Candidate List Solution

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

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.ipac.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	52766.0	0.17601	SLS	17.5g	--	15	0	63	8	7	10	0
1	2	WISEA J003807.80-010936.7	9.53252	-1.16024	G	62442.0	0.208284	SLS	18.0g	--	16	0	72	12	9	10	0
2	3	WISEA J040422.92-054134.8	61.09548	-5.69303	G	75113.0	0.250549	SLS	18.6g	--	7	0	61	10	7	8	0
3	4	WISEA J073806.16+282359.6	114.52568	28.39995	G	69225.0	0.230909	SLS	18.0g	--	10	0	66	7	4	10	0
4	5	2MASX J07550424+1353261	118.76776	13.89065	G	66746.0	0.22264	SLS	18.5g	--	10	0	38	7	6	6	0
5	6	WISEA J082655.11+181147.7	126.72969	18.19665	G	79580.0	0.265449	SLS	18.4g	--	8	0	46	7	6	6	0
6	7	2MASX J08542169+0449308	133.59033	4.82511	G	47005.0	0.156793	SLS	16.7g	--	18	0	58	8	6	10	0
7	8	WHL J090944.8+222607	137.4367	22.43539	GClstr	88683.0	0.295813	PUN		--	7	0	0	4	5	0	0
8	9	2MASX J09094480+2226078	137.4367	22.43538	G	85557.0	0.285388	SLS	19.0g	--	9	0	30	5	6	6	0
9	10	2MASX J09260805+2405242	141.53357	24.09004	G	66672.0	0.222393	SLS	17.8g	--	16	0	32	7	9	6	0
10	11	WHL J092608.1+240524	141.53357	24.09004	GClstr	66284.0	0.2211	SUN		--	8	0	0	6	7	0	0
11	12	WISEA J093347.77+211436.7	143.44901	21.24346	G	51620.0	0.172186	SLS	16.9g	--	48	0	51	20	15	6	1
12	13	WISEA J093622.14+390628.9	144.09222	39.10801	G	84821.0	0.282931	SLS	18.5g	--	6	0	66	7	3	10	0
13	14	MSPM 05544	146.2235	22.8851	GClstr	26738.0	0.08919	MUN		--	3	0	0	1	1	0	0
14	15	CGCG 122-067	146.22351	22.88512	G	26686.0	0.089016	SLS	15.3g	--	34	0	49	13	11	8	0
15	16	WISEA J094700.08+254045.8	146.75035	25.67938	G	32690.0	0.109043	SLS	15.8g	--	22	0	46	8	7	6	0
16	17	SDSS J095727.02+083501.7	149.36259	8.58383	G	76903.0	0.256521	SLS	18.3g	--	8	0	39	5	3	8	0
17	18	WISEA J100356.88+382902.1	150.98703	38.48391	G	77525.0	0.258596	SLS	17.8g	--	8	0	57	7	4	8	0
18	19	WISEA J100416.04+295844.1	151.06683	29.97899	G	89471.0	0.298443	SLS	18.7g	--	7	0	66	7	4	10	0
19	20	WISEA J100956.35+261132.0	152.48475	26.19223	G	72216.0	0.240886	SLS	18.1g	--	13	0	46	7	4	6	0
