The dependence of assembly bias on the cosmic web

For low-mass haloes, the physical origins of halo assembly bias have been linked to the slowdown of accretion due to tidal forces, which are expected to be more dominant in some cosmic-web environments as compared to others. In this work, we use publicly available data from the application of the Discrete Persistent Structures Extractor (DisPerSE) to the IllustrisTNG magnetohydrodynamical simulation to investigate the dependence of the related galaxy assembly bias effect on the cosmic web. We first show that, at fixed halo mass, the galaxy population displays significant low-mass secondary bias when split by distance to DisPerSE critical points representing nodes ($d_{\rm node}$), filaments ($d_{\rm skel}$), and saddles ($d_{\rm sadd}$), with objects closer to these features being more tightly clustered. The secondary bias produced by some of these parameters exceeds the assembly bias signal considerably at some mass ranges, particularly for $d_{\rm sadd}$. We also demonstrate that the assembly bias signal is reduced significantly when clustering is conditioned to galaxies being close or far from these critical points. The maximum attenuation is measured for galaxies close to saddle points, where less than 35$\%$ of the signal remains. Conversely, objects near voids preserve a fairly pristine effect (almost 85$\%$ of the signal). Our analysis confirms the important role played by the tidal field in shaping assembly bias, but they are also consistent with the signal being the result of different physical mechanisms. Our work introduces some new aspects of secondary bias where the predictions from hydrodynamical simulations can be directly tested with observational data.Comment: 12 pages, 7 figures. Submitted to MNRAS, comments welcom

The dependence of halo bias on age, concentration and spin

Halo bias is the main link between the matter distribution and dark matter halos. In its simplest form, halo bias is determined by halo mass, but there are known additional dependencies on other halo properties which are of consequence for accurate modeling of galaxy clustering. Here we present the most precise measurement of these secondary-bias dependencies on halo age, concentration, and spin, for a wide range of halo masses spanning from 10$^{10.7}$ to 10$^{14.7}$ $h^{-1}$ M$_{\odot}$. At the high-mass end, we find no strong evidence of assembly bias for masses above M$_{vir}$ $\sim10^{14}$ $h^{-1}$ M$_{\odot}$. Secondary bias exists, however, for halo concentration and spin, up to cluster-size halos, in agreement with previous findings. For halo spin, we report, for the first time, two different regimes: above M$_{vir}\sim$10$^{11.5}$ $h^{-1}$ M$_{\odot}$, halos with larger values of spin have larger bias, at fixed mass, with the effect reaching almost a factor 2. This trend reverses below this characteristic mass. In addition to these results, we test, for the first time, the performance of a multi-tracer method for the determination of the relative bias between different subsets of halos. We show that this method increases significantly the signal-to-noise of the secondary-bias measurement as compared to a traditional approach. This analysis serves as the basis for follow-up applications of our multi-tracer method to real data.Comment: 11 pages, 6 figures, submitted to MNRA

The galaxy size - halo mass scaling relations and clustering properties of central and satellite galaxies

In this work, we combine size and stellar mass measurements from the Sloan Digital Sky Server (SDSS) with the group finder algorithm of Rodriguez \& Merch\'an in order to determine the stellar and halo mass -- size relations of central and satellite galaxies separately. We show that, while central and satellite galaxies display similar stellar mass -- size relations, their halo mass -- size relations differ significantly. As expected, more massive haloes tend to host larger central galaxies. However, the size of satellite galaxies depends only slightly on halo virial mass. We show that these results are compatible with a remarkably simple model in which the size of central and satellite galaxies scales as the cubic root of their host halo mass, with the normalization for satellites being $\sim$ 30 \% smaller than that for central galaxies, which can be attributed to tidal stripping. We further check that our measurements are in excellent agreement with predictions from the IllustrisTNG hydrodynamical simulation. In the second part of this paper, we analyse how the clustering properties of central and satellite galaxies depend on their size. We demonstrate that, independently of the stellar mass threshold adopted, smaller galaxies are more tightly clustered than larger galaxies when either the entire sample or only satellites are considered. The opposite trend is observed on large scales when the size split is performed for the central galaxies alone. Our results place significant constraints for halo-galaxy connection models that link galaxy size with the properties of their hosting haloes.Comment: 15 pages, 12 figures. Accepted for publication in MNRA

High-fidelity reproduction of central galaxy joint distributions with Neural Networks

The relationship between galaxies and haloes is central to the description of galaxy formation, and a fundamental step towards extracting precise cosmological information from galaxy maps. However, this connection involves several complex processes that are interconnected. Machine Learning methods are flexible tools that can learn complex correlations between a large number of features, but are traditionally designed as deterministic estimators. In this work, we use the IllustrisTNG300-1 simulation and apply neural networks in a binning classification scheme to predict probability distributions of central galaxy properties, namely stellar mass, colour, specific star formation rate, and radius, using as input features the halo mass, concentration, spin, age, and the overdensity on a scale of 3 $h^{-1}$ Mpc. The model captures the intrinsic scatter in the relation between halo and galaxy properties, and can thus be used to quantify the uncertainties related to the stochasticity of the galaxy properties with respect to the halo properties. In particular, with our proposed method, one can define and accurately reproduce the properties of the different galaxy populations in great detail. We demonstrate the power of this tool by directly comparing traditional single-point estimators and the predicted joint probability distributions, and also by computing the power spectrum of a large number of tracers defined on the basis of the predicted colour-stellar mass diagram. We show that the neural networks reproduce clustering statistics of the individual galaxy populations with excellent precision and accuracy.Comment: 12 pages, 7 figure