert a Code Cell below by clicking on the "Insert" Menu and choosing "Insert Cell Below". Then consult QuickReference.md to figure out how to use astroquery to search NED for all objects in a paper, based on the refcode of the paper. Inspect the resulting astropy table.

objects_in_paper = Ned.query_refcode('2016ApJ...817..109O')
objects_in_paper.show_in_notebook()

Table length=62

idx	No.	Object Name	RA	DEC	Type	Velocity	Redshift	Redshift Flag	Magnitude and Filter	Separation	References	Notes	Photometry Points	Positions	Redshift Points	Diameter Points	Associations
			degrees	degrees		km / s				arcmin
0	1	WISEA J001550.14-100242.3	3.95892	-10.04511	G	52788.0	0.176083		17.5g	--	13	0	63	8	7	10	0
1	2	WISEA J003807.80-010936.7	9.53252	-1.16024	G	62565.0	0.208695		18.0g	--	14	0	71	12	9	10	0
2	3	WISEA J040422.92-054134.8	61.09548	-5.69303	G	75138.0	0.250635		18.6g	--	6	0	61	10	7	8	0
3	4	WISEA J073806.16+282359.6	114.52568	28.39995	G	69255.0	0.23101		18.0g	--	9	0	66	7	4	10	0
4	5	2MASX J07550424+1353261	118.76776	13.89065	G	66754.0	0.222669		18.5g	--	8	0	38	7	6	6	0
5	6	WISEA J082655.11+181147.7	126.72969	18.19665	G	79608.0	0.265543		18.4g	--	6	0	46	7	6	6	0
6	7	2MASX J08542169+0449308	133.59033	4.82511	G	47018.0	0.156835		16.7g	--	16	0	58	8	6	10	0
7	8	WHL J090944.8+222607	137.4367	22.43539	GClstr	90837.0	0.303	PHOT		--	7	0	0	4	5	0	0
8	9	2MASX J09094480+2226078	137.4367	22.43538	G	85557.0	0.285386		19.0g	--	8	0	30	5	6	6	0
9	10	2MASX J09260805+2405242	141.53357	24.09004	G	66689.0	0.222451		17.8g	--	14	0	32	7	9	6	0
10	11	WHL J092608.1+240524	141.53357	24.09004	GClstr	53363.0	0.178	PHOT		--	6	0	0	4	5	0	0
11	12	WISEA J093347.77+211436.7	143.44901	21.24346	G	51626.0	0.172206		16.9g	--	42	0	51	17	13	6	1
12	13	WISEA J093622.14+390628.9	144.09222	39.10801	G	84847.0	0.283019		18.5g	--	5	0	66	7	3	10	0
13	14	MSPM 05544	146.2235	22.8851	GClstr	26738.0	0.08919			--	3	0	0	1	1	0	0
14	15	CGCG 122-067	146.22351	22.88512	G	26684.0	0.089008		15.3g	--	27	0	48	9	9	8	0
15	16	WISEA J094700.08+254045.8	146.75035	25.67938	G	32710.0	0.10911		15.8g	--	19	0	46	7	7	6	0
16	17	SDSS J095727.02+083501.7	149.36259	8.58383	G	76917.0	0.256568		18.3g	--	7	0	39	5	3	8	0
17	18	WISEA J100356.88+382902.1	150.98703	38.48391	G	77544.0	0.258658		17.8g	--	7	0	57	7	4	8	0
18	19	WISEA J100416.04+295844.1	151.06683	29.97899	G	89469.0	0.298435		18.7g	--	6	0	66	7	4	10	0
19	20	WISEA J100956.35+261132.0	152.48475	26.19223	G	72232.0	0.240939		18.1g	--	11	0	46	7	4	6	0
20	21	GMBCG J152.52936+32.89139	152.52936	32.89139	GClstr	95634.0	0.319	PHOT		--	3	0	0	1	1	0	0
21	22	WISEA J101007.05+325329.0	152.52936	32.89141	G	86914.0	0.289913		18.7g	--	9	0	66	7	4	10	0
22	23	2MASS J10160396+3037481	154.01655	30.62998	G	75553.0	0.252016		18.7g	--	6	0	49	7	4	8	0
23	24	WISEA J102154.85+072415.5	155.47856	7.40432	G	87111.0	0.29057		18.4g	--	5	0	55	7	3	8	0
24	25	WISEA J103015.74-010607.0	157.56561	-1.10198	G	84617.0	0.282251		18.3g	--	10	0	62	9	6	10	0
25	26	2MASX J10304263+0418219	157.67753	4.30602	G	48300.0	0.16111		16.8g	--	21	0	59	8	6	10	0
26	27	SDSS CE J160.241898-01.069106	160.235	-1.06611	GClstr	76153.0	0.254019	EST		--	6	0	0	3	5	0	0
27	28	2MASX J10405643-0103584	160.23509	-1.06632	G	75039.0	0.250303		18.2g	--	15	0	51	9	9	10	0
28	29	WISEA J104724.97+230917.4	161.85435	23.15474	G	54754.0	0.182639		18.46	--	17	0	46	8	6	6	0
29	30	WISEA J111917.41+141946.5	169.8226	14.3296	G	43104.0	0.143781		16.6g	--	18	0	66	8	6	10	0
30	31	2MASS J11292875+0255498	172.36978	2.93055	G	71860.0	0.239698		17.5g	--	8	0	49	7	5	8	0
31	32	WISEA J113800.86+521303.8	174.50367	52.21775	G	88744.0	0.296018		19.0g	--	8	0	57	7	3	8	0
