303.1. Galaxy photometry

303.1. Galaxy photometry¶

For the Rubin Science Platform at data.lsst.cloud.
Data Release: Data Preview 1
Container Size: large
LSST Science Pipelines version: r29.2.0
Last verified to run: 2025-09-02
Repository: github.com/lsst/tutorial-notebooks

Learning objective: Explore the available measurements of galaxy photometry produced by the LSST pipelines and their applications.

LSST data products: Object table

Packages: lsst.rsp.get_tap_service

Credit: This notebook benefitted from an earlier exploration of simulated data and notebook development by Melissa Graham and Dan Taranu, helpful discussions with Jim Bosch, and ideas from Douglas Tucker.

Get Support: Everyone is encouraged to ask questions or raise issues in the Support Category of the Rubin Community Forum. Rubin staff will respond to all questions posted there.

1. Introduction¶

Photometry is the measurement of how much light is apparent from astronomical sources. The amount of light arriving on the telescope from the object is typically referred to as the flux density, or apparent magnitude (depending on units). Flux density is defined as the amount of energy arriving on the telescope per unit area, per unit time, per unit frequency (or wavelength) of the light.

The LSST Science Pipelines makes a variety of photometric measurements for point-like and extended sources. This notebook will teach the user about the automated photometry measurements for extended sources that are measured on the deepCoadd images and appear in the Object Catalog as part of the LSST pipelines data products.

The photometry measurements in the catalogs are flux densities in units of nano-Jansky [nJy]. 1 Jy = 10^{-23} ergs/s/cm^2/Hz.

1.1. Import packages¶

Import numpy, a fundamental package for scientific computing with arrays in Python (numpy.org), and matplotlib, a comprehensive library for data visualization (matplotlib.org; matplotlib gallery).

From the lsst package, import modules for accessing the Table Access Protocol (TAP) service, the butler, and image display functions from the LSST Science Pipelines (pipelines.lsst.io).

In [1]:

import numpy as np
import matplotlib.pyplot as plt

from lsst.rsp import get_tap_service

from scipy.stats import binned_statistic

1.2. Define parameters and functions¶

Get an instance of the TAP service, and assert that it exists.

In [2]:

service = get_tap_service("tap")
assert service is not None

2. Types of photometry¶

This section will explore photometry measurements produced by the LSST pipelines, and provide some guidance for which are optimal for various applications for science with galaxies.

2.1. Explore the schema¶

Numerous photometry measurements are produced by the LSST Pipelines. Two types of photometry are in the object table. The first are total fluxes (see Section 3), which aim to approximate (or model) all of the light coming from object. The second class of fluxes are measured inside an on-sky aperture but not corrected for flux that may fall outside: thus they are apparent fluxes but do not recover the intrisic (total) flux (see Section 4 and 5). The apparent fluxes are optimized for other purposes, such as for measuring accurate light profiles or accurate colors.

Schema for the object catalog for DP1 is available here. It lists the catalog header and brief explanation of the parameters.

First, see what is available in the object table by querying the tap_schema columns, and printing all the parameters available related to "Flux" measured in the i-band (as an example). For clarity, the return also omits errors and flags associated with the photometric measurements outlined in section 1.1.

In [3]:

query = "SELECT column_name, datatype, description, unit " \
        "FROM tap_schema.columns " \
        "WHERE table_name = 'dp1.Object'"

results = service.search(query).to_table()

In [4]:

search_string = 'Flux'
band = 'i_'
exclude1 = 'Err'
exclude2 = 'flag'
for cname in results['column_name']:
    if cname.find(search_string) > -1 and cname.find(band) > -1 and \
       cname.find(exclude1) == -1 and cname.find(exclude2) == -1:

        print(cname)

i_ap03Flux
i_ap06Flux
i_ap09Flux
i_ap12Flux
i_ap17Flux
i_ap25Flux
i_ap35Flux
i_ap50Flux
i_ap70Flux
i_bdFluxB
i_bdFluxD
i_calibFlux
i_cModelFlux
i_cModelFlux_inner
i_deblend_zeroFlux
i_free_cModelFlux
i_free_cModelFlux_inner
i_free_psfFlux
i_gaap0p7Flux
i_gaap1p0Flux
i_gaap1p5Flux
i_gaap2p5Flux
i_gaap3p0Flux
i_gaapOptimalFlux
i_gaapPsfFlux
i_kronFlux
i_psfFlux
i_psfFlux_area
i_sersicFlux

The object catalog also has pre-computed AB magnitudes (Mag columns) for cModel and PSF. Query the tap_schema columns, and print all the parameters available related to Mag measured in the i-band.

In [5]:

search_string = 'Mag'
band = 'i_'
exclude1 = 'Err'
for cname in results['column_name']:
    if cname.find(search_string) > -1 and cname.find(band) > -1 and \
       cname.find(exclude1) == -1:

        print(cname)

i_cModelMag
i_psfMag

2.2. Select a galaxy sample¶

Below, query the DP1 objectTable for a selection of photometric measurements.

Limit the search to contain galaxies using the i_extendedness flag (which will exclude point sources). Further, identify objects which have been detected at high signal to noise > 20 and whose photometric measurements have not been flagged as having an issue (i_kronFlux_flag or i_cModel_flag or sersic_no_data_flag = 0 means the photometry is ok). Also exclude very bright galaxies (i-band magnitude < 20).

Search for the sample using the DP1 imaging obtained in the Extended Chandra Deep Field South (ECDFS; center ra, dec = 53.2, -28.1 in degrees) which was one of the deepest imaging obtained as part of DP1.

In [6]:

target_ra = 53.2
target_dec = -28.1

In [7]:

query = "SELECT obj.objectId, obj.coord_ra, obj.coord_dec, " + \
        "obj.detect_fromBlend, obj.detect_isIsolated, " + \
        "obj.i_blendedness, obj.i_extendedness, " + \
        "obj.i_kronFlux, obj.i_kronFluxErr, obj.i_kronRad, " + \
        "obj.i_cModelFlux, obj.i_cModelFluxErr, obj.i_gaap1p0Flux, " + \
        "obj.g_gaap1p0Flux, obj.g_gaap3p0Flux, obj.sersic_index, " + \
        "obj.i_sersicFlux, obj.i_bdFluxB, obj.i_bdFluxD, " + \
        "obj.i_kronFlux_flag, obj.i_cModel_flag, obj.sersic_no_data_flag, " + \
        "obj.shape_xx, obj.shape_xy, obj.shape_yy, " + \
        "obj.i_ap03Flux, obj.i_ap06Flux, obj.i_ap09Flux, obj.i_ap12Flux, " + \
        "obj.i_ap17Flux, obj.i_ap25Flux, obj.i_ap35Flux, obj.i_ap50Flux " + \
        "FROM dp1.Object AS obj " + \
        "WHERE (obj.i_cModelFlux/obj.i_cModelFluxErr > 20) AND " + \
        "(obj.i_extendedness = 1) AND (obj.sersic_no_data_flag = 0) AND " + \
        "(obj.i_kronFlux_flag = 0) AND (obj.i_cModel_flag = 0) AND " + \
        "(scisql_nanojanskyToAbMag(obj.i_cModelFlux) > 20) AND " + \
        "CONTAINS(POINT('ICRS', obj.coord_ra, obj.coord_dec), " + \
        "CIRCLE('ICRS',"+str(target_ra)+","+str(target_dec)+", 0.1)) = 1 "

In [8]:

job = service.submit_job(query)
job.run()
job.wait(phases=['COMPLETED', 'ERROR'])
print('Job phase is', job.phase)

Job phase is COMPLETED

In [9]:

if job.phase == 'ERROR':
    job.raise_if_error()
assert job.phase == 'COMPLETED'

Print the results of the search query.

