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Register a new userReproducible $k$-means clustering in galaxy feature data from the GAMA survey
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A fundamental bimodality of galaxies in the local Universe is apparent in many of the features used to describe them. Multiple sub-populations exist within this framework, each representing galaxies following distinct evolutionary pathways. Accurately identifying and characterising these sub-populations requires that a large number of galaxy features be analysed simultaneously. Future galaxy surveys such as LSST and Euclid will yield data volumes for which traditional approaches to galaxy classification will become unfeasible. To address this, we apply a robust $k$-means unsupervised clustering method to feature data derived from a sample of 7338 local-Universe galaxies selected from the Galaxy And Mass Assembly (GAMA) survey. This allows us to partition our sample into $k$ clusters without the need for training on pre-labelled data, facilitating a full census of our high dimensionality feature space and guarding against stochastic effects. We find that the local galaxy population natively splits into $2$, $3$, $5$ and a maximum of $6$ sub-populations, with each corresponding to a distinct ongoing evolutionary mechanism. Notably, the impact of the local environment appears strongly linked with the evolution of low-mass ($M_{*} < 10^{10}$ M$_{odot}$) galaxies, with more massive systems appearing to evolve more passively from the blue cloud onto the red sequence. With a typical run time of $sim3$ minutes per value of $k$ for our galaxy sample, we show how $k$-means unsupervised clustering is an ideal tool for future analysis of large extragalactic datasets, being scalable, adaptable, and providing crucial insight into the fundamental properties of the local galaxy population.
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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 present the Large Area Radio Galaxy Evolution Spectroscopic Survey (LARGESS), a spectroscopic catalogue of radio sources designed to include the full range of radio AGN populations out to redshift z = 0.8. The catalogue covers roughly 800 square degrees of sky, and provides optical identifications for 19,179 radio sources from the 1.4 GHz Faint Images of the Radio Sky at Twenty-cm (FIRST) survey down to an optical magnitude limit of i_mod < 20.5 in Sloan Digital Sky Survey (SDSS) images. Both galaxies and point-like objects are included, and no colour cuts are applied. In collaboration with the WiggleZ and Galaxy And Mass Assembly (GAMA) spectroscopic survey teams, we have obtained new spectra for over 5,000 objects in the LARGESS sample. Combining these new spectra with data from earlier surveys provides spectroscopic data for 12,329 radio sources in the survey area, of which 10,856 have reliable redshifts. 85% of the LARGESS spectroscopic sample are radio AGN (median redshift z = 0.44), and 15% are nearby star-forming galaxies (median z = 0.08). Low-excitation radio galaxies (LERGs) comprise the majority (83%) of LARGESS radio AGN at z < 0.8, with 12% being high-excitation radio galaxies (HERGs) and 5% radio-loud QSOs. Unlike the more homogeneous LERG and QSO sub-populations, HERGs are a heterogeneous class of objects with relatively blue optical colours and a wide dispersion in mid-infrared colours. This is consistent with a picture in which most HERGs are hosted by galaxies with recent or ongoing star formation as well as a classical accretion disk.
The Galaxy And Mass Assembly (GAMA) survey is one of the largest contemporary spectroscopic surveys of low-redshift galaxies. Covering an area of ~286 deg^2 (split among five survey regions) down to a limiting magnitude of r < 19.8 mag, we have collected spectra and reliable redshifts for 238,000 objects using the AAOmega spectrograph on the Anglo-Australian Telescope. In addition, we have assembled imaging data from a number of independent surveys in order to generate photometry spanning the wavelength range 1 nm - 1 m. Here we report on the recently completed spectroscopic survey and present a series of diagnostics to assess its final state and the quality of the redshift data. We also describe a number of survey aspects and procedures, or updates thereof, including changes to the input catalogue, redshifting and re-redshifting, and the derivation of ultraviolet, optical and near-infrared photometry. Finally, we present the second public release of GAMA data. In this release we provide input catalogue and targeting information, spectra, redshifts, ultraviolet, optical and near-infrared photometry, single-component Sersic fits, stellar masses, H$alpha$-derived star formation rates, environment information, and group properties for all galaxies with r < 19.0 mag in two of our survey regions, and for all galaxies with r < 19.4 mag in a third region (72,225 objects in total). The database serving these data is available at http://www.gama-survey.org/.
We present predictions for the galaxy-galaxy lensing profile from the EAGLE hydrodynamical cosmological simulation at redshift z=0.18, in the spatial range 0.02 < R/(Mpc/h) < 2, and for five logarithmically equi-spaced stellar mass bins in the range 10.3 < $log_{10}$(Mstar/ $M_{odot}$) < 11.8. We compare these excess surface density profiles to the observed signal from background galaxies imaged by the Kilo Degree Survey around spectroscopically confirmed foreground galaxies from the GAMA survey. Exploiting the GAMA galaxy group catalogue, the profiles of central and satellite galaxies are computed separately for groups with at least five members to minimise contamination. EAGLE predictions are in broad agreement with the observed profiles for both central and satellite galaxies, although the signal is underestimated at R$approx$0.5-2 Mpc/h for the highest stellar mass bins. When central and satellite galaxies are considered simultaneously, agreement is found only when the selection function of lens galaxies is taken into account in detail. Specifically, in the case of GAMA galaxies, it is crucial to account for the variation of the fraction of satellite galaxies in bins of stellar mass induced by the flux-limited nature of the survey. We report the inferred stellar-to-halo mass relation and we find good agreement with recent published results. We note how the precision of the galaxy-galaxy lensing profiles in the simulation holds the potential to constrain fine-grained aspects of the galaxy-dark matter connection.
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