Elucidating Galaxy Assembly Bias in SDSS

Salcedo, Andrés N.; Zhao, Ying; Zhang, Youcai; Wang, Huiyuan; Yang, Xiaohu; Wu, Yiheng; Jing, Y. P.; Mo, H. J.; Weinberg, David H.

doi:10.48550/arxiv.2010.04176

Cited by 19 publications

(24 citation statements)

References 91 publications

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“…Analyses of projected galaxy two-point correlation functions (2PCFs) in the Sloan Digital Sky Survey (SDSS) show no significant evidence of galaxy assembly bias (e.g. Lin et al 2016;Vakili & Hahn 2019;Zentner et al 2019;Salcedo et al 2020). The reported strong assembly bias effect in the projected 2PCFs of massive clusters (Miyatake et al 2016) appears to be an artefact caused by an insufficient control of projection effects (Zu et al 2017).…”

Section: Introductionmentioning

confidence: 99%

On the Constraints of Galaxy Assembly Bias in Velocity Space

McCarthy,

Zheng,

Guo

et al. 2021

Preprint

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If the formation of central galaxies in dark matter haloes traces the assembly history of their host haloes, in haloes of fixed mass, central galaxy clustering may show dependence on properties indicating their formation history. Such a galaxy assembly bias effect has been investigated by Lin et al. (2016), with samples of central galaxies constructed in haloes of similar mass and with mean halo mass verified by galaxy lensing measurements, and no significant evidence of assembly bias is found from the analysis of the projected two-point correlation functions of early-and late-forming central galaxies. In this work, we extend the the investigation of assembly bias effect from real space to redshift (velocity) space, with an extended construction of early-and late-forming galaxies. We carry out halo occupation distribution modelling to constrain the galaxy-halo connection to see whether there is any sign of the effect of assembly bias. We find largely consistent host halo mass for early-and late-forming central galaxies, corroborated by lensing measurements. The central velocity bias parameters, which are supposed to characterise the mutual relaxation between central galaxies and their host haloes, are inferred to overlap between early-and lateforming central galaxies. However, we find a large amplitude of velocity bias for early-forming central galaxies (e.g. with central galaxies moving at more than 50% that of dark matter velocity dispersion inside host haloes), which may signal an assembly bias effect. A large sample with two-point correlation functions and other clustering measurements and improved modelling will help reach a conclusive result.

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Section: Introductionmentioning

confidence: 99%

On the Constraints of Galaxy Assembly Bias in Velocity Space

McCarthy,

Zheng,

Guo

et al. 2021

Preprint

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“…Halo concentration, for example, has been a popular secondary parameter Croton et al (2007a); Vakili & Hahn (2019); Kobayashi et al (2020); Wechsler & Tinker (2018); Paranjape et al (2015), although numerous recent studies have shown that the environment of the halos plays a significant role in determining the galaxy distribution (Hadzhiyska et al 2020a,c;Hadzhiyska et al 2021;Yuan et al 2020;McEwen & Weinberg 2018;Xu et al 2020;Salcedo et al 2020b,c;Abbas & Sheth 2007;Pujol & Gaztañaga 2014). There also have been multiple prescriptions for including the halo environment in the HOD model (McEwen & Weinberg 2018;Xu et al 2020;Salcedo et al 2020b,c;Yuan et al 2020;Hadzhiyska et al 2020b;Salcedo et al 2020a).…”