In [10]:

results = job.fetch_result()
tab = results.to_table()
tab

Out[10]:

Table length=3415

objectId	coord_ra	coord_dec	detect_fromBlend	detect_isIsolated	i_blendedness	i_extendedness	i_kronFlux	i_kronFluxErr	i_kronRad	i_cModelFlux	i_cModelFluxErr	i_gaap1p0Flux	g_gaap1p0Flux	g_gaap3p0Flux	sersic_index	i_sersicFlux	i_bdFluxB	i_bdFluxD	i_kronFlux_flag	i_cModel_flag	sersic_no_data_flag	shape_xx	shape_xy	shape_yy	i_ap03Flux	i_ap06Flux	i_ap09Flux	i_ap12Flux	i_ap17Flux	i_ap25Flux	i_ap35Flux	i_ap50Flux
	deg	deg					nJy	nJy		nJy	nJy	nJy	nJy	nJy		nJy	nJy	nJy				pix2	pix2	pix2	nJy	nJy	nJy	nJy	nJy	nJy	nJy	nJy
int64	float64	float64	bool	bool	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	bool	bool	bool	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32	float32
611254385447544670	53.196927847186444	-28.1804600718091	True	False	2.55524e-05	1.0	6018.85	57.2624	5.28506	6624.39	36.3065	4359.9	375.164	645.085	1.82671	6117.17	7542.89	5583.97	False	False	False	9.04622	0.613085	13.5986	1813.42	3933.53	5041.48	5571.84	5888.74	6139.44	6701.27	8470.54
611254385447544671	53.19726601065951	-28.1822341580892	True	False	0.00778949	1.0	843.99	64.7884	6.00986	955.275	41.2438	546.298	313.86	450.621	4.0311	790.634	988.255	766.448	False	False	False	9.8238	0.496949	19.5712	220.921	484.32	647.856	762.393	840.881	997.566	1593.21	3445.8
611254385447544672	53.198798287317125	-28.179788251097747	True	False	0.0051192	1.0	589.539	54.5148	5.04057	522.132	25.9248	463.538	332.443	480.736	1.91497	580.486	606.066	522.872	False	False	False	8.34709	0.838076	9.25815	206.376	423.048	503.375	557.332	568.097	828.45	1397.17	2669.67
611254385447544688	53.1334069598854	-28.18051608260111	True	False	0.0547919	1.0	1959.29	43.7322	4.156	1869.68	23.1372	1728.79	1424.43	1705.37	4.4447	1927.4	1958.49	1838.16	False	False	False	6.57617	-1.46361	6.40932	862.233	1561.37	1808.45	1923.02	1994.92	2310.73	3074.49	4691.66
611254385447544708	53.13791468796697	-28.16274697875353	True	False	0.00952725	1.0	874.149	39.5331	3.84487	796.676	19.5714	809.129	353.69	515.152	5.66921	846.656	798.454	799.426	False	False	False	4.31486	0.193086	5.2599	438.852	716.456	828.858	888.12	1012.76	1204.57	1584.29	2757.13
611254385447544709	53.132257927134766	-28.16659920666505	True	False	0.00643329	1.0	3546.08	119.228	11.5203	4084.18	72.3724	1296.43	465.274	1105.05	3.85711	6783.63	4579.83	2531.25	False	False	False	26.7702	-3.23608	21.9209	440.929	1119.83	1678.8	2061.74	2613.9	3225.35	4114.26	5814.9
611254385447544710	53.14235306936339	-28.164458551572867	True	False	0.000453248	1.0	1345.03	55.7257	5.32483	1425.89	32.4023	1014.32	383.904	543.342	0.905101	1294.57	1750.83	1301.34	False	False	False	10.2958	-0.73507	12.8173	406.784	924.919	1180.77	1285.19	1277.56	1468.98	2145.59	3677.16
611254385447544711	53.14249653587028	-28.176201285069098	True	False	0.00066845	1.0	1144.07	60.2769	5.631	1032.82	27.548	837.146	637.767	855.186	4.53282	1118.85	1031.01	899.098	False	False	False	8.94127	-0.0640278	6.27459	392.18	732.72	933.6	1068.94	1202.1	1581.03	2191.76	4156.13
611254385447544687	53.13851073674786	-28.167620876533874	True	False	0.00669168	1.0	2318.04	42.262	4.08279	2128.36	20.3774	2121.73	1330.87	1517.57	5.99998	2157.22	2124.57	2105.51	False	False	False	5.04899	-0.295263	5.04941	1122.2	1880.03	2155.67	2331.62	2443.33	2714.67	3388.9	5090.99
611254385447544712	53.12184924256594	-28.168119072755374	True	False	0.0133237	1.0	1052.17	48.0556	4.61556	1113.29	29.5018	833.606	462.538	661.158	2.90778	1130.07	1110.47	961.537	False	False	False	6.1884	1.21349	11.7068	369.817	746.588	949.289	1017.8	1040.67	1267.09	1941.75	3255.5
611254385447544713	53.13789671815362	-28.175109249407495	True	False	0.0367149	1.0	1040.21	53.2293	5.00992	889.168	22.5484	845.635	387.116	722.322	5.86556	1202.08	891.633	852.633	False	False	False	6.40817	-0.0757515	5.25277	427.324	744.595	890.127	990.736	1152.57	1473.59	2500.85	8026.94
611254385447544704	53.13999427109625	-28.167077826115072	True	False	0.000843636	1.0	1338.66	49.5786	4.76992	1284.18	26.2861	1089.8	532.204	729.233	0.570971	1221.98	1542.38	1288.24	False	False	False	9.23703	-3.67454	10.5158	471.895	986.739	1231.24	1299.26	1346.09	1644.31	2482.32	4467.03
611254385447544696	53.139668081861565	-28.171158166398737	True	False	0.00188084	1.0	1576.75	58.0149	5.5163	1268.66	23.213	1180.91	1163.01	1692.87	5.99098	1717.33	1309.15	1226.87	False	False	False	6.80839	-0.130121	6.16629	572.288	1047.53	1253.07	1412.19	1672.09	2387.32	2874.44	4906.55
611254385447544706	53.12580444979522	-28.1742883581862	True	False	0.000554989	1.0	1154.85	48.532	4.63082	902.834	20.7191	904.344	366.086	498.37	5.99157	1017.44	900.443	882.628	False	False	False	5.10729	-0.101972	5.45125	463.048	785.265	946.576	1101.62	1171.76	1400.65	1991.28	3851.74
611254385447544693	53.139233135213125	-28.177719256631434	True	False	0.00370562	1.0	1914.2	59.2467	5.56042	1633.13	27.1873	1374.52	979.827	1357.47	3.11831	1926.45	1931.49	1588.68	False	False	False	9.30701	-0.674206	8.67378	603.71	1231.31	1520.79	1728.39	1954.01	2206.34	2839.41	4744.13
611254385447544707	53.131244812016796	-28.164301864562006	True	False	0.000767627	1.0	938.669	44.2952	4.25192	834.55	19.8887	844.945	387.017	597.606	6.0	843.658	844.08	837.259	False	False	False	5.22281	-0.0119719	5.00656	452.511	745.248	853.641	920.003	1114.46	1407.95	2083.57	3804.12
611254385447544692	53.13324544722604	-28.165936615043606	True	False	0.00390644	1.0	1668.51	49.0513	4.76948	1630.71	25.7117	1380.11	462.867	636.339	0.773023	1617.02	1944.45	1635.43	False	False	False	7.31123	2.32475	11.4547	605.147	1278.95	1513.34	1598.41	1692.75	1961.0	2772.27	4900.25