20	21	GMBCG J152.52936+32.89139	152.52936	32.89139	GClstr	95634.0	0.319	PUN		--	4	0	0	2	2	0	0
21	22	WISEA J101007.05+325329.0	152.52936	32.89141	G	86909.0	0.289896	SLS	18.7g	--	10	0	66	7	4	10	0
22	23	2MASS J10160396+3037481	154.01655	30.62998	G	75520.0	0.251906	SLS	18.7g	--	6	0	49	7	4	8	0
23	24	WISEA J102154.85+072415.5	155.47856	7.40432	G	87123.0	0.290611	SLS	18.4g	--	6	0	55	7	3	8	0
24	25	WISEA J103015.74-010607.0	157.56561	-1.10198	G	84625.0	0.282277	SLS	18.3g	--	11	0	62	9	6	10	0
25	26	2MASX J10304263+0418219	157.67753	4.30602	G	48244.0	0.160924	SLS	16.8g	--	24	0	59	8	6	10	0
26	27	SDSS CE J160.241898-01.069106	160.235	-1.06611	GClstr	75548.0	0.252	SUN		--	8	0	0	5	7	0	0
27	28	2MASX J10405643-0103584	160.23509	-1.06632	G	75021.0	0.250242	SLS	18.2g	--	17	0	51	9	9	10	0
28	29	WISEA J104724.97+230917.4	161.85435	23.15474	G	54729.0	0.182556	SLS	18.46	--	19	0	46	8	6	6	0
29	30	WISEA J111917.41+141946.5	169.8226	14.3296	G	43101.0	0.143771	SLS	16.6g	--	20	0	66	8	6	10	0
30	31	2MASS J11292875+0255498	172.36978	2.93055	G	71830.0	0.239599	SLS	17.5g	--	9	0	49	7	5	8	0
31	32	WISEA J113800.86+521303.8	174.50367	52.21775	G	88717.0	0.295927	SLS	19.0g	--	9	0	57	7	3	8	0
32	33	WISEA J114100.04+384807.4	175.25022	38.80207	G	80255.0	0.267701	SLS	18.5g	--	7	0	66	8	6	10	0
33	34	WISEA J115052.96+460448.1	177.72076	46.08006	G	86778.0	0.289461	SLS	18.5g	--	10	0	53	9	7	8	0
34	35	WISEA J115356.21+492355.4	178.48416	49.39881	G	49984.0	0.166728	SLS	17.1g	--	19	0	67	9	6	10	0
35	36	2MASX J11593546+1257080	179.89761	12.95205	G	79003.0	0.263525	SLS	18.3g	--	7	0	54	6	4	10	0
36	37	WISEA J120053.92+480007.8	180.22477	48.00213	G	83466.0	0.278413	SLS	18.4g	--	8	0	70	7	5	10	0
37	38	GMBCG J180.22479+48.00211	180.22479	48.00211	GClstr	74948.0	0.25	PUN		--	6	0	0	3	4	0	0
38	39	WISEA J121644.33+122450.5	184.18477	12.41404	G	77030.0	0.256944	SLS	18.3g	--	5	0	55	7	3	8	0
39	40	WISEA J122100.50+482729.1	185.25209	48.45807	G	89836.0	0.299659	SLS	18.7g	--	8	0	61	8	3	8	0
40	41	WISEA J123215.19+102119.1	188.06311	10.35535	G	49729.0	0.165877	SLS	17.2g	--	19	0	66	9	6	10	0
41	42	WISEA J123431.08+515629.7	188.62952	51.94146	G	88714.0	0.295917	SLS	18.1g	--	31	0	64	11	12	10	0