32	33	WISEA J114100.04+384807.4	175.25022	38.80207	G	80215.0	0.267567		18.5g	--	6	0	66	8	6	10	0
33	34	WISEA J115052.96+460448.1	177.72076	46.08006	G	86801.0	0.289538		18.5g	--	10	0	53	9	7	8	0
34	35	WISEA J115356.21+492355.4	178.48416	49.39881	G	50033.0	0.166892		17.1g	--	16	0	66	9	6	10	0
35	36	2MASX J11593546+1257080	179.89761	12.95205	G	79017.0	0.263572		18.3g	--	6	0	54	6	4	10	0
36	37	WISEA J120053.92+480007.8	180.22477	48.00213	G	83527.0	0.278617		18.4g	--	7	0	66	7	5	10	0
37	38	GMBCG J180.22479+48.00211	180.22479	48.00211	GClstr	75548.0	0.252	PHOT		--	5	0	0	2	3	0	0
38	39	WISEA J121644.33+122450.5	184.18477	12.41404	G	77090.0	0.257144		18.3g	--	5	0	55	7	3	8	0
39	40	WISEA J122100.50+482729.1	185.25209	48.45807	G	89874.0	0.299787		18.7g	--	8	0	61	8	3	8	0
40	41	WISEA J123215.19+102119.1	188.06311	10.35535	G	49742.0	0.16592		17.2g	--	17	0	66	9	6	10	0
41	42	WISEA J123431.08+515629.7	188.62952	51.94146	G	88733.0	0.295982		18.1g	--	28	0	63	10	11	10	0
42	43	WISEA J123746.63+481227.5	189.44448	48.20767	G	81721.0	0.272592		18.2g	--	7	0	64	8	4	10	0
43	44	WISEA J131039.32+223502.8	197.66385	22.58407	G	69341.0	0.231295		18.1g	--	10	0	48	8	5	6	1
44	45	WISEA J132757.52+334529.3	201.98969	33.75806	G	74633.0	0.248949		17.7g	--	14	0	88	12	4	10	0
45	46	WISEA J134228.33+115734.5	205.61806	11.95954	G	83682.0	0.279133		19.0g	--	7	0	66	7	4	10	0
46	47	WISEA J134355.49+244048.1	205.98124	24.68001	G	41149.0	0.137258		17.81	--	17	0	46	8	6	6	0
47	48	SDSSCGB 16827	206.99833	32.44039	GGroup	--	--		18.44	--	3	0	0	1	0	0	0
48	49	2MASX J13475962+3227100	206.99841	32.45286	G	66888.0	0.223113		18.1g	--	10	0	54	6	4	10	0
49	50	WISEA J135546.08+025456.0	208.942	2.91553	G	71617.0	0.23889		18.7g	--	10	0	51	7	5	8	0
50	51	WISEA J140138.37+263527.6	210.40991	26.591	G	85152.0	0.284036		18.7g	--	7	0	30	5	4	4	0
51	52	2MASS J14175497+2704341	214.47905	27.07624	G	47238.0	0.157569		16.9g	--	16	0	25	6	6	4	0
52	53	WISEA J143447.88+020228.8	218.69942	2.04129	G	83976.0	0.280115		18.6g	--	8	0	62	9	5	8	0
53	54	WISEA J144728.35+590831.9	221.86807	59.14209	G	73609.0	0.245534		18.3g	--	14	0	69	11	8	10	0
54	55	WISEA J153619.01+452247.7	234.07909	45.37968	G	70813.0	0.236206		18.1g	--	8	0	57	7	3	8	0
55	56	WISEA J154307.78+193751.7	235.78241	19.63104	G	68625.0	0.228909		17.0g	--	34	1	50	16	14	6	0
56	57	WISEA J154950.90+234444.0	237.46216	23.7456	G	78611.0	0.262217		18.5g	--	6	0	31	5	4	4	0
57	58	GMBCG J240.41924+27.30444	240.41924	27.30444	GClstr	57860.0	0.193	PHOT		--	3	0	0	1	1	0	0
58	59	2MASX J16014061+2718161	240.41924	27.30443	G	49332.0	0.164554		16.7g	--	19	0	54	7	7	10	1
59	60	WISEA J163945.96+460905.8	249.94148	46.1517	G	74151.0	0.247342		18.3g	--	10	0	70	8	4	10	0
60	61	WISEA J173406.17+602919.1	263.52577	60.48859	G	82685.0	0.275807		18.5g	--	8	0	66	7	4	10	0
61	62	SDSSCGB 59704	263.53292	60.48197	GGroup	82743.0	0.276		17.20	--	3	0	0	1	1	0	0

3. Filter the NED results.

The results from NED will include galaxies, but also other kinds of objects. Print the 'Type' column to see the full range of classifications. Next, print the 'Type' of just the first source in the table, in order to determine its data type (since Python 3 distinguishes between strings and byte strings). Finally, use the data type information to filter the results so that we only keep the galaxies in the list.

objects_in_paper['Type']

<MaskedColumn name='Type' dtype='object' description="NED's Preferred Object Type: G,GPair,GTrpl,GGroup,GClstr,QSO,AbLS,RadioS,IrS,EmLS,UvES,XrayS,SN" length=62>

G

G

G

G

G

G

G

GClstr

G

G

GClstr

G

...