611254385447544729	53.13537390662842	-28.168412634376303	True	False	0.430555	1.0	9474.5	328.975	28.0712	2734.32	87.6303	630.309	305.484	750.092	3.36841	3748.96	2729.27	2042.26	False	False	False	40.1179	14.6703	132.91	279.832	533.367	721.491	955.97	1429.48	1982.41	2387.89	4743.5
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
611255072642313257	53.17023418840526	-28.00431090561822	True	False	0.0395665	1.0	855.899	53.4061	5.12536	840.003	23.6764	746.882	307.62	453.144	1.15507	840.808	933.329	834.535	False	False	False	9.34916	1.53112	5.47386	353.964	690.448	796.76	827.644	909.729	1216.06	1907.01	3186.71
611255072642313157	53.191281071678034	-28.013092552084622	True	False	0.000979161	1.0	464.602	31.6539	3.09638	441.947	21.0998	423.822	129.766	137.514	3.39422	506.374	475.459	443.646	False	False	False	7.24486	0.524554	5.08058	231.83	373.927	450.857	430.145	462.889	620.861	1117.88	2114.15
611255072642313158	53.19241903367821	-28.01238675191122	True	False	0.000538889	1.0	1492.32	188.95	18.1726	594.93	26.4674	474.547	180.07	259.575	0.573534	611.483	681.856	596.586	False	False	False	10.3002	-6.99878	12.9255	205.173	440.99	519.301	552.806	466.347	565.385	1062.29	1964.34
611255072642313223	53.20187752574822	-28.011830707015303	True	False	0.00820966	1.0	5668.0	39.1137	3.65578	5512.78	22.1091	5325.73	1053.28	1175.81	1.03987	5461.56	5683.46	5526.56	False	False	False	6.33081	0.543492	4.81811	2811.95	4843.44	5415.42	5625.9	5747.62	5895.64	6328.79	7393.49
611255072642313256	53.17306878479608	-28.00641102134664	True	False	0.000237623	1.0	1189.84	54.3475	5.2178	1210.26	30.5575	900.381	452.193	656.808	0.760444	1173.04	1666.74	1213.78	False	False	False	11.1706	-3.42166	15.1764	351.094	825.228	1085.33	1128.42	1148.3	1223.6	1761.17	3059.69
611255072642313252	53.17027666797002	-28.006166451694114	True	False	1.25513e-06	1.0	1942.03	32.4056	3.15589	1967.71	20.8909	1884.4	840.656	979.35	5.13896	1922.7	1965.5	1923.51	False	False	False	5.04716	0.325417	5.37331	1021.16	1713.54	1888.39	1942.88	1965.86	1995.69	2668.23	4170.63
611255072642313250	53.172850375787114	-28.00796616315619	True	False	7.52207e-05	1.0	5172.9	48.8043	4.73958	5083.71	28.1692	4011.36	1441.18	1937.62	0.524515	4926.79	6462.66	5098.54	False	False	False	8.03174	4.18749	15.4342	1688.61	3690.81	4601.79	4909.87	5137.82	5249.49	5711.33	6150.68
611255072642313249	53.17116084639328	-28.00421412937987	True	False	0.000784868	1.0	6727.95	47.3428	4.51595	6666.16	27.481	5564.65	1801.58	2152.63	0.654341	6504.57	8498.12	6684.79	False	False	False	9.28057	0.344461	9.75093	2428.48	5181.47	6195.84	6507.26	6685.01	6939.74	7729.76	8871.88
611255072642313222	53.2013736966671	-28.012418066332	True	False	0.00477363	1.0	7160.27	46.4819	4.32675	7248.86	26.8062	6061.9	833.774	996.112	3.76961	7199.04	7244.6	6494.71	False	False	False	7.20259	0.0453614	6.66745	2941.26	5485.32	6566.43	7000.7	7166.41	7337.19	7824.46	8752.27
611255072642313174	53.176345104255134	-28.01208398052733	True	False	0.0439997	1.0	1274.71	46.9674	4.53709	1218.94	24.9809	1068.31	623.626	805.914	0.773852	1277.08	1464.09	1222.42	False	False	False	7.62133	1.71155	9.61831	478.802	974.931	1183.24	1254.13	1319.79	1408.52	1691.21	2778.85
611255072642313179	53.17702969866711	-28.013220305310938	True	False	0.280033	1.0	594.368	47.51	4.61235	538.702	22.799	498.629	324.892	455.467	3.88928	567.579	621.557	550.911	False	False	False	9.1675	-0.991261	6.98671	237.062	452.574	536.37	572.987	626.354	897.213	1570.98	2815.02
611255072642313178	53.17785604804501	-28.01054338803276	True	False	0.0985817	1.0	420.831	47.5318	4.6012	449.065	22.2849	405.34	308.068	462.358	3.35672	430.544	471.577	450.313	False	False	False	6.82411	0.780469	6.17998	207.681	396.776	381.878	414.342	481.54	628.137	1222.11	2158.58
611255072642313177	53.17807141534588	-28.010953967067866	True	False	0.0434244	1.0	548.186	41.6479	4.0583	526.718	20.1524	518.214	173.596	339.787	5.57485	558.344	525.851	516.863	False	False	False	5.2266	-0.390099	5.00447	277.818	467.838	523.914	554.811	634.127	1041.61	1670.55	2531.08
611255072642313176	53.17591639246409	-28.013775425567584	True	False	0.000591536	1.0	1110.48	51.9062	5.02765	1065.91	26.2604	887.463	720.07	970.686	0.790164	1095.29	1330.1	1068.99	False	False	False	7.56738	0.807425	11.5429	387.831	816.641	990.156	1062.66	1131.77	1291.51	1800.33	3075.17
611255072642313175	53.17934177956848	-28.011312845208455	True	False	0.00996476	1.0	764.845	37.2241	3.65143	765.196	20.0028	737.402	255.231	388.286	5.44329	761.611	770.757	767.385	False	False	False	5.20637	0.219013	4.94947	414.111	668.644	729.545	771.829	860.665	1163.21	1871.91	3201.4
611255072642313172	53.176618094491474	-28.01272211276504	True	False	0.0164287	1.0	2720.88	35.246	3.43515	2674.23	20.5701	2592.6	745.412	870.112	3.97655	2687.18	2725.59	2673.67	False	False	False	5.1012	-0.328203	5.49309	1401.21	2352.43	2626.45	2673.29	2705.44	3037.69	3535.69	4413.11
611255072642313171	53.1776827869903	-28.012784590849083	True	False	0.00510771	1.0	8631.77	55.5416	5.38972	8129.56	29.7255	6105.33	1502.91	2222.0	0.82965	8258.68	10804.7	8152.94	False	False	False	20.8762	-1.31625	6.09615	2466.93	5560.64	7157.49	7988.4	8593.74	8886.71	9417.6	10596.0
611255072642313165	53.22247074477941	-28.01132744515098	True	False	7.49282e-08	1.0	3565.86	48.7109	4.52078	3535.11	26.4576	2983.18	1526.1	1818.8	4.36035	3488.68	3533.92	3179.08	False	False	False	7.99947	0.2084	5.83063	1436.43	2688.48	3238.73	3460.6	3597.95	3500.75	3756.98	4612.41
611255072642313207	53.15719031397691	-28.01120444790971	True	False	0.00206837	1.0	591.463	43.6672	4.18805	557.342	25.9141	481.11	228.279	362.25	2.90524	647.188	651.724	558.571	False	False	False	10.0677	-1.71456	7.98101	217.43	445.904	527.788	589.399	504.982	716.209	1368.83	2516.79