Section: Introductionmentioning

confidence: 99%

Modeling the galaxy-halo connection with machine learning

Delgado¹,

Wadekar²,

Hadzhiyska³

et al. 2021

Preprint

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To extract information from the clustering of galaxies on non-linear scales, we need to model the connection between galaxies and halos accurately and in a flexible manner. Standard halo occupation distribution (HOD) models make the assumption that the galaxy occupation in a halo is a function of only its mass, however, in reality, the occupation can depend on various other parameters including halo concentration, assembly history, environment, spin, etc. Using the IllustrisTNG hydrodynamic simulation as our target, we show that machine learning tools can be used to capture this high-dimensional dependence and provide more accurate galaxy occupation models. Specifically, we use a random forest regressor to identify which secondary halo parameters best model the galaxy-halo connection and symbolic regression to augment the standard HOD model with simple equations capturing the dependence on those parameters, namely the local environmental overdensity and shear, at the location of a halo. This not only provides insights into the galaxy-formation relationship but, more importantly, improves the clustering statistics of the modeled galaxies significantly. Our approach demonstrates that machine learning tools can help us better understand and model the galaxy-halo connection, and are therefore useful for galaxy formation and cosmology studies from upcoming galaxy surveys.

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“…We avoid this definition for simplicity. It can also be viewed from the related perspective of halo occupations, i.e., as the dependence of the galaxy content of haloes (occupancy variations) on halo properties beyond halo mass (see, e.g., Artale et al 2018;Zehavi et al 2018;Bose et al 2019;Salcedo et al 2020). Other definitions include the recently proposed anisotropic assembly bias, which employs higher order moments of the power spectrum (Obuljen et al 2019(Obuljen et al , 2020.…”

Section: Introductionmentioning

confidence: 99%

“…Despite several detection claims, galaxy assembly bias is still generally regarded as an unconfirmed hypothesis (see, e.g., Miyatake et al 2016;Lin et al 2016;Sunayama et al 2016;Zu et al 2017;Montero-Dorta et al 2017;Niemiec et al 2018;Zentner et al 2019;Walsh & Tinker 2019;Sunayama & More 2019;Obuljen et al 2019;Montero-Dorta et al 2020a;Obuljen et al 2020;Salcedo et al 2020 for more information). In the context of hydrodynamical simulations, Montero-Dorta et al (2020b) showed that the halo assembly bias signal is present in IllustrisTNG and propagates to the central galaxy population when this is split by properties such as colour or SFR.…”

Section: Introductionmentioning

confidence: 99%

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

Montero-Dorta,

Chaves-Montero,

Artale

et al. 2021

Preprint

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Halo assembly bias is the secondary dependence of the clustering of dark-matter haloes on their assembly histories at fixed halo mass. This established dependence is expected to manifest itself on the clustering of the galaxy population, a potential effect commonly known as galaxy assembly bias. Using the IllustrisTNG300 magnetohydrodynamical simulation, we analyse the dependence of the properties and clustering of galaxies on the shape of the specific mass accretion history of their hosting haloes (sMAH). We first show that several halo and galaxy properties strongly correlate with the slope of the sMAH (𝛽) at fixed halo mass. Namely, haloes with increasingly steeper 𝛽 increment their halo masses faster at early times, and their hosted galaxies present larger stellar-to-halo mass ratios, lose their gas faster, reach the peak of their star formation histories at higher redshift, and become quenched earlier. We also demonstrate that 𝛽 is more directly connected to these key galaxy formation properties than other broadly employed halo proxies, such as formation time. Finally, we measure the secondary dependence of galaxy clustering on 𝛽 at fixed halo mass as a function of redshift. By tracing back the evolution of individual haloes, we show that the amplitude of the galaxy assembly bias signal for the progenitors of 𝑧 = 0 galaxies increases with redshift, reaching a factor of 2 at 𝑧 = 1 for haloes of 𝑀 halo = 10 11.5 − 10 12 ℎ −1 M . The measurement of the evolution of assembly bias along the merger tree provides a new theoretical perspective to the study of secondary bias. Our findings, which show a tight relationship between halo accretion and both the clustering and some of the main observational properties of the galaxy population, have also important implications for the generation of mock catalogues for upcoming cosmological surveys.

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Elucidating Galaxy Assembly Bias in SDSS

Cited by 19 publications

References 91 publications

On the Constraints of Galaxy Assembly Bias in Velocity Space

On the Constraints of Galaxy Assembly Bias in Velocity Space

Modeling the galaxy-halo connection with machine learning

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

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