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Register a new userGalaxy And Mass Assembly (GAMA): Assimilation of KiDS into the GAMA database
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The Galaxy And Mass Assembly Survey (GAMA) covers five fields with highly complete spectroscopic coverage ($>95$ per cent) to intermediate depths ($r<19.8$ or $i < 19.0$ mag), and collectively spans 250 square degrees of Equatorial or Southern sky. Four of the GAMA fields (G09, G12, G15 and G23) reside in the ESO VST KiDS and ESO VISTA VIKING survey footprints, which combined with our GALEX, WISE and Herschel data provide deep uniform imaging in the $FUV,NUV,ugriZYJHK_s,W1,W2,W3,W4,P100,P160,S250,S350,S500$ bands. Following the release of KiDS DR4, we describe the process by which we ingest the KiDS data into GAMA (replacing the SDSS data previously used for G09, G12 and G15), and redefine our core optical and near-IR catalogues to provide a complete and homogeneous dataset. The source extraction and analysis is based on the new ProFound image analysis package, providing matched-segment photometry across all bands. The data are classified into stars, galaxies, artefacts, and ambiguous objects, and objects are linked to the GAMA spectroscopic target catalogue. Additionally, a new technique is employed utilising ProFound to extract photometry in the unresolved MIR-FIR regime. The catalogues including the full FUV-FIR photometry are described and will be fully available as part of GAMA DR4. They are intended for both standalone science, selection for targeted follow-up with 4MOST, as well as an accompaniment to the upcoming and ongoing radio arrays now studying the GAMA $23^h$ field.
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Strong gravitational lenses are a rare and instructive type of astronomical object. Identification has long relied on serendipity, but different strategies -- such as mixed spectroscopy of multiple galaxies along the line of sight, machine learning algorithms, and citizen science -- have been employed to identify these objects as new imaging surveys become available. We report on the comparison between spectroscopic, machine learning, and citizen science identification of galaxy-galaxy lens candidates from independently constructed lens catalogs in the common survey area of the equatorial fields of the GAMA survey. In these, we have the opportunity to compare high-completeness spectroscopic identifications against high-fidelity imaging from the Kilo Degree Survey (KiDS) used for both machine learning and citizen science lens searches. We find that the three methods -- spectroscopy, machine learning, and citizen science -- identify 47, 47, and 13 candidates respectively in the 180 square degrees surveyed. These identifications barely overlap, with only two identified by both citizen science and machine learning. We have traced this discrepancy to inherent differences in the selection functions of each of the three methods, either within their parent samples (i.e. citizen science focuses on low-redshift) or inherent to the method (i.e. machine learning is limited by its training sample and prefers well-separated features, while spectroscopy requires sufficient flux from lensed features to lie within the fiber). These differences manifest as separate samples in estimated Einstein radius, lens stellar mass, and lens redshift. The combined sample implies a lens candidate sky-density $sim0.59$ deg$^{-2}$ and can inform the construction of a training set spanning a wider mass-redshift space.
Using the complete GAMA-I survey covering ~142 sq. deg. to r=19.4, of which ~47 sq. deg. is to r=19.8, we create the GAMA-I galaxy group catalogue (G3Cv1), generated using a friends-of-friends (FoF) based grouping algorithm. Our algorithm has been tested extensively on one family of mock GAMA lightcones, constructed from Lambda-CDM N-body simulations populated with semi-analytic galaxies. Recovered group properties are robust to the effects of interlopers and are median unbiased in the most important respects. G3Cv1 contains 14,388 galaxy groups (with multiplicity >= 2$), including 44,186 galaxies out of a possible 110,192 galaxies, implying ~40% of all galaxies are assigned to a group. The similarities of the mock group catalogues and G3Cv1 are multiple: global characteristics are in general well recovered. However, we do find a noticeable deficit in the number of high multiplicity groups in GAMA compared to the mocks. Additionally, despite exceptionally good local spatial completeness, G3Cv1 contains significantly fewer compact groups with 5 or more members, this effect becoming most evident for high multiplicity systems. These two differences are most likely due to limitations in the physics included of the current GAMA lightcone mock. Further studies using a variety of galaxy formation models are required to confirm their exact origin.
We measure the projected 2-point correlation function of galaxies in the 180 deg$^2$ equatorial regions of the GAMA II survey, for four different redshift slices between z = 0.0 and z=0.5. To do this we further develop the Cole (2011) method of producing suitable random catalogues for the calculation of correlation functions. We find that more r-band luminous, more massive and redder galaxies are more clustered. We also find that red galaxies have stronger clustering on scales less than ~3 $h^{-1}$ Mpc. We compare to two differe
We explore the clustering of galaxy groups in the Galaxy and Mass Assembly (GAMA) survey to investigate the dependence of group bias and profile on separation scale and group mass. Due to the inherent uncertainty in estimating the group selection function, and hence the group auto-correlation function, we instead measure the projected galaxy--group cross-correlation function. We find that the group profile has a strong dependence on scale and group mass on scales $r_bot lesssim 1 h^{-1} mathrm{Mpc}$. We also find evidence that the most massive groups live in extended, overdense, structures. In the first application of marked clustering statistics to groups, we find that group-mass marked clustering peaks on scales comparable to the typical group radius of $r_bot approx 0.5 h^{-1} mathrm{Mpc}$. While massive galaxies are associated with massive groups, the marked statistics show no indication of galaxy mass segregation within groups. We show similar results from the IllustrisTNG simulations and the L-Galaxies model, although L-Galaxies shows an enhanced bias and galaxy mass dependence on small scales.
Recent work has suggested that mid-IR wavelengths are optimal for estimating the mass-to-light ratios of stellar populations and hence the stellar masses of galaxies. We compare stellar masses deduced from spectral energy distribution (SED) models, fitted to multi-wavelength optical-NIR photometry, to luminosities derived from {it WISE} photometry in the $W1$ and $W2$ bands at 3.6 and 4.5$mu$m for non-star forming galaxies. The SED derived masses for a carefully selected sample of low redshift ($z le 0.15$) passive galaxies agree with the prediction from stellar population synthesis models that $M_*/L_{W1} simeq 0.6$ for all such galaxies, independent of other stellar population parameters. The small scatter between masses predicted from the optical SED and from the {it WISE} measurements implies that random errors (as opposed to systematic ones such as the use of different IMFs) are smaller than previous, deliberately conservative, estimates for the SED fits. This test is subtly different from simultaneously fitting at a wide range of optical and mid-IR wavelengths, which may just generate a compromise fit: we are directly checking that the best fit model to the optical data generates an SED whose $M_*/L_{W1}$ is also consistent with separate mid-IR data. We confirm that for passive low redshift galaxies a fixed $M_*/L_{W1} = 0.65$ can generate masses at least as accurate as those obtained from more complex methods. Going beyond the mean value, in agreement with expectations from the models, we see a modest change in $M_*/L_{W1}$ with SED fitted stellar population age but an insignificant one with metallicity.
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