Below, convert the fluxes (units $nJy$) extracted from the objectTable into AB magnitudes using: $m_{AB} = -2.5log( f_{nJy}) + 31.4$ (see e.g. AB Magnitudes Wikipedia page for the conversion between flux in Jy and AB magnitudes).

Warning: The following cell will produce warnings for invalid value encountered in log10, which happens if the source flux is negative. This occasionally happens from aperture photometry if the included pixels inside the aperture have negative values and can be safely ignored for this example. The log10 will return a NaN which can be filtered out later.

In [11]:

cmodel_mag = -2.50 * np.log10(tab['i_cModelFlux']) + 31.4
sersic_mag = -2.50 * np.log10(tab['i_sersicFlux']) + 31.4
bdFluxD_mag = -2.50 * np.log10(tab['i_bdFluxD']) + 31.4
bdFluxB_mag = -2.50 * np.log10(tab['i_bdFluxB']) + 31.4
ap06_mag = -2.50 * np.log10(tab['i_ap06Flux']) + 31.4
ap09_mag = -2.50 * np.log10(tab['i_ap09Flux']) + 31.4
ap17_mag = -2.50 * np.log10(tab['i_ap17Flux']) + 31.4
ap12_mag = -2.50 * np.log10(tab['i_ap12Flux']) + 31.4
ap35_mag = -2.50 * np.log10(tab['i_ap35Flux']) + 31.4
kron_mag = -2.50 * np.log10(tab['i_kronFlux']) + 31.4
gaap_mag = -2.50 * np.log10(tab['i_gaap1p0Flux']) + 31.4

/tmp/ipykernel_143067/3117612974.py:7: RuntimeWarning: invalid value encountered in log10
  ap17_mag = -2.50 * np.log10(tab['i_ap17Flux']) + 31.4
/tmp/ipykernel_143067/3117612974.py:9: RuntimeWarning: invalid value encountered in log10
  ap35_mag = -2.50 * np.log10(tab['i_ap35Flux']) + 31.4
/tmp/ipykernel_143067/3117612974.py:10: RuntimeWarning: invalid value encountered in log10
  kron_mag = -2.50 * np.log10(tab['i_kronFlux']) + 31.4

Below, store two shape parameters that will be useful for interpreting photometric apertures. First, the Kron Radius, which is a good proxy for the size of the galaxy light profile. Then, the sersic index, which describes the shape of the light profile of galaxies.

In [12]:

i_kronRad = tab['i_kronRad']
sersic_index = tab['sersic_index']

3. Total fluxes¶

The following measurements are available: total fluxes from the sersic model (with shape parameters left free), from cModel (sum of bulge and disk sersic components fitted to the galaxy); and bulge/disk (BD) fluxes with sersic index fixed.

Sersic fluxes¶

This photometric measurement models all galaxies as a single Sersic profile and calculates its total flux according to the best fitting model. More information the sersic profile is available here.

The best fit sersic model is evaluated based on a model to all filters, and then the flux in each filter is calculated by integrating the galaxy light from the best fit model, assuming the best-fit sersic shape parameters.

<f>_sersicFlux    : Flux from the final sersic fit. Forced on <f>-band.
<f>_sersicFluxErr : Uncertainty of <f>_sersicFlux
sersic_no_data_flag   : Failure flag for <f>_sersicFlux

DP1 is the first Rubin data release that contains Sersic fluxes. cModel fluxes (see below) have seen more testing and been in use longer (and originate with SDSS). The LSST pipeline package responsible for sersic fluxes is called multiprofit and documentation is available here.

Bulge/Disk (BD) fluxes¶

This is a photometric measurement similar to using a single Sersic profile, integrated to estimate the flux. But, rather than using the best fit sersic index obtained from modeling the 6 images in each filter, the BD fluxes fixes the sersic index n to either 1 (exponential disk) or 4 (de Vaucouleurs bulge).

<f>_bdFlux*    : Flux from the final sersic fit. Forced on <f>-band. If * = D then this refers to disk and assumes sersic index n=1 (exponential profile). * = B refers to a bulge with de Vaucouleurs profile (n=4).
<f>_bdFlux*Err : Uncertainty of <f>_sersicFlux

Composite Model (CModel) fluxes¶

Similar in nature to those measured for SDSS (information available here) that will be familiar to SDSS users. In short, it is the linear combination of the best fit exponential (disk or D; sersic index n = 1) and de Vaucouleurs (bulge or B; sersic index n = 4) profiles. Thus, cModel is a good compromise between the BD fluxes that assume either a bulge or a disk.

<f>_cModelFlux    : Flux from the final cmodel fit. Forced on <f>-band.
<f>_cModelFluxErr : Uncertainty of <f>_cModelFlux
<f>_cModel_flag   : Failure flag for <f>_cModelFlux

For most cases the "fixed" cModel photometry (i.e. the catalog entries listed above) are preferred to that measured with more degrees of freedom labeled <f>_free_cModelFlux. The difference is that the fixed ones above uses a reference band (recorded as refBand in the schema) where the galaxy is well detected to determine the other parameters, which are then fixed when fitting for the flux in the other bands. The _free_cModelFlux measurement allows all parameters to be free and independent in each filter. The fixed _cModelFlux measurements are generally recommended for galaxy science applications where total flux measurements are needed (e.g. for intrinsic luminosity or mass).

In the Object table, a pre-computed AB magnitude (<f>_cModelMag) also exists for this measurement.

3.1. Comparing total fluxes¶

This section will make several plots that shows how the different photometric measurements compare.

First, compare cModelFlux (two component bulge+disk sersic model flux) to sersicFlux (single sersic model flux with shape parameters free), bdFluxB (sersic with n=4) and bdFluxD (sersic with n=1).

