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We explore the radial distribution of satellite galaxies in groups in the Galaxy and Mass Assembly (GAMA) survey and the IllustrisTNG simulations. Considering groups with masses $12.0 \leq \log_{10} (\mathcal{M}_h / h^{-1} \mathrm{M}_{\odot}) < 14.8$ at $z<0.267$, we find a good agreement between GAMA and a sample of TNG300 groups and galaxies designed to match the GAMA selection. Both display a flat profile in the centre of groups, followed by a decline that becomes steeper towards the group edge, and normalised profiles show no dependence on group mass. Using matched satellites from TNG and dark matter-only TNG-Dark runs we investigate the effect of baryons on satellite radial location. At $z=0$, we find that the matched subhaloes from the TNG-Dark runs display a much flatter radial profile: namely, satellites selected above a minimum stellar mass exhibit both smaller halo-centric distances and longer survival times in the full-physics simulations compared to their dark-matter only analogues. We then divide the TNG satellites into those which possess TNG-Dark counterparts and those which do not, and develop models for the radial positions of each. We find the satellites with TNG-Dark counterparts are displaced towards the halo centre in the full-physics simulations, and this difference has a power-law behaviour with radius. For the `orphan' galaxies without TNG-Dark counterparts, we consider the shape of their radial distribution and provide a model for their motion over time, which can be used to improve the treatment of satellite galaxies in semi-analytic and semi-empirical models of galaxy formation., Comment: 20 pages, 22 figures. MNRAS, version after referee report

More often than not a lunch time conversation will veer off into bizarre and uncharted territories. In rare instances these frontiers of conversation can lead to deep insights about the Universe we inhabit. This paper details the fruits of one such conversation. In this paper we will answer the question: How many cows do you need to form a planetoid entirely comprised of cows, which will support a methane atmoosphere produced by the planetary herd? We will not only present the necessary assumptions and theory underpinning the cow-culations, but also present a thorough (and rather robust) discussion of the viability of, and implications for accomplishing, such a feat.

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., Comment: 18 pages, 16 figures. Accepted by MNRAS

How do galaxy properties (such as stellar mass, luminosity, star formation rate, and morphology) and their evolution depend on the mass of their host dark matter halo? Using the Galaxy and Mass Assembly (GAMA) group catalogue, we address this question by exploring the dependence on host halo mass of the luminosity function (LF) and stellar mass function (SMF) for grouped galaxies subdivided by colour, morphology and central/satellite. We find that spheroidal galaxies in particular dominate the bright and massive ends of the LF and SMF, respectively. More massive haloes host more massive and more luminous central galaxies. The satellite LF and SMF respectively show a systematic brightening of characteristic magnitude, and increase in characteristic mass, with increasing halo mass. In contrast to some previous results, the faint-end and low-mass slopes show little systematic dependence on halo mass. Semi-analytic models and simulations show similar or enhanced dependence of central mass and luminosity on halo mass. Faint and low-mass simulated satellite galaxies are remarkably independent of halo mass, but the most massive satellites are more common in more massive groups. In the first investigation of low-redshift LF and SMF evolution in group environments, we find that the red/blue ratio of galaxies in groups has increased since redshift $z \approx 0.3$ relative to the field population. This observation strongly suggests that quenching of star formation in galaxies as they are accreted into galaxy groups is a significant and ongoing process., Comment: 22 pages, 18 figures, MNRAS in press

In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy's globular cluster population $N_{\rm GC}$ is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between $N_{\rm GC}$ and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion, and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous), and allows modelling the population of globular clusters on their natural scale as a non-negative integer variable. Prediction intervals of 99% around the trend for expected $N_{\rm GC}$comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35% smaller than other types with similar brightness., Comment: 14 pages, 12 figures. Accepted for publication in MNRAS

Revealing hidden patterns in astronomical data is often the path to fundamental scientific breakthroughs; meanwhile the complexity of scientific inquiry increases as more subtle relationships are sought. Contemporary data analysis problems often elude the capabilities of classical statistical techniques, suggesting the use of cutting edge statistical methods. In this light, astronomers have overlooked a whole family of statistical techniques for exploratory data analysis and robust regression, the so-called Generalized Linear Models (GLMs). In this paper -- the first in a series aimed at illustrating the power of these methods in astronomical applications -- we elucidate the potential of a particular class of GLMs for handling binary/binomial data, the so-called logit and probit regression techniques, from both a maximum likelihood and a Bayesian perspective. As a case in point, we present the use of these GLMs to explore the conditions of star formation activity and metal enrichment in primordial minihaloes from cosmological hydro-simulations including detailed chemistry, gas physics, and stellar feedback. We predict that for a dark mini-halo with metallicity $\approx 1.3 \times 10^{-4} Z_{\bigodot}$, an increase of $1.2 \times 10^{-2}$ in the gas molecular fraction, increases the probability of star formation occurrence by a factor of 75%. Finally, we highlight the use of receiver operating characteristic curves as a diagnostic for binary classifiers, and ultimately we use these to demonstrate the competitive predictive performance of GLMs against the popular technique of artificial neural networks., Comment: 20 pages, 10 figures, 3 tables, accepted for publication in Astronomy and Computing