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Register a new userGalaxy And Mass Assembly (GAMA) Blended Spectra Catalog: Strong Galaxy-Galaxy Lens and Occulting Galaxy Pair Candidates
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We present the catalogue of blended galaxy spectra from the Galaxy And Mass Assembly (GAMA) survey. These are cases where light from two galaxies are significantly detected in a single GAMA fibre. Galaxy pairs identified from their blended spectrum fall into two principal classes: they are either strong lenses, a passive galaxy lensing an emission-line galaxy; or occulting galaxies, serendipitous overlaps of two galaxies, of any type. Blended spectra can thus be used to reliably identify strong lenses for follow-up observations (high resolution imaging) and occulting pairs, especially those that are a late-type partly obscuring an early-type galaxy which are of interest for the study of dust content of spiral and irregular galaxies. The GAMA survey setup and its autoz automated redshift determination were used to identify candidate blended galaxy spectra from the cross-correlation peaks. We identify 280 blended spectra with a minimum velocity separation of 600 km/s, of which 104 are lens pair candidates, 71 emission-line-passive pairs, 78 are pairs of emission-line galaxies and and 27 are pairs of galaxies with passive spectra. We have visually inspected the candidates in the Sloan Digital Sky Survey (SDSS) and Kilo Degree Survey (KiDS) images. Many blended objects are ellipticals with blue fuzz (Ef in our classification). These latter Ef classifications are candidates for possible strong lenses, massive ellipticals with an emission-line galaxy in one or more lensed images. The GAMA lens and occulting galaxy candidate samples are similar in size to those identified in the entire SDSS. This blended spectrum sample stands as a testament of the power of this highly complete, second-largest spectroscopic survey in existence and offers the possibility to expand e.g., strong gravitational lens surveys.
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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.
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.
We report an expanded sample of visual morphological classifications from the Galaxy and Mass Assembly (GAMA) survey phase two, which now includes 7,556 objects (previously 3,727 in phase one). We define a local (z <0.06) sample and classify galaxies into E, S0-Sa, SB0-SBa, Sab-Scd, SBab-SBcd, Sd-Irr, and little blue spheroid types. Using these updated classifications, we derive stellar mass function fits to individual galaxy populations divided both by morphological class and more general spheroid- or disk-dominated categories with a lower mass limit of log(Mstar/Msun) = 8 (one dex below earlier morphological mass function determinations). We find that all individual morphological classes and the combined spheroid-/bulge-dominated classes are well described by single Schechter stellar mass function forms. We find that the total stellar mass densities for individual galaxy populations and for the entire galaxy population are bounded within our stellar mass limits and derive an estimated total stellar mass density of rho_star = 2.5 x 10^8 Msun Mpc^-3 h_0.7, which corresponds to an approximately 4% fraction of baryons found in stars. The mass contributions to this total stellar mass density by galaxies that are dominated by spheroidal components (E and S0-Sa classes) and by disk components (Sab-Scd and Sd-Irr classes) are approximately 70% and 30%, respectively.
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.
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