42	43	WISEA J123746.63+481227.5	189.44448	48.20767	G	81679.0	0.272452	SLS	18.2g	--	8	0	65	8	4	10	0
43	44	WISEA J131039.32+223502.8	197.66385	22.58407	G	69320.0	0.231225	SLS	18.1g	--	12	0	48	8	5	6	1
44	45	WISEA J132757.52+334529.3	201.98969	33.75806	G	74623.0	0.248916	SLS	17.7g	--	16	0	88	12	4	10	0
45	46	WISEA J134228.33+115734.5	205.61806	11.95954	G	83561.0	0.27873	SLS	19.0g	--	8	0	66	7	4	10	0
46	47	WISEA J134355.49+244048.1	205.98124	24.68001	G	41147.0	0.13725	SLS	17.81	--	21	0	46	10	8	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	66870.0	0.223055	SLS	18.1g	--	11	0	54	6	4	10	0
49	50	WISEA J135546.08+025456.0	208.942	2.91553	G	71602.0	0.238837	SLS	18.7g	--	11	0	51	7	5	8	0
50	51	WISEA J140138.37+263527.6	210.40991	26.591	G	85128.0	0.283958	SLS	18.7g	--	8	0	30	5	4	4	0
51	52	2MASS J14175497+2704341	214.47905	27.07624	G	47226.0	0.15753	SLS	16.9g	--	17	0	25	6	6	4	0
52	53	WISEA J143447.88+020228.8	218.69942	2.04129	G	83914.0	0.279907	SLS	18.6g	--	10	0	62	9	5	8	0
53	54	WISEA J144728.35+590831.9	221.86807	59.14209	G	73602.0	0.245509	SLS	18.3g	--	15	0	70	11	8	10	0
54	55	WISEA J153619.01+452247.7	234.07909	45.37968	G	70804.0	0.236177	SLS	18.1g	--	9	0	57	7	3	8	0
55	56	WISEA J154307.78+193751.7	235.78241	19.63104	G	68774.0	0.229405	SLS	17.0g	--	43	1	50	21	18	6	0
56	57	WISEA J154950.90+234444.0	237.46216	23.7456	G	78570.0	0.262082	SLS	18.5g	--	7	0	31	5	4	4	0
57	58	GMBCG J240.41924+27.30444	240.41924	27.30444	GClstr	57860.0	0.193	PUN		--	4	0	0	2	2	0	0
58	59	2MASX J16014061+2718161	240.41924	27.30443	G	49285.0	0.164398	SLS	16.7g	--	22	0	54	7	7	10	1
59	60	WISEA J163945.96+460905.8	249.94148	46.1517	G	74087.0	0.247128	SLS	18.3g	--	13	0	70	8	4	10	0
60	61	WISEA J173406.17+602919.1	263.52577	60.48859	G	82730.0	0.275956	SLS	18.5g	--	8	0	66	7	4	10	0
61	62	SDSSCGB 59704	263.53292	60.48197	GGroup	82743.0	0.276	SST	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 (e.g. galaxy clusters, galaxy groups). Print the ‘Type’ column to see the full range of classifications and 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