G

G

G

G

G

G

G

GClstr

G

G

G

GGroup

# Let's see whether we are dealing with strings or byte strings
objects_in_paper[0]['Type']

'G'

# Recast the Type column as strings, to avoid any issues with byte strings
galaxies = objects_in_paper[np.array(objects_in_paper['Type'], dtype='str') == 'G']

galaxies.show_in_notebook()

Table length=53

idx	No.	Object Name	RA	DEC	Type	Velocity	Redshift	Redshift Flag	Magnitude and Filter	Separation	References	Notes	Photometry Points	Positions	Redshift Points	Diameter Points	Associations
			degrees	degrees		km / s				arcmin
0	1	WISEA J001550.14-100242.3	3.95892	-10.04511	G	52788.0	0.176083		17.5g	--	13	0	63	8	7	10	0
1	2	WISEA J003807.80-010936.7	9.53252	-1.16024	G	62565.0	0.208695		18.0g	--	14	0	71	12	9	10	0
2	3	WISEA J040422.92-054134.8	61.09548	-5.69303	G	75138.0	0.250635		18.6g	--	6	0	61	10	7	8	0
3	4	WISEA J073806.16+282359.6	114.52568	28.39995	G	69255.0	0.23101		18.0g	--	9	0	66	7	4	10	0
4	5	2MASX J07550424+1353261	118.76776	13.89065	G	66754.0	0.222669		18.5g	--	8	0	38	7	6	6	0
5	6	WISEA J082655.11+181147.7	126.72969	18.19665	G	79608.0	0.265543		18.4g	--	6	0	46	7	6	6	0
6	7	2MASX J08542169+0449308	133.59033	4.82511	G	47018.0	0.156835		16.7g	--	16	0	58	8	6	10	0
7	9	2MASX J09094480+2226078	137.4367	22.43538	G	85557.0	0.285386		19.0g	--	8	0	30	5	6	6	0
8	10	2MASX J09260805+2405242	141.53357	24.09004	G	66689.0	0.222451		17.8g	--	14	0	32	7	9	6	0
9	12	WISEA J093347.77+211436.7	143.44901	21.24346	G	51626.0	0.172206		16.9g	--	42	0	51	17	13	6	1
10	13	WISEA J093622.14+390628.9	144.09222	39.10801	G	84847.0	0.283019		18.5g	--	5	0	66	7	3	10	0
11	15	CGCG 122-067	146.22351	22.88512	G	26684.0	0.089008		15.3g	--	27	0	48	9	9	8	0
12	16	WISEA J094700.08+254045.8	146.75035	25.67938	G	32710.0	0.10911		15.8g	--	19	0	46	7	7	6	0
13	17	SDSS J095727.02+083501.7	149.36259	8.58383	G	76917.0	0.256568		18.3g	--	7	0	39	5	3	8	0
14	18	WISEA J100356.88+382902.1	150.98703	38.48391	G	77544.0	0.258658		17.8g	--	7	0	57	7	4	8	0
15	19	WISEA J100416.04+295844.1	151.06683	29.97899	G	89469.0	0.298435		18.7g	--	6	0	66	7	4	10	0
16	20	WISEA J100956.35+261132.0	152.48475	26.19223	G	72232.0	0.240939		18.1g	--	11	0	46	7	4	6	0
17	22	WISEA J101007.05+325329.0	152.52936	32.89141	G	86914.0	0.289913		18.7g	--	9	0	66	7	4	10	0
18	23	2MASS J10160396+3037481	154.01655	30.62998	G	75553.0	0.252016		18.7g	--	6	0	49	7	4	8	0
19	24	WISEA J102154.85+072415.5	155.47856	7.40432	G	87111.0	0.29057		18.4g	--	5	0	55	7	3	8	0
20	25	WISEA J103015.74-010607.0	157.56561	-1.10198	G	84617.0	0.282251		18.3g	--	10	0	62	9	6	10	0
21	26	2MASX J10304263+0418219	157.67753	4.30602	G	48300.0	0.16111		16.8g	--	21	0	59	8	6	10	0
22	28	2MASX J10405643-0103584	160.23509	-1.06632	G	75039.0	0.250303		18.2g	--	15	0	51	9	9	10	0
23	29	WISEA J104724.97+230917.4	161.85435	23.15474	G	54754.0	0.182639		18.46	--	17	0	46	8	6	6	0