In [13]:

fig, (ax, ax2) = plt.subplots(ncols=2, nrows=1,
                              width_ratios=[0.8, 0.2], figsize=(10, 6))

bins = np.arange(16, 27, 1)

ylims = [-1.2, 1.2]

ax.plot(cmodel_mag, (cmodel_mag-bdFluxD_mag), 's', alpha=.3,
        label='cModel-bdFluxD', color='blue')
x = cmodel_mag
y = (cmodel_mag-bdFluxD_mag)
bin_mean, bin_edge, binnum = binned_statistic(x, y,
                                              statistic='median', bins=bins)
binctr = bin_edge[:-1] + (bin_edge[1:]-bin_edge[:-1])/2
ax.plot(binctr, bin_mean, color='k', lw=3, label=None, zorder=10)
ax.plot(binctr, bin_mean, color='blue', lw=2, label='bin median', zorder=11)

ax.plot(cmodel_mag, (cmodel_mag-bdFluxB_mag), '^', alpha=.3,
        label='cModel-bdFluxB', color='r')
x = cmodel_mag
y = (cmodel_mag-bdFluxB_mag)
bin_mean, bin_edge, binnum = binned_statistic(x, y,
                                              statistic='median', bins=bins)
binctr = bin_edge[:-1] + (bin_edge[1:]-bin_edge[:-1])/2
ax.plot(binctr, bin_mean, color='k', lw=3, label=None, zorder=10)
ax.plot(binctr, bin_mean, color='r', lw=2, label='bin median', zorder=11)


ax.plot(cmodel_mag, (cmodel_mag - sersic_mag), 'o', alpha=.3,
        label='cModel - sersic', color='g')

x = cmodel_mag
y = (cmodel_mag-sersic_mag)
bin_mean, bin_edge, binnum = binned_statistic(x, y,
                                              statistic='median', bins=bins)
binctr = bin_edge[:-1] + (bin_edge[1:]-bin_edge[:-1])/2
ax.plot(binctr, bin_mean, color='k', lw=3, label=None, zorder=10)
ax.plot(binctr, bin_mean, color='g', lw=2, label='bin median', zorder=11)

ax.axhline(0, linestyle='--')
ax.set_xlabel('cModel Magnitude')
ax.set_ylabel('cModel mag - other mag')
ax.set_ylim([-1, 1])
ax.legend()

ax2.hist((cmodel_mag-bdFluxD_mag), orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 40), align='mid',
         histtype="step", color='blue', stacked=True, fill=False, label='bdFluxD')
ax2.hist((cmodel_mag-bdFluxB_mag), orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 40), align='mid',
         histtype="step", color='r', stacked=True, fill=False, label='bdFluxB')
ax2.hist((cmodel_mag-sersic_mag), orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 40), align='mid',
         histtype="step", color='g', stacked=True, fill=False, label='Sersic')

ax2.set_ylim(ylims)
ax2.axhline(0, linestyle='--', color='k')
ax.axhline(0, linestyle='--', color='k')
ax.set_xlabel('i-band AB magnitude [cModel]')
ax.set_ylabel('cModel mag - Other mag')
ax.set_ylim(ylims)

ax.legend()
ax2.legend()

Out[13]:

<matplotlib.legend.Legend at 0x792939287740>

Figure 1: Left panel: a plot comparing the magnitude difference between cModel and dbFluxD (blue), bdFluxB (red) and sersic (green) vs i-band magnitude as measured by cModel. Right panel: a histogram showing the difference in color between cModel and the three other photometric measurements, across all magnitudes. The sersic fluxes are in best agreement with cModel (smallest scatter in right panel) with the bdFluxB returning systematically fainter magnitudes, and bdFluxD returning systematically brighter magnitudes.

Below, explore how the BD fluxes compare to sersic fluxes as a function of the shape of the light profile, sersic index n.

In [14]:

fig, (ax, ax2) = plt.subplots(ncols=2, nrows=1,
                              width_ratios=[0.8, 0.2], figsize=(10, 6))

bins = np.arange(0, 7, 0.25)

ylims = [-1.2, 1.2]

ax.plot(sersic_index, (sersic_mag-bdFluxD_mag), 's', alpha=.3,
        label='cModel-bdFluxD', color='blue')
x = sersic_index
y = (sersic_mag-bdFluxD_mag)
bin_mean, bin_edge, binnum = binned_statistic(x, y,
                                              statistic='median', bins=bins)
binctr = bin_edge[:-1] + (bin_edge[1:]-bin_edge[:-1])/2
ax.plot(binctr, bin_mean, color='k', lw=3, label=None, zorder=10)
ax.plot(binctr, bin_mean, color='blue', lw=2, label='bin median', zorder=11)

ax.plot(sersic_index, (sersic_mag-bdFluxB_mag), '^', alpha=.3,
        label='cModel-bdFluxB', color='r')
x = sersic_index
y = (sersic_mag-bdFluxB_mag)
bin_mean, bin_edge, binnum = binned_statistic(x, y,
                                              statistic='median', bins=bins)
binctr = bin_edge[:-1] + (bin_edge[1:]-bin_edge[:-1])/2
ax.plot(binctr, bin_mean, color='k', lw=3, label=None, zorder=10)
ax.plot(binctr, bin_mean, color='r', lw=2, label='bin median', zorder=11)

ax.axhline(0, linestyle='--')

ax.set_ylabel('sersic mag - BD mag')
ax.set_ylim([-1, 1])
ax.legend()


ax2.hist((cmodel_mag-bdFluxD_mag), orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 40), align='mid',
         histtype="step", color='blue', stacked=True, fill=False, label='bdFluxD')
ax2.hist((cmodel_mag-bdFluxB_mag), orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 40), align='mid',
         histtype="step", color='r', stacked=True, fill=False, label='bdFluxB')

ax2.set_ylim(ylims)
ax2.axhline(0, linestyle='--', color='k')
ax.axhline(0, linestyle='--', color='k')
ax.set_xlabel('Sersic Index n')
ax.set_ylabel('Sersic mag - Other mag')
ax.set_ylim(ylims)
ax.set_xlim([0, 8])
ax.legend()
ax2.legend()

Out[14]:

<matplotlib.legend.Legend at 0x792939205b20>

Fig 2: Comparison of the sersic mag (flux measured with sersic parameters left free) with the bdFlux that is measured with sersic index fixed to either n=1 (D) or n=4 (B) as a function of sersic index n. Fixing the sersic index can inject scatter in the flux relative to leaving it free, but the sersic flux converges on bdFluxD(B) for n=1 and n=4.

4. Apparent fluxes¶

This section explores three types of apparent fluxes: Kron (elliptical apertures that typically includes more than 90% of intrinsic light), and aperture photometry (flux measured inside circles of varying size).

Here, apparent fluxes refers to photometry that are not corrected for flux outside of the measurement aperture (i.e. not corrected to be a total flux). These measurements have their own applications but should not be used to measure mass or luminosity.

Kron fluxes¶

A decent summary of Kron fluxes in the NED documentation. The aperture used for the fluxes is 2.5 x R1 where R1 is the luminosity weighted radius (also called "first moment"; Kron et al. 1980).

<f>_kronFlux      : Flux from Kron Flux algorithm. Measured on <f>-band.
<f>_kronFluxErr   : Uncertainty of <f>_kronFlux.
<f>_kronFlux_flag : Failure flag for <f>_kronFlux.

The Kron radius, <f>_kronRad, is also available. In this case of LSST pipeline output, the Kron flux is not corrected for light that is emitted outside of the Kron aperture. While in many cases it will collect the majority of light, it will not be as accurate as the cModel for science cases requiring total flux.