# Keep only the galaxies from the list
galaxies = objects_in_paper[np.array(objects_in_paper['Type']) == '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	52766.0	0.17601	SLS	17.5g	--	15	0	63	8	7	10	0
1	2	WISEA J003807.80-010936.7	9.53252	-1.16024	G	62442.0	0.208284	SLS	18.0g	--	16	0	72	12	9	10	0
2	3	WISEA J040422.92-054134.8	61.09548	-5.69303	G	75113.0	0.250549	SLS	18.6g	--	7	0	61	10	7	8	0
3	4	WISEA J073806.16+282359.6	114.52568	28.39995	G	69225.0	0.230909	SLS	18.0g	--	10	0	66	7	4	10	0
4	5	2MASX J07550424+1353261	118.76776	13.89065	G	66746.0	0.22264	SLS	18.5g	--	10	0	38	7	6	6	0
5	6	WISEA J082655.11+181147.7	126.72969	18.19665	G	79580.0	0.265449	SLS	18.4g	--	8	0	46	7	6	6	0
6	7	2MASX J08542169+0449308	133.59033	4.82511	G	47005.0	0.156793	SLS	16.7g	--	18	0	58	8	6	10	0
7	9	2MASX J09094480+2226078	137.4367	22.43538	G	85557.0	0.285388	SLS	19.0g	--	9	0	30	5	6	6	0
8	10	2MASX J09260805+2405242	141.53357	24.09004	G	66672.0	0.222393	SLS	17.8g	--	16	0	32	7	9	6	0
9	12	WISEA J093347.77+211436.7	143.44901	21.24346	G	51620.0	0.172186	SLS	16.9g	--	48	0	51	20	15	6	1
10	13	WISEA J093622.14+390628.9	144.09222	39.10801	G	84821.0	0.282931	SLS	18.5g	--	6	0	66	7	3	10	0
11	15	CGCG 122-067	146.22351	22.88512	G	26686.0	0.089016	SLS	15.3g	--	34	0	49	13	11	8	0
12	16	WISEA J094700.08+254045.8	146.75035	25.67938	G	32690.0	0.109043	SLS	15.8g	--	22	0	46	8	7	6	0
13	17	SDSS J095727.02+083501.7	149.36259	8.58383	G	76903.0	0.256521	SLS	18.3g	--	8	0	39	5	3	8	0
14	18	WISEA J100356.88+382902.1	150.98703	38.48391	G	77525.0	0.258596	SLS	17.8g	--	8	0	57	7	4	8	0
15	19	WISEA J100416.04+295844.1	151.06683	29.97899	G	89471.0	0.298443	SLS	18.7g	--	7	0	66	7	4	10	0
16	20	WISEA J100956.35+261132.0	152.48475	26.19223	G	72216.0	0.240886	SLS	18.1g	--	13	0	46	7	4	6	0
17	22	WISEA J101007.05+325329.0	152.52936	32.89141	G	86909.0	0.289896	SLS	18.7g	--	10	0	66	7	4	10	0
18	23	2MASS J10160396+3037481	154.01655	30.62998	G	75520.0	0.251906	SLS	18.7g	--	6	0	49	7	4	8	0
19	24	WISEA J102154.85+072415.5	155.47856	7.40432	G	87123.0	0.290611	SLS	18.4g	--	6	0	55	7	3	8	0
20	25	WISEA J103015.74-010607.0	157.56561	-1.10198	G	84625.0	0.282277	SLS	18.3g	--	11	0	62	9	6	10	0
21	26	2MASX J10304263+0418219	157.67753	4.30602	G	48244.0	0.160924	SLS	16.8g	--	24	0	59	8	6	10	0
22	28	2MASX J10405643-0103584	160.23509	-1.06632	G	75021.0	0.250242	SLS	18.2g	--	17	0	51	9	9	10	0
23	29	WISEA J104724.97+230917.4	161.85435	23.15474	G	54729.0	0.182556	SLS	18.46	--	19	0	46	8	6	6	0
24	30	WISEA J111917.41+141946.5	169.8226	14.3296	G	43101.0	0.143771	SLS	16.6g	--	20	0	66	8	6	10	0
25	31	2MASS J11292875+0255498	172.36978	2.93055	G	71830.0	0.239599	SLS	17.5g	--	9	0	49	7	5	8	0