24	30	WISEA J111917.41+141946.5	169.8226	14.3296	G	43104.0	0.143781		16.6g	--	18	0	66	8	6	10	0
25	31	2MASS J11292875+0255498	172.36978	2.93055	G	71860.0	0.239698		17.5g	--	8	0	49	7	5	8	0
26	32	WISEA J113800.86+521303.8	174.50367	52.21775	G	88744.0	0.296018		19.0g	--	8	0	57	7	3	8	0
27	33	WISEA J114100.04+384807.4	175.25022	38.80207	G	80215.0	0.267567		18.5g	--	6	0	66	8	6	10	0
28	34	WISEA J115052.96+460448.1	177.72076	46.08006	G	86801.0	0.289538		18.5g	--	10	0	53	9	7	8	0
29	35	WISEA J115356.21+492355.4	178.48416	49.39881	G	50033.0	0.166892		17.1g	--	16	0	66	9	6	10	0
30	36	2MASX J11593546+1257080	179.89761	12.95205	G	79017.0	0.263572		18.3g	--	6	0	54	6	4	10	0
31	37	WISEA J120053.92+480007.8	180.22477	48.00213	G	83527.0	0.278617		18.4g	--	7	0	66	7	5	10	0
32	39	WISEA J121644.33+122450.5	184.18477	12.41404	G	77090.0	0.257144		18.3g	--	5	0	55	7	3	8	0
33	40	WISEA J122100.50+482729.1	185.25209	48.45807	G	89874.0	0.299787		18.7g	--	8	0	61	8	3	8	0
34	41	WISEA J123215.19+102119.1	188.06311	10.35535	G	49742.0	0.16592		17.2g	--	17	0	66	9	6	10	0
35	42	WISEA J123431.08+515629.7	188.62952	51.94146	G	88733.0	0.295982		18.1g	--	28	0	63	10	11	10	0
36	43	WISEA J123746.63+481227.5	189.44448	48.20767	G	81721.0	0.272592		18.2g	--	7	0	64	8	4	10	0
37	44	WISEA J131039.32+223502.8	197.66385	22.58407	G	69341.0	0.231295		18.1g	--	10	0	48	8	5	6	1
38	45	WISEA J132757.52+334529.3	201.98969	33.75806	G	74633.0	0.248949		17.7g	--	14	0	88	12	4	10	0
39	46	WISEA J134228.33+115734.5	205.61806	11.95954	G	83682.0	0.279133		19.0g	--	7	0	66	7	4	10	0
40	47	WISEA J134355.49+244048.1	205.98124	24.68001	G	41149.0	0.137258		17.81	--	17	0	46	8	6	6	0
41	49	2MASX J13475962+3227100	206.99841	32.45286	G	66888.0	0.223113		18.1g	--	10	0	54	6	4	10	0
42	50	WISEA J135546.08+025456.0	208.942	2.91553	G	71617.0	0.23889		18.7g	--	10	0	51	7	5	8	0
43	51	WISEA J140138.37+263527.6	210.40991	26.591	G	85152.0	0.284036		18.7g	--	7	0	30	5	4	4	0
44	52	2MASS J14175497+2704341	214.47905	27.07624	G	47238.0	0.157569		16.9g	--	16	0	25	6	6	4	0
45	53	WISEA J143447.88+020228.8	218.69942	2.04129	G	83976.0	0.280115		18.6g	--	8	0	62	9	5	8	0
46	54	WISEA J144728.35+590831.9	221.86807	59.14209	G	73609.0	0.245534		18.3g	--	14	0	69	11	8	10	0
47	55	WISEA J153619.01+452247.7	234.07909	45.37968	G	70813.0	0.236206		18.1g	--	8	0	57	7	3	8	0
48	56	WISEA J154307.78+193751.7	235.78241	19.63104	G	68625.0	0.228909		17.0g	--	34	1	50	16	14	6	0
49	57	WISEA J154950.90+234444.0	237.46216	23.7456	G	78611.0	0.262217		18.5g	--	6	0	31	5	4	4	0
50	59	2MASX J16014061+2718161	240.41924	27.30443	G	49332.0	0.164554		16.7g	--	19	0	54	7	7	10	1
51	60	WISEA J163945.96+460905.8	249.94148	46.1517	G	74151.0	0.247342		18.3g	--	10	0	70	8	4	10	0
52	61	WISEA J173406.17+602919.1	263.52577	60.48859	G	82685.0	0.275807		18.5g	--	8	0	66	7	4	10	0