Aperture fluxes¶

This contains the enclosed flux inside a given aperture (and not corrected to total fluxes using an aperture correction that accounts for the flux falling outside the aperture). Fixed aperture size refers to the aperture radius in pixels.

<f>_ap<pix>Flux     : Flux within <pix>-pixel aperture. Forced on <f>-band.
<f>_ap<pix>FluxErr  : Uncertainty of <f>_ap<pix>Flux.
<f>_ap<pix>FluxFlag : Failure flag for <f>_ap<pix>Flux.

The apertures are 3, 6, 9, 12, 17, 25, 35, 50, and 70 pixels. In the column name, apertures are 03, 06, 09, 12, and so on. While aperture fluxes are not corrected for the loss outside the aperture, if the aperture size is much larger than the galaxy size then it will approximate the total flux of the galaxy. The general application of these measurements are for measuring radial profiles (see Section 4 below).

4.1. Comparing total to apparent fluxes¶

This section will make several plots that compare the cModel flux (which we take as the fiducial total flux, as commonly used for SDSS) to some of the apparent flux measurements. Kron, and aperture photometry are all measures of light within a fixed aperture.

Generally, magnitudes measured using aperture photometry in the LSST pipeline are fainter than those measured from cModel, because the fixed circular aperture systematically underestimates the flux in the galaxy wings (and the lost flux increases as the intrinsic size of the galaxy increases, e.g. as traced by the Kron radius).

First, compare cModel to Kron which should typically enclose 90% of the light.

In [15]:

fig, (ax, ax2) = plt.subplots(ncols=2, nrows=1,
                              width_ratios=[0.8, 0.2], figsize=(10, 6))

bins = np.arange(16, 27, 1)

ylims = [-1.2, 1.2]

ax.plot(cmodel_mag, (cmodel_mag - kron_mag), 's', alpha=.3,
        label='cModel-Kron', color='blue')
x = cmodel_mag
y = (cmodel_mag-kron_mag)
bin_mean, bin_edge, binnum = binned_statistic(x, y,
                                              statistic='median', bins=bins)
binctr = bin_edge[:-1] + (bin_edge[1:]-bin_edge[:-1]) / 2
ax.plot(binctr, bin_mean, color='k', lw=3, label=None, zorder=10)
ax.plot(binctr, bin_mean, color='blue', lw=2, label='bin median', zorder=11)

ax.plot(cmodel_mag, (cmodel_mag-sersic_mag), 'o', alpha=.3,
        label='cModel - sersic', color='r')

x = cmodel_mag
y = (cmodel_mag-sersic_mag)
bin_mean, bin_edge, binnum = binned_statistic(x, y,
                                              statistic='median', bins=bins)
binctr = bin_edge[:-1] + (bin_edge[1:]-bin_edge[:-1])/2
ax.plot(binctr, bin_mean, color='k', lw=3, label=None, zorder=10)
ax.plot(binctr, bin_mean, color='r', lw=2, label='bin median', zorder=11)

ax.axhline(0, linestyle='--')
ax.set_xlabel('cModel Magnitude')
ax.set_ylabel('cModel mag - sersic mag')
ax.set_ylim([-1, 1])
ax.legend()


ax2.hist((cmodel_mag-sersic_mag), orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 50), align='mid',
         histtype="step", color='red', stacked=True, fill=False, label='Sersic')
ax2.hist((cmodel_mag-kron_mag), orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 50), align='mid',
         histtype="step", color='blue', stacked=True, fill=False, label='Kron')

ax2.set_ylim(ylims)
ax2.axhline(0, linestyle='--', color='k')
ax.axhline(0, linestyle='--', color='k')
ax.set_xlabel('Kron Radius [i-band; pixels]')
ax.set_ylabel('cModel mag - Other mag')
ax.set_ylim(ylims)

ax.legend()
ax2.legend()

Out[15]:

<matplotlib.legend.Legend at 0x7929310b7470>

Figure 3: A figure comparing the difference between cModel and Kron magnitudes, compared to the difference between cModel and sersic magnitudes (left panel). Generally, both Kron and sersic the measurements are in comparable agreement with cModel. The right panel shows the histogram of magnitude differences, demonstrating that there are not systematic offsets but slightly higher scatter from Kron with respect to cModel.

Below, explore how circular aperture photometry compares to cModel. Generally, magnitudes measured using aperture photometry in the LSST pipeline are fainter than those measured from cModel, because the fixed circular aperture systematically underestimates the flux in the galaxy wings (and the lost flux increases as the intrinsic size of the galaxy increases, e.g. as traced by the Kron radius).

In [16]:

fig, (ax, ax2) = plt.subplots(ncols=2, nrows=1,
                              width_ratios=[0.8, 0.2], figsize=(10, 6))

bins = np.arange(2, 15, 1)
ylims = [-1.5, 1.5]

ax.plot(i_kronRad, (cmodel_mag-ap06_mag), '^', alpha=.3,
        label='6-pixel aperture', color='red')
ax.plot(i_kronRad, (cmodel_mag-ap09_mag), 's', alpha=.3,
        label='9-pixel aperture', color='orange')
ax.plot(i_kronRad, (cmodel_mag-ap12_mag), 'o', alpha=.3,
        label='12-pixel aperture', color='green')
ax.plot(i_kronRad, (cmodel_mag-ap17_mag), '.', alpha=.3,
        label='17-pixel aperture', color='blue')

ax2.hist((cmodel_mag-ap17_mag), edgecolor='blue', orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 40), align='mid',
         histtype="step", stacked=True, fill=False)
ax2.hist((cmodel_mag-ap12_mag), edgecolor='green', orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 40), align='mid',
         histtype="step", stacked=True, fill=False)
ax2.hist((cmodel_mag-ap09_mag), edgecolor='orange', orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 40), align='mid',
         histtype="step", stacked=True, fill=False)
ax2.hist((cmodel_mag-ap06_mag), edgecolor='red', orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 40), align='mid',
         histtype="step", stacked=True, fill=False)

ax2.set_ylim(ylims)
ax.axhline(0, linestyle='--', color='k')
ax2.axhline(0, linestyle='--', color='k')
ax.set_xlabel('Kron Radius [i-band; pixels]')
ax.set_ylabel('cModel mag - Aperture mag')
ax.set_ylim(ylims)
ax.set_xlim([2, 12])
ax.legend()

Out[16]:

<matplotlib.legend.Legend at 0x79292b7d2ba0>

Figure 4: A comparison of the difference between cModel photometry and aperture photometry measured by the LSST pipelines for four different aperture sizes as a function of galaxy size (as measured using the Kron radius). The left panel shows the scatter plot of difference in photometry vs Kron radius, and the right panel shows a histogram of these values that demonstrate that larger aperture sizes have photometry that is closer to the cModel. The right panel shows histograms of the data in the left panel, where the colors indicate for the same data in each panel.

