The dependence of halo bias on age, concentration, and spin

Abstract

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 . At the high-mass end, we find no strong evidence of assembly bias for masses above M vir ∼ 10 14 h −1 M . 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 ∼10 11.5 h −1 M , 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.

Mimicking the halo-galaxy connection using machine learning

“…The fact that we are able to predict the TNG300 clustering with precision, even when the sample is split in multiple ways, serves as a motivation to explore in more detail the effect of galaxy assembly bias, i.e., the dependence of the properties and clustering of galaxies on halo properties beyond halo mass 5 (see, e.g., Sheth & Tormen 2004;Gao et al 2005;Dalal et al 2008;Borzyszkowski et al 2017;Salcedo et al 2018;Sato-Polito et al 2019;Montero-Dorta et al 2020b;Tucci et al 2021;Montero-Dorta et al 2021b). On the cosmology front, as it has been already shown in the literature, ML can be used to generate high-fidelity mocks for upcoming surveys by applying the trained machine to N-body numerical simulations (e.g., Xu et al 2013;Zhang et al 2019;Yip et al 2019;Alves de Oliveira et al 2020;Kasmanoff et al 2020).…”

The Multi-Tracer Optimal Estimator applied to VIPERS

“…From the perspective of the halo-galaxy connection, improving on our ability to measure galaxy biases is advantageous in the context of assembly bias studies (see, e.g., Miyatake et al 2016;Lin et al 2016;Montero-Dorta et al 2017;Niemiec et al 2018). In Sato-Polito et al (2019) we show how applying the MTOE to multiple subsets of haloes increases the signal-to-noise of the secondary bias (i.e., "halo assembly bias") measurement to a level that can only be achieved if the underlying dark-matter density field of the simulation is known. This convenient "trick" is not attainable with real data, therefore the usefulness of MTOE.…”

“…We have successfully applied MTOE to the determination of the power spectra and the ratios of biases for different subsets of haloes in the context of the study of halo assembly bias (or more generally, secondary bias). In Sato-Polito et al (2019), we show that MTOE provides a significant improvement in the signal-to-noise of the secondary-bias measurement. In Sato-Polito et al (2019), each subset of haloes is regarded as a different tracer, and a total of 32 tracers per simulation box are considered.…”

On the influence of halo mass accretion history on galaxy properties and assembly bias

“…Among these properties, halo mass is responsible for the primary dependence: more massive haloes are more tightly clustered than less massive haloes, as expected from the Λ-cold dark matter (Λ-CDM) structure formation formalism (e.g., Press & Schechter 1974;Sheth & Tormen 2002). More recently, a number of additional secondary dependencies at fixed halo mass have been unveiled (see, e.g., Sheth & Tormen 2004;Gao et al 2005;Wechsler et al 2006;Gao & White 2007;Angulo et al 2008;Li et al 2008;Lazeyras et al 2017;Salcedo et al 2018;Han et al 2019;Mao et al 2018;Sato-Polito et al 2019;Johnson et al 2019;Ramakrishnan et al 2019;Montero-Dorta et al 2020b;Tucci et al 2021). Among these dependencies, the one that has drawn more attention is precisely the dependence on the assembly history of haloes, an effect dubbed halo assembly bias.…”

“…Among these dependencies, the one that has drawn more attention is precisely the dependence on the assembly history of haloes, an effect dubbed halo assembly bias. Typically lower mass haloes that assemble a significant portion of their mass early on are more tightly clustered than haloes that form at later times, with the signal progressively vanishing towards the high-mass end (e.g., Gao et al 2005;Sato-Polito et al 2019).…”