4. Search the NAVO Registry for image resources.

The paper selected super spirals using WISE, SDSS, and GALEX images. Search the NAVO registry for all image resources, using the 'service_type' search parameter. How many image resources are currently available?

image_services = vo.regsearch(servicetype='image')

print(f'{len(image_services)} result(s) found.')

image_services.to_table()['ivoid', 'short_name', 'res_title']

328 result(s) found.

Table length=328

ivoid	short_name	res_title
object	object	object
ivo://3crsnapshots/sia	3CRSnap.sia	3CRSnapshots Simple Image Access Service
ivo://3crsnapshots/sia	3CRSnap.sia	3CRSnapshots Simple Image Access Service
ivo://archive.stsci.edu/borg	BoRG	Brightest of Reionizing Galaxies (BoRG)
ivo://archive.stsci.edu/ghosts	GHOSTS	Galaxy Halos, Outer disks, Substructure, Thick disks and Star clusters (GHOSTS)
ivo://archive.stsci.edu/sia/galex	GALEX	Galaxy Evolution Explorer
ivo://archive.stsci.edu/siap/hla	HLA	Hubble Legacy Archive
ivo://arvo/siap	ArVO SIAP	Armenian Virtual Observatory SIAP
ivo://astron.nl/apertif_dr1/q/apertif_dr1_continuum_images	Continuum images	M) Apertif DR1 - Continuum images
ivo://astron.nl/apertif_dr1/q/apertif_dr1_polarization_cubes	Pol. cubes	M) Apertif DR1 - Polarization images and cubes
ivo://astron.nl/apertif_dr1/q/apertif_dr1_spectral_cubes	Spectral cubes	M) Apertif DR1 - HI spectral cubes
...	...	...
ivo://wfau.roe.ac.uk/ukidssdr3-siap	UKIDSS DR3 SIAP	UKIDSS DR3 SIAP Service
ivo://wfau.roe.ac.uk/ukidssdr4-siap	UKIDSS DR4 SIAP	UKIDSS DR4 SIAP Service
ivo://wfau.roe.ac.uk/ukidssdr5-siap	UKIDSS DR5 SIAP	UKIDSS DR5 SIAP Service
ivo://wfau.roe.ac.uk/ukidssdr6-siap	UKIDSS DR6 SIAP	UKIDSS DR6 SIAP Service
ivo://wfau.roe.ac.uk/ukidssdr7-siap	UKIDSS DR7 SIAP	UKIDSS DR7 SIAP Service
ivo://wfau.roe.ac.uk/ukidssdr8-siap	UKIDSS DR8 SIAP	UKIDSS DR8 SIAP Service
ivo://wfau.roe.ac.uk/ukidssdr9-siap	UKIDSS DR9 SIAP	UKIDSS DR9 SIAP Service
ivo://wfau.roe.ac.uk/vhsdr1-siap	VHS DR1 SIAP	VHS DR1 SIAP Service
ivo://wfau.roe.ac.uk/vista-siap	VISTA SIAP	VISTA SIAP Service
ivo://xcatdb/3xmmdr7/epicsia	3XMM-DR7-SIA	Epic Image SIAP of the SSC Interface for the 3XMM-DR7 Catalogue

5. Search the NAVO Registry for image resources that will allow you to search for AllWISE images.

There are hundreds of image resources...too many to quickly read through. Try adding the 'keywords' search parameter to your registry search, and find the image resource you would need to search the AllWISE images. Remember from the Known Issues that 'keywords' must be a list.

allwise_image_services = vo.regsearch(servicetype='image', keywords=['allwise'])

print(f'{len(allwise_image_services)} result(s) found.')

allwise_image_services.to_table()['ivoid', 'short_name', 'res_title']

1 result(s) found.

Table length=1

ivoid	short_name	res_title
object	object	object
ivo://irsa.ipac/wise/images/allwise/l3a	AllWISE L3a	AllWISE Atlas (L3a) Coadd Images

6. Choose the AllWISE image service that you are interested in.

allwise_image_service = allwise_image_services[0]
allwise_image_service.service

<pyvo.dal.sia.SIAService at 0x7fe819eac450>

7. Choose one of the galaxies in the NED list.

ra = galaxies['RA'][0]
dec = galaxies['DEC'][0]
pos = SkyCoord(ra, dec, unit = 'deg')

ra,dec

(3.95892, -10.04511)

8. Search for a list of AllWISE images that cover this galaxy.

How many images are returned? Which are you most interested in?

allwise_image_table = allwise_image_service.search(pos=pos, size=0)
allwise_image_table

<Table length=4>
      sia_title        ...    coadd_id  
                       ...              
        object         ...     object   
---------------------- ... -------------
W1 Coadd 0046m107_ac51 ... 0046m107_ac51
W3 Coadd 0046m107_ac51 ... 0046m107_ac51
W4 Coadd 0046m107_ac51 ... 0046m107_ac51
W2 Coadd 0046m107_ac51 ... 0046m107_ac51