In [17]:

fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(7, 6))
ylims = [-1.2, 1.2]
bins = np.arange(16, 27, 1)

ax.plot(cmodel_mag, (cmodel_mag-ap06_mag), '^', alpha=.1,
        label='cModel 6-pix aperture', color='red')
x = cmodel_mag
y = (cmodel_mag-ap06_mag)
bin_mean, bin_edge, binnum = binned_statistic(x, y,
                                              statistic='median', bins=bins)
binctr = bin_edge[:-1] + (bin_edge[1:]-bin_edge[:-1])/2
ax.plot(binctr, bin_mean, color='red', lw=2, label='bin median', zorder=10)

ax.plot(cmodel_mag, (cmodel_mag-ap09_mag), 's', alpha=.1,
        label='cModel 9-pix aperture', color='orange')
x = cmodel_mag
y = (cmodel_mag-ap09_mag)
bin_mean, bin_edge, binnum = binned_statistic(x, y,
                                              statistic='median', bins=bins)
binctr = bin_edge[:-1] + (bin_edge[1:]-bin_edge[:-1])/2
ax.plot(binctr, bin_mean, color='orange', lw=2,
        label='bin median', zorder=10)

ax.plot(cmodel_mag, (cmodel_mag-ap12_mag), 'o', alpha=.1,
        label='cModel 12-pix aperture', color='green')
x = cmodel_mag
y = (cmodel_mag-ap12_mag)
bin_mean, bin_edge, binnum = binned_statistic(x, y,
                                              statistic='median', bins=bins)
binctr = bin_edge[:-1] + (bin_edge[1:]-bin_edge[:-1])/2
ax.plot(binctr, bin_mean, color='green', lw=2,
        label='bin median', zorder=10)

ax.plot(cmodel_mag, (cmodel_mag-ap17_mag), '.', alpha=.1,
        label='cModel 17-pix aperture', color='blue')
x = cmodel_mag
y = (cmodel_mag-ap17_mag)
bin_mean, bin_edge, binnum = binned_statistic(x, y,
                                              statistic='median', bins=bins)
binctr = bin_edge[:-1] + (bin_edge[1:]-bin_edge[:-1])/2
ax.plot(binctr, bin_mean, color='blue', lw=2,
        label='bin median', zorder=10)

ax.axhline(0, linestyle='--')
ax.set_xlabel('cModel Magnitude')
ax.set_ylabel('cModel mag - Aperture mag')
ax.set_ylim([-1, 1])
ax.set_xlim([20, 25])
ax.legend()

Out[17]:

<matplotlib.legend.Legend at 0x79292b6e6990>

Figure 5: A similar comparison of the difference between cModel photometry and aperture photometry measured by the LSST pipelines for four different aperture sizes, this time as a function of cModel magnitude. Running median is included.

These two figures show that the aperture photometry typically under-estimates the flux relative to the total flux estimated using cModel. As expected, there is a general trend for larger apertures to get closer to the total flux from cModel for large galaxies (i.e. whose Kron Radius is larger). There is also a general trend for the aperture photometry to be less discrepant at fainter magnitudes, since faint galaxies tend to be small.

4.2. Application of aperture photometry: radial profile¶

A science application for the aperture photometry is easy visualization of the radial profile of galaxies. In the cell below, make this plot for both a large galaxy (first) and a smaller galaxy of similar brightness (second). The query looks for bright galaxies whose cModel magnitude ~ 20 ABmag. Dividing the aperture flux by the surface area of the aperture yields the surface brightness, which can be plotted as a function of radius from the center of the galaxy to compare radial light profiles.

In [18]:

wh = np.where((tab['i_kronRad'] > 20) & (cmodel_mag > 20)
              & (cmodel_mag < 20.5))[0]

indx = 0
arcsec_per_pix = 0.2

rad = np.array([3, 6, 9, 12, 17, 25, 35, 50]) * arcsec_per_pix
area = np.pi * rad**2
profile = np.array([tab['i_ap03Flux'][wh][indx], tab['i_ap06Flux'][wh][indx],
                    tab['i_ap09Flux'][wh][indx], tab['i_ap12Flux'][wh][indx],
                    tab['i_ap17Flux'][wh][indx], tab['i_ap25Flux'][wh][indx],
                    tab['i_ap35Flux'][wh][indx],
                    tab['i_ap50Flux'][wh][indx]]) / area

plt.plot(rad, profile, linestyle=':',
         label='Large Radius R='
         + str(np.round(i_kronRad[wh][indx]*arcsec_per_pix, 2)))
plt.xlabel('Aperture Radius [arcsec]')
plt.ylabel(r'Surface Brightness [nJy arcsec$^{-2}$]')

wh2 = np.where((tab['i_kronRad'] < 8) & (tab['i_kronRad'] > 5)
               & (cmodel_mag > 20) & (cmodel_mag < 20.5))[0]

indx = 0

profile = np.array([tab['i_ap03Flux'][wh2][indx], tab['i_ap06Flux'][wh2][indx],
                    tab['i_ap09Flux'][wh2][indx], tab['i_ap12Flux'][wh2][indx],
                    tab['i_ap17Flux'][wh2][indx], tab['i_ap25Flux'][wh2][indx],
                    tab['i_ap35Flux'][wh2][indx],
                    tab['i_ap50Flux'][wh2][indx]])/area

plt.plot(rad, profile,
         label='Small Radius R='
         + str(round(i_kronRad[wh2][indx]*arcsec_per_pix, 2)))
plt.legend()
plt.yscale('log')

Figure 6: Plot demonstrating the use of aperture photometry to plot the surface brightness profile (as a function of aperture radius) for a galaxy with large Kron radius (green solid) and small Kron radius (blue dotted).

5. Photometry for color¶

This section will explore GaaP fluxes (which are optimized for measuring accurate colors between bands).

GaaP fluxes¶

These are the Gaussian-aperture-and-PSF flux that is defined in Kuijken et al. 2008. The main goal of this method is to measure accurate colors while accounting for the different spatial resolution between filters. This is sometimes achieved in other datasets by convolving all images to the largest PSF, but this process of PSF-matching is computationally very time consuming for large images, thus motivating GaaP as a faster alternative. It is not a measure of total flux in a filter. Several measurement apertures are available.

Aperture

<f>_gaap<ap>Flux    : GaaP flux with <ap> aperture after multiplying the seeing aperture. Forced on <f>-band.
<f>_gaap<ap>FluxErr : Uncertainty of <f>_gaap<ap>Flux.

Where the measurement apertures are 0.5, 0.7, 1.0, 1.5, 2.5, and 3.0 arcseconds. In the column name <ap> appears as 0p5, 0p7, etc. Multiplying by the "seeing aperture" refers to convolving the PSF with a kernel so that the PSF is as if the seeing were 1.15 arcseconds. This has the effect of smearing the images of all filters consistently so that the colors are accurate.

For photometric redshifts, and other analysis where accurate colors are important, it is recommended to start with the GaaP fluxes with 1.0 aperture (optimal aperture was found to not perform as well, and should not be used). The largest aperture gaap3p0 might work better for larger galaxies, but gaap1p0 has better overall performance. Experiment yourself to see how it works for your science case.

5.1. Kron and GaaP comparison¶

In the next cell, compare the cModel instead to the Kron and GaaP measures.