26	32	WISEA J113800.86+521303.8	174.50367	52.21775	G	88717.0	0.295927	SLS	19.0g	--	9	0	57	7	3	8	0
27	33	WISEA J114100.04+384807.4	175.25022	38.80207	G	80255.0	0.267701	SLS	18.5g	--	7	0	66	8	6	10	0
28	34	WISEA J115052.96+460448.1	177.72076	46.08006	G	86778.0	0.289461	SLS	18.5g	--	10	0	53	9	7	8	0
29	35	WISEA J115356.21+492355.4	178.48416	49.39881	G	49984.0	0.166728	SLS	17.1g	--	19	0	67	9	6	10	0
30	36	2MASX J11593546+1257080	179.89761	12.95205	G	79003.0	0.263525	SLS	18.3g	--	7	0	54	6	4	10	0
31	37	WISEA J120053.92+480007.8	180.22477	48.00213	G	83466.0	0.278413	SLS	18.4g	--	8	0	70	7	5	10	0
32	39	WISEA J121644.33+122450.5	184.18477	12.41404	G	77030.0	0.256944	SLS	18.3g	--	5	0	55	7	3	8	0
33	40	WISEA J122100.50+482729.1	185.25209	48.45807	G	89836.0	0.299659	SLS	18.7g	--	8	0	61	8	3	8	0
34	41	WISEA J123215.19+102119.1	188.06311	10.35535	G	49729.0	0.165877	SLS	17.2g	--	19	0	66	9	6	10	0
35	42	WISEA J123431.08+515629.7	188.62952	51.94146	G	88714.0	0.295917	SLS	18.1g	--	31	0	64	11	12	10	0
36	43	WISEA J123746.63+481227.5	189.44448	48.20767	G	81679.0	0.272452	SLS	18.2g	--	8	0	65	8	4	10	0
37	44	WISEA J131039.32+223502.8	197.66385	22.58407	G	69320.0	0.231225	SLS	18.1g	--	12	0	48	8	5	6	1
38	45	WISEA J132757.52+334529.3	201.98969	33.75806	G	74623.0	0.248916	SLS	17.7g	--	16	0	88	12	4	10	0
39	46	WISEA J134228.33+115734.5	205.61806	11.95954	G	83561.0	0.27873	SLS	19.0g	--	8	0	66	7	4	10	0
40	47	WISEA J134355.49+244048.1	205.98124	24.68001	G	41147.0	0.13725	SLS	17.81	--	21	0	46	10	8	6	0
41	49	2MASX J13475962+3227100	206.99841	32.45286	G	66870.0	0.223055	SLS	18.1g	--	11	0	54	6	4	10	0
42	50	WISEA J135546.08+025456.0	208.942	2.91553	G	71602.0	0.238837	SLS	18.7g	--	11	0	51	7	5	8	0
43	51	WISEA J140138.37+263527.6	210.40991	26.591	G	85128.0	0.283958	SLS	18.7g	--	8	0	30	5	4	4	0
44	52	2MASS J14175497+2704341	214.47905	27.07624	G	47226.0	0.15753	SLS	16.9g	--	17	0	25	6	6	4	0
45	53	WISEA J143447.88+020228.8	218.69942	2.04129	G	83914.0	0.279907	SLS	18.6g	--	10	0	62	9	5	8	0
46	54	WISEA J144728.35+590831.9	221.86807	59.14209	G	73602.0	0.245509	SLS	18.3g	--	15	0	70	11	8	10	0
47	55	WISEA J153619.01+452247.7	234.07909	45.37968	G	70804.0	0.236177	SLS	18.1g	--	9	0	57	7	3	8	0
48	56	WISEA J154307.78+193751.7	235.78241	19.63104	G	68774.0	0.229405	SLS	17.0g	--	43	1	50	21	18	6	0
49	57	WISEA J154950.90+234444.0	237.46216	23.7456	G	78570.0	0.262082	SLS	18.5g	--	7	0	31	5	4	4	0
50	59	2MASX J16014061+2718161	240.41924	27.30443	G	49285.0	0.164398	SLS	16.7g	--	22	0	54	7	7	10	1
51	60	WISEA J163945.96+460905.8	249.94148	46.1517	G	74087.0	0.247128	SLS	18.3g	--	13	0	70	8	4	10	0
52	61	WISEA J173406.17+602919.1	263.52577	60.48859	G	82730.0	0.275956	SLS	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']