9. Use the .to_table() method to view the results as an Astropy table.

allwise_images = allwise_image_table.to_table()
allwise_images

Table length=4

sia_title	sia_url	sia_naxes	sia_fmt	sia_ra	sia_dec	sia_naxis	sia_crpix	sia_crval	sia_proj	sia_scale	sia_cd	sia_bp_id	sia_bp_ref	sia_bp_hi	sia_bp_lo	magzp	magzpunc	unc_url	cov_url	coadd_id
				deg	deg		pix	deg		deg / pix	deg / pix
object	object	int32	object	float64	float64	object	object	object	object	object	object	object	float64	float64	float64	float64	float64	object	object	object
W1 Coadd 0046m107_ac51	https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w1-int-3.fits	2	image/fits	4.61538	-10.601111	[4095 4095]	[2048.0 2048.0]	[4.61538 -10.601111]	SIN	[-0.0003819444391411 0.0003819444391411]	[-0.0003819444391411 -0.0 -0.0 0.0003819444391411]	W1	3.35e-06	3.78e-06	3.13e-06	20.5	0.006	https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w1-unc-3.fits.gz	https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w1-cov-3.fits.gz	0046m107_ac51
W3 Coadd 0046m107_ac51	https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w3-int-3.fits	2	image/fits	4.61538	-10.601111	[4095 4095]	[2048.0 2048.0]	[4.61538 -10.601111]	SIN	[-0.0003819444391411 0.0003819444391411]	[-0.0003819444391411 -0.0 -0.0 0.0003819444391411]	W3	1.156e-05	1.627e-05	7.6e-06	18.0	0.012	https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w3-unc-3.fits.gz	https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w3-cov-3.fits.gz	0046m107_ac51
W4 Coadd 0046m107_ac51	https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w4-int-3.fits	2	image/fits	4.61538	-10.601111	[4095 4095]	[2048.0 2048.0]	[4.61538 -10.601111]	SIN	[-0.0003819444391411 0.0003819444391411]	[-0.0003819444391411 -0.0 -0.0 0.0003819444391411]	W4	2.209e-05	2.336e-05	1.984e-05	13.0	0.012	https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w4-unc-3.fits.gz	https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w4-cov-3.fits.gz	0046m107_ac51
W2 Coadd 0046m107_ac51	https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w2-int-3.fits	2	image/fits	4.61538	-10.601111	[4095 4095]	[2048.0 2048.0]	[4.61538 -10.601111]	SIN	[-0.0003819444391411 0.0003819444391411]	[-0.0003819444391411 -0.0 -0.0 0.0003819444391411]	W2	4.6e-06	5.19e-06	4.02e-06	19.5	0.007	https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w2-unc-3.fits.gz	https://irsa.ipac.caltech.edu/ibe/data/wise/allwise/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w2-cov-3.fits.gz	0046m107_ac51

10. From the result in 8., select the first record for an image taken in WISE band W1 (3.6 micron)

Hints:

• Loop over records and test on the .bandpass_id attribute of each record
• Print the .title and .bandpass_id of the record you find, to verify it is the right one.

for allwise_image_record in allwise_image_table:
    if 'W1' in allwise_image_record.bandpass_id:
        break
print(allwise_image_record.title, allwise_image_record.bandpass_id)

W1 Coadd 0046m107_ac51 W1

11. Visualize this AllWISE image.

## If you only run this once, you can do it in memory in one line:
##  This fetches the FITS as an astropy.io.fits object in memory
#allwise_w1_image = allwise_image_record.getdataobj()
## But if you might run this notebook repeatedly with limited bandwidth, 
##  download it once and cache it.  
file_name = download_file(allwise_image_record.getdataurl(), cache=True)  
allwise_w1_image = fits.open(file_name)

fig = plt.figure()

wcs = WCS(allwise_w1_image[0].header)
ax = fig.add_subplot(1, 1, 1, projection=wcs)
ax.imshow(allwise_w1_image[0].data, cmap='gray_r', origin='lower', vmax = 10)
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')

<matplotlib.collections.PathCollection at 0x7fe819f93d90>

12. Plot a cutout of the AllWISE image, centered on your position.

Try a 60 arcsecond cutout.

size = 60
cutout = Cutout2D(allwise_w1_image[0].data, pos, (size, size), wcs=wcs)
wcs = cutout.wcs

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1, projection=wcs)
ax.imshow(cutout.data, cmap='gray_r', origin='lower', vmax = 10)
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')

<matplotlib.collections.PathCollection at 0x7fe8200d9b90>

13. Try visualizing a cutout of a GALEX image that covers your position.

Repeat steps 4, 5, 6, 8 through 12 for GALEX.

galex_image_services = vo.regsearch(keywords=['galex'], servicetype='image')
print(f'{len(galex_image_services)} result(s) found.')
galex_image_services.to_table()['ivoid', 'short_name', 'res_title']

5 result(s) found.

Table length=5

ivoid	short_name	res_title
object	object	object
ivo://archive.stsci.edu/sia/galex	GALEX	Galaxy Evolution Explorer
ivo://irsa.ipac/spitzer/images/lvl	LVL	Spitzer Local Volume Legacy Survey
ivo://irsa.ipac/wise/images/z0mgs	z0MGS	The z=0 Multiwavelength Galaxy Synthesis
ivo://mast.stsci/siap/galex_atlas	GALEX_Atlas	GALEX Atlas of Nearby Galaxies
ivo://nasa.heasarc/skyview/galex	GALEX	Galaxy Explorer All Sky Survey: Near UV

galex_image_service = galex_image_services[0]

galex_image_table = galex_image_service.search(pos=pos, size=0.0, intersect='covers')

for i in range(len(galex_image_table)):
    if (('image/fits' in galex_image_table[i].format) and
        (galex_image_table['enrValue'][i]==2.35e-07)):
        break
galex_image_record = galex_image_table[i]
print(galex_image_record.title, galex_image_record.bandpass_id)

ais_270_sg14-nd-int.fits.gz UV

## See above regarding two ways to do this: 
#galex_nuv_image = fits.open(galex_image_record.getdataurl())
file_name = download_file(galex_image_record.getdataurl(), cache=True)  
galex_nuv_image=fits.open(file_name)

image_data = galex_nuv_image[0].data
print('Min:', np.min(image_data))
print('Max:', np.max(image_data))
print('Mean:', np.mean(image_data))
print('Stdev:', np.std(image_data))