In [19]:

fig, (ax, ax2) = plt.subplots(ncols=2, nrows=1,
                              width_ratios=[0.8, 0.2], figsize=(10, 6))
ylims = [-2, 2]

ax.plot(i_kronRad, (cmodel_mag-gaap_mag), 's', alpha=.3,
        label='gaap1p0', color='orange')

ax.plot(i_kronRad, (cmodel_mag-kron_mag), 'o', alpha=.3,
        label='Kron', color='blue')

ax2.hist((cmodel_mag-gaap_mag), orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 40), align='mid',
         histtype="step", color='orange', stacked=True, fill=False, label='gaap1p0')
ax2.hist((cmodel_mag-kron_mag), orientation="horizontal",
         bins=np.linspace(ylims[0], ylims[1], 40), align='mid',
         histtype="step", color='blue', stacked=True, fill=False, label='Kron')

ax2.set_ylim(ylims)
ax.axhline(0, linestyle='--', color='k')
ax2.axhline(0, linestyle='--', color='k')
ax.set_xlabel('Kron Radius [i-band; pixels]')
ax.set_ylabel('cModel mag - Other mag')
ax.set_ylim(ylims)
ax.set_xlim([2, 15])
ax.legend()
ax2.legend()

Out[19]:

<matplotlib.legend.Legend at 0x792939582ff0>

Figure 7: The left panel figure shows the i-band magnitude difference between cModel and Kron (orange circles) and between cModel and gaap1p0 (blue squares) vs the Kron radius (a proxy for galaxy size) for the galaxies in the query. The dashed line indicates where the two magnitudes would have the same value. The gaap1p0 magnitude always underestimates the flux, but the offset becomes worse for larger galaxies (relative to the fixed aperture). The right panel shows the histogram of the magnitude differences in the left panel, illustrating that while cModel - Kron magnitudes are similar on average (blue histogram) the gaap1p0 systematically underestimates the flux relative to cModel (orange histogram).

5.2. CMD with GaaP¶

This section demonstrates using GaaP photometry to calculate accurate galaxy colors to identify different types of galaxies. First, define magnitudes from g, r, and i band photometry. The second cell will then compare the colors of galaxies that overlap the galaxy cluster with that in the field. In clusters, galaxies tend to be old, red elliptical galaxies and thus exhibit a well defined red sequence in color space.

The earlier query in Section 2 returned galaxies from a blank, "field" location (the ECDFS). These will be dominated by bluer star forming galaxies which are most common in field environments.

Below, add a new query near a known galaxy cluster at redshift z=0.3. This is Abell 360 and sits in the DP1 data from the low ecliptic latitude field.

In [20]:

cluster_ra = 37.83
cluster_dec = 6.98

query = "SELECT obj.objectId, obj.coord_ra, obj.coord_dec, " + \
        "obj.detect_fromBlend, obj.detect_isIsolated, " + \
        "obj.i_blendedness, obj.i_extendedness, " + \
        "obj.i_kronFlux, obj.i_kronFluxErr, obj.i_kronRad, " + \
        "obj.i_cModelFlux, obj.i_cModelFluxErr, obj.i_gaap1p0Flux,  obj.i_gaap3p0Flux, " + \
        "obj.g_gaap1p0Flux, obj.g_gaap3p0Flux, " + \
        "obj.i_kronFlux_flag, obj.i_cModel_flag " + \
        "FROM dp1.Object AS obj " + \
        "WHERE (obj.i_cModelFlux/obj.i_cModelFluxErr > 20) AND " + \
        "(obj.i_extendedness = 1) AND " + \
        "(obj.i_kronFlux_flag = 0) AND (obj.i_cModel_flag = 0) AND " + \
        "CONTAINS(POINT('ICRS', obj.coord_ra, obj.coord_dec), " + \
        "CIRCLE('ICRS',"+str(cluster_ra)+","+str(cluster_dec)+", 0.2)) = 1 "

In [21]:

job = service.submit_job(query)
job.run()
job.wait(phases=['COMPLETED', 'ERROR'])
print('Job phase is', job.phase)

Job phase is COMPLETED

In [22]:

if job.phase == 'ERROR':
    job.raise_if_error()
assert job.phase == 'COMPLETED'

In [23]:

results = job.fetch_result()
tab2 = results.to_table()

First, calculate the magnitudes of galaxies in the 'field' location. Then, calculate the magnitudes for the other filters near the galaxy cluster from the query performed in the cell above. This will enable plotting their colors.

Warning: Like in Section 2, the following cell will produce warnings for invalid value encountered in log10, which happens if the source flux is negative. This happens for a small number of objects and since the goal of the plot is to see the distribution of the majority of sources, the warning can be safely ignored.

In [24]:

g_field_gaap_mag = -2.50 * np.log10(tab['g_gaap1p0Flux']) + 31.4
i_field_gaap_mag = -2.50 * np.log10(tab['i_gaap1p0Flux']) + 31.4
i_field_cmodel_mag = -2.50 * np.log10(tab['i_cModelFlux']) + 31.4
i_cluster_gaap_mag = -2.50 * np.log10(tab2['i_gaap1p0Flux']) + 31.4
g_cluster_gaap_mag = -2.50 * np.log10(tab2['g_gaap1p0Flux']) + 31.4
i_cluster_cmodel_mag = -2.50 * np.log10(tab2['i_cModelFlux']) + 31.4

/tmp/ipykernel_143067/3627765783.py:5: RuntimeWarning: invalid value encountered in log10
  g_cluster_gaap_mag = -2.50 * np.log10(tab2['g_gaap1p0Flux']) + 31.4

In [25]:

fig, (ax, ax1) = plt.subplots(ncols=1, nrows=2, figsize=(10, 6))

ax.plot(i_field_cmodel_mag, (g_field_gaap_mag-i_field_gaap_mag),
        '.', alpha=.1, color='blue', label='Field Galaxies (ECDFS)')
ax.set_xlabel('i-band Magnitude [cModel]')
ax.set_ylabel('g-i color')
ax.set_ylim([-1, 4])
ax.legend()

ax1.plot(i_cluster_cmodel_mag, (g_cluster_gaap_mag-i_cluster_gaap_mag),
         '.', alpha=.1, color='r', label='Cluster Galaxies (Abell 360)')
ax1.set_xlabel('i-band Magnitude [cModel]')
ax1.set_ylabel('g-i color')
ax1.set_ylim([-1, 4])
ax1.legend()

Out[25]:

<matplotlib.legend.Legend at 0x7929310a7470>

Figure 8: The g − i vs. i color-magnitude diagram for galaxies selected in the queries. Top panel shows the galaxies selected from in a random field that does not contain a galaxy cluster (ECDFS). The bottom panel shows galaxies from a field with a galaxy cluster (A360). The cluster galaxies appear as a "red sequence" with red i-g colors, because the Balmer / 4000 Angstrom break spectral feature that traces older stars sits between the bands.

A very nice red sequence appears from the red, old galaxies in the cluster!

6. Exercise for the learner¶

Compare the <f>_free_cModelFlux measurements to <f>_cModelFlux in the filters that are not the reference band where the <f>_cModelFlux was measured (i.e. refBand). Investigate how leaving the cModel measurements free differs from the one measured with parameters fixed to the refBand, as a function of decreasing signal to noise. As an additional exercise, check how the signal to noise in colors measured using <f>_gaap1p0Flux values compare to those measured with the larger 3.0" aperture, <f>_gaap3p0Flux, where the larger aperture may increase the noise.