263 result(s) found.

Table length=263

ivoid	short_name	res_title
object	object	object
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 (GALEX)
ivo://archive.stsci.edu/siap/hla	HLA	Hubble Legacy Archive
ivo://arvo/siap	ArVO SIAP	Armenian Virtual Observatory SIAP
ivo://astron.nl/apertif_dr_bootes/q/cutout	DR Bootes	Apertif DR Bootes - Mosaic
ivo://astron.nl/hetdex/lotss-dr1-img/cutout	LoTSS-DR1 Cutout	LoTSS-DR1 Image Cutout Service
ivo://astron.nl/hetdex/lotss-dr1-img/imgs	LoTSS-DR1 images	LoTSS-DR1 Image Archive
ivo://astron.nl/lofartier1/q_img/cutout	LoTSS-PDR Cutout	LoTSS-PDR Image Cutout Service
...	...	...
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 0x7fb6d071e0e0>

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

<DALResultsTable 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	cloud_access	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	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	{"aws": {"bucket_name": "nasa-irsa-wise", "key":"wise/allwise/images/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w1-int-3.fits", "region": "us-west-2"}}	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	{"aws": {"bucket_name": "nasa-irsa-wise", "key":"wise/allwise/images/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w3-int-3.fits", "region": "us-west-2"}}	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	{"aws": {"bucket_name": "nasa-irsa-wise", "key":"wise/allwise/images/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w4-int-3.fits", "region": "us-west-2"}}	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	{"aws": {"bucket_name": "nasa-irsa-wise", "key":"wise/allwise/images/p3am_cdd/00/0046/0046m107_ac51/0046m107_ac51-w2-int-3.fits", "region": "us-west-2"}}	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 0x7fb6cfdc1900>

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 0x7fb6c7bc04f0>

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']

3 result(s) found.

Table length=3

ivoid	short_name	res_title
object	object	object
ivo://archive.stsci.edu/sia/galex	GALEX	Galaxy Evolution Explorer (GALEX)
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['energy_bounds_center'][i]==2.35e-07) and
        (galex_image_table[i]['productType'] == 'SCIENCE')):
        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 NUV

## 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 0x7fb6c49af220>

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 0x7fb6c7bc39a0>

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'

• 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=21

ivoid	short_name	res_title	source_value
object	object	object	object
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) images	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'

sdss_image_table = sdss_image_service.search(pos=pos, size=0.0)
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')

WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 54007.000000 from DATE-OBS'. [astropy.wcs.wcs]

<matplotlib.collections.PathCollection at 0x7fb6c4740070>

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')

WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 54007.000000 from DATE-OBS'. [astropy.wcs.wcs]

<matplotlib.collections.PathCollection at 0x7fb6c47a7280>

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.
    galex_image_record = None
    for record in galex_image_table:
        if (('image/fits' in record.format) and
            (record['energy_bounds_center'] == 2.35e-07) and
            (record['productType'] == 'SCIENCE')):
            galex_image_record = record
            break

    if galex_image_record is not None:
        # 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')
    else:
        # We didn't find a suitable image, so leave that subplot blank.
        ax = fig.add_subplot(1, 3, 1, projection=galex_cutout.wcs)
        ax.set_title('GALEX image not found')

    # SDSS

    # Find the SDSS images that overlap the position.
    sdss_image_table = sdss_image_service.search(pos=pos, size=0)

    # Find the first SDSS r-band image.
    sdss_rband_record = None
    for record in sdss_image_table:
        if 'Sloan Digital Sky Survey - Filter r' in record.title:
            sdss_rband_record = record
            break

    if sdss_rband_record is not None:
        # Create a cutout.
        file_name = download_file(sdss_rband_record.getdataurl(), cache=True)
        sdss_rband_image=fits.open(file_name)

        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)
    else:
        # We didn't find a suitable image, so leave that subplot blank.
        ax = fig.add_subplot(1, 3, 2, projection=galex_cutout.wcs)
        ax.set_title('SDSS rband image not found')

    # AllWISE

    # Find the AllWISE images that overlap the position.
    allwise_image_table = allwise_image_service.search(pos=pos, size=0)

    # Find the first AllWISE W1 channel image.
    allwise_image_record = None
    for record in allwise_image_table:
        if 'W1' in record.bandpass_id:
            allwise_image_record = record
            break

    if allwise_image_record is not None:
        # Create a cutout.
        file_name = download_file(allwise_image_record.getdataurl(), cache=True)
        allwise_w1_image=fits.open(file_name)

        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)
    else:
        # We didn't find a suitable image, so leave that subplot blank.
        ax = fig.add_subplot(1, 3, 3, projection=galex_cutout.wcs)
        ax.set_title('AllWISE W1 image not found')

WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 54007.000000 from DATE-OBS'. [astropy.wcs.wcs]

WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 54424.000000 from DATE-OBS'. [astropy.wcs.wcs]

WARNING: FITSFixedWarning: 'datfix' made the change 'Set MJD-OBS to 51465.000000 from DATE-OBS'. [astropy.wcs.wcs]