Min: 0.0
Max: 7.1870303
Mean: 0.0014934327
Stdev: 0.012639926

fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=WCS(galex_nuv_image[0].header))
ax.imshow(galex_nuv_image[0].data, cmap='gray_r', origin='lower', vmin=0.0, vmax=0.01)
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')   

<matplotlib.collections.PathCollection at 0x7fe82018ba90>

cutout = Cutout2D(galex_nuv_image[0].data, pos, size, wcs=WCS(galex_nuv_image[0].header))

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1, projection=cutout.wcs)
ax.imshow(cutout.data, cmap='gray_r', origin='lower', vmin = 0.0, vmax = 0.01)
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')

<matplotlib.collections.PathCollection at 0x7fe81a28b690>

14. Try visualizing a cutout of an SDSS image that covers your position.

Hints:

• Search the registry using `keywords=['sloan']
• Find the service with a short_name of 'SDSS SIAP'
• From Known Issues, recall that an empty string must be specified to the format parameter dues to a bug in the service.
• After obtaining your search results, select r-band images using the .title attribute of the records that are returned, since .bandpass_id is not populated.

sdss_image_services = vo.regsearch(keywords=['sloan'], servicetype='image')
sdss_image_services.to_table()['ivoid', 'short_name', 'res_title', 'source_value']

Table length=22

ivoid	short_name	res_title	source_value
object	object	object	object
ivo://irsa.ipac/spitzer/images/shela	SHELA	Spitzer/HETDEX Exploratory Large-Area Survey
ivo://mast.stsci/siap/al218	VLA.AL218	VLA-A Array AL218 Texas Survey Source Snapshots (AL218)
ivo://mast.stsci/siap/vla-first	VLA-FIRST	VLA Faint Images of the Radio Sky at Twenty Centimeters (FIRST)
ivo://nasa.heasarc/skyview/sdss	SDSS	Sloan Digital Sky Survey g-band
ivo://nasa.heasarc/skyview/sdssdr7	SDSSDR7	Sloan Digital Sky Survey g-band DR7
ivo://nasa.heasarc/skyview/stripe82vla	Stripe82VLA	VLA Survey of SDSS Stripe 82
ivo://org.gavo.dc/bgds/q/sia	bgds sia	Bochum Galactic Disk Survey (BGDS) SIAP service	2015AN....336..590H
ivo://sdss.jhu/services/siap-images	SDSS SIAP	Sloan Digital Sky Survey Images (Latest Release)
ivo://sdss.jhu/services/siapdr1-images	SDSSDR1	Sloan Digital Sky Survey DR1 - Images
ivo://sdss.jhu/services/siapdr2-images	SDSSDR2	Sloan Digital Sky Survey DR2 - Images
ivo://sdss.jhu/services/siapdr3-color	SDSSDR3-Color	Sloan Digital Sky Survey DR3
ivo://sdss.jhu/services/siapdr3-g	SDSSDR3-G	Sloan Digital Sky Survey DR3 - Filter g
ivo://sdss.jhu/services/siapdr3-i	SDSSDR3-I	Sloan Digital Sky Survey DR3 - Filter i
ivo://sdss.jhu/services/siapdr3-r	SDSSDR3-R	Sloan Digital Sky Survey DR3 - Filter r
ivo://sdss.jhu/services/siapdr3-u	SDSSDR3-U	Sloan Digital Sky Survey DR3 - Filter u
ivo://sdss.jhu/services/siapdr3-z	SDSSDR3-Z	Sloan Digital Sky Survey DR3 - Filter z
ivo://sdss.jhu/services/siapdr4-color	SDSSDR4-Color	Sloan Digital Sky Survey DR4
ivo://sdss.jhu/services/siapdr4-images	SDSSDR4	Sloan Digital Sky Survey DR4 - Images
ivo://sdss.jhu/services/siapdr5-images	SDSSDR5	Sloan Digital Sky Survey DR5 - Images
ivo://sdss.jhu/services/siapdr7-images	SDSSDR7	Sloan Digital Sky Survey DR7 - Images
ivo://sdss.jhu/services/siapdr8-images	SDSSDR8	Sloan Digital Sky Survey DR8 - Images
ivo://sdss.jhu/services/siapdr9-images	SDSSDR9	Sloan Digital Sky Survey DR9 - Images

#  Use list comprehension to check each service's short_name attribute.
#   Given the above, we know the first match is the right one.  
sdss_image_service = [s for s in sdss_image_services if 'SDSS SIAP' in s.short_name ][0]
sdss_image_service.short_name

'SDSS SIAP'

As a workaround to a bug in the SDSS service, pass format='' when searching.

sdss_image_table = sdss_image_service.search(pos=pos, size=0.0, format='', intersect='covers')
len(sdss_image_table['Title'])

60

for sdss_rband_record in sdss_image_table:
    if 'Sloan Digital Sky Survey - Filter r' in sdss_rband_record.title:
        break
print(sdss_rband_record.title, sdss_rband_record.bandpass_id)

Sloan Digital Sky Survey - Filter r None

##  See above regarding two ways to do this
# sdss_rband_image = fits.open(sdss_rband_record.getdataurl())
file_name = download_file(sdss_rband_record.getdataurl(), cache=True)  
sdss_rband_image=fits.open(file_name)

fig = plt.figure()

ax = fig.add_subplot(1, 1, 1, projection=WCS(sdss_rband_image[0].header))

interval = vis.PercentileInterval(99.9)
vmin,vmax = interval.get_limits(sdss_rband_image[0].data)
norm = vis.ImageNormalize(vmin=vmin, vmax=vmax, stretch=vis.LogStretch(1000))
ax.imshow(sdss_rband_image[0].data, cmap = 'gray_r', norm = norm, origin = 'lower')          
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')

<matplotlib.collections.PathCollection at 0x7fe81a6e5e10>

cutout = Cutout2D(sdss_rband_image[0].data, pos, size, wcs=WCS(sdss_rband_image[0].header))
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1, projection=cutout.wcs)
vmin,vmax = interval.get_limits(sdss_rband_image[0].data)
norm = vis.ImageNormalize(vmin=vmin, vmax=vmax, stretch=vis.LogStretch(1000))
ax.imshow(cutout.data, cmap = 'gray_r', norm = norm, origin = 'lower')          
ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')

<matplotlib.collections.PathCollection at 0x7fe82017e350>

15. Try looping over all positions and plotting multiwavelength cutouts.

Warning: this cell takes a long time to run! We limit it to the first three galaxies only.

# Pick the first 3 galaxies.
galaxy_subset = galaxies[0:3]

# For each galaxy,
for galaxy in galaxy_subset:

    # Establish the position.
    ra = galaxy['RA']
    dec = galaxy['DEC']
    pos = SkyCoord(ra, dec, unit = 'deg')  
    
    # Set up the plot for this position.
    fig = plt.figure(figsize=(20,6))
    plt.suptitle('POSITION = ' + str(ra) + ', ' + str(dec), fontsize=16)

    # GALEX
    
    # Find the GALEX images that overlap the position.
    galex_image_table = galex_image_service.search(pos=pos, size=0.25)
    
    # Find the GALEX All-Sky Image Survey (AIS) Near-UV FITS coadd. 
    for i in range(len(galex_image_table)):
        if (('image/fits' in galex_image_table[i].format) and
            (galex_image_table['enrValue'][0]==2.35e-07)):
            break
    galex_image_record = galex_image_table[i]
 
    # Create a cutout.
    file_name = download_file(galex_image_record.getdataurl(), cache=True)  
    gimage=fits.open(file_name)
    galex_cutout = Cutout2D(gimage[0].data, pos, size, wcs=WCS(gimage[0].header))
    
    # Plot the cutout in the first position of a 1x3 (rowsxcols) grid.
    ax = fig.add_subplot(1, 3, 1, projection=galex_cutout.wcs)
    ax.set_title(galex_image_record.title)
    ax.imshow(galex_cutout.data, cmap='gray_r', origin='lower', vmin = 0.0, vmax = 0.01)
    ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
    
    # SDSS
    
    # Find the SDSS images that overlap the position.
    sdss_image_table = sdss_image_service.search(pos=pos, size=0, format='')
    
    # Find the first SDSS r-band image.
    for sdss_rband_record in sdss_image_table:
        if 'Sloan Digital Sky Survey - Filter r' in sdss_rband_record.title:
            break
    file_name = download_file(sdss_rband_record.getdataurl(), cache=True)  
    sdss_rband_image=fits.open(file_name)
    
    # Create a cutout.
    sdss_cutout = Cutout2D(sdss_rband_image[0].data, pos, size,
                           wcs=WCS(sdss_rband_image[0].header))
    
    # Plot the cutout in the second position of a 1x3 grid.
    vmin,vmax = interval.get_limits(sdss_cutout.data)
    norm = vis.ImageNormalize(vmin=vmin, vmax=vmax, stretch=vis.LogStretch(1000))
    ax = fig.add_subplot(1, 3, 2, projection=sdss_cutout.wcs)
    ax.imshow(sdss_cutout.data, cmap = 'gray_r', norm = norm, origin = 'lower')          
    ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
    ax.set_title(sdss_rband_record.title)
    
    # AllWISE
    
    # Find the AllWISE images that overlap the position.
    allwise_image_table = allwise_image_service.search(pos=pos, size=0)
    
    # Find the AllWISE W1 channel images.
    for allwise_image_record in allwise_image_table:
        if 'W1' in allwise_image_record.bandpass_id:
            break
    file_name = download_file(allwise_image_record.getdataurl(), cache=True)  
    allwise_w1_image=fits.open(file_name)

    
    # Create a cutout.
    allwise_cutout = Cutout2D(allwise_w1_image[0].data, pos, (size, size),
                              wcs=WCS(allwise_w1_image[0].header))

    # Plot the cutout in the third position of a 1x3 grid.
    ax = fig.add_subplot(1, 3, 3, projection=allwise_cutout.wcs)
    ax.imshow(allwise_cutout.data, cmap='gray_r', origin='lower', vmax = 10)
    ax.scatter(ra, dec, transform=ax.get_transform('fk5'), s=500, edgecolor='red', facecolor='none')
    ax.set_title(allwise_image_record.title)