arXiv subjects

Xiaoju Xu

Publications and source records attributed to Xiaoju Xu.

13 recordsLinked to original sources

arXiv2026

The Impact of Splashback Galaxies on Galaxy Assembly Bias

The clustering of galaxies is affected by the assembly history of their underlying dark matter halos. This complex phenomenon, known as galaxy assembly bias, has been extensively studied, but the exact physical origin remains unclear. Splashback halos, typically low-mass halos that have traversed larger neighboring halos, have been suggested to be associated with halo assembly bias. Using a semi-analytic galaxy-formation model applied to the Millennium simulation, we explicitly explore the role that splasback galaxies play in galaxy assembly bias. We identify splashbacks as present-day central galaxies that were formerly satellites of a more massive host, and construct stellar-mass selected galaxy samples with the splashbacks either removed or reclassified as satellites of their former host halo. We find that splashbacks tend to reside in low-mass, highly concentrated halos and in dense environments, and that they have relatively high stellar-to-halo mass ratios. Splashbacks appear to be largely responsible for the low-mass tail of the occupancy variation for highly concentrated halos and for halos in dense environments. Finally, when computing the impact of assembly bias on galaxy clustering, we find that while removing the splashbacks significantly reduces the signal, reassigning them has little effect on its amplitude but shifts the transition scale. We repeat the analysis with the hydrodynamical simulation TNG300, confirming the robustness of our results. Our results provide insight into assembly bias and have potential implications for modeling the galaxy-halo connection.

astro-ph.CO↗

arXiv2026

What can galaxy clustering really tell us about the galaxy-halo connections?

Subhalo abundance matching (SHAM) is a commonly used framework for modeling the galaxy-halo connection. Yet, its standard implementation has difficulty reproducing the observed galaxy clustering with high accuracy (e.g., $χ^2/\mathrm{dof} \approx 1$). To overcome this issue, we propose a novel CS-SHAM framework, in which central and satellite galaxies are independently matched to main and satellite subhalos in simulations. Within this scheme, we introduce three free parameters to explicitly characterize the satellite fraction, $f_{\mathrm{sat}}$, as a function of stellar mass or absolute magnitude. To evaluate the performance of CS-SHAM, we apply it to two sets of mock galaxy catalogs built with the conventional SHAM method but using different subhalo mass proxies, $M_{\mathrm{peak}}$ and $V_{\mathrm{peak}}$, as well as two additional galaxy samples generated from a SAM and from TNG-300. We demonstrate that CS-SHAM reliably reproduces galaxy clustering whether $M_{\mathrm{peak}}$ or $V_{\mathrm{peak}}$ is used as the subhalo mass proxy. We also find that the models are unable to place robust constraints on $f_{\mathrm{sat}}$ if different mass proxies are employed. Indeed, within the CS-SHAM framework the halo occupation distribution (HOD) and conditional luminosity or stellar mass function (CLF/CSMF) are accurately recovered. Furthermore, we demonstrate for the first time that galaxy clustering constrains the HOD and CLF/CSMF primarily for relatively massive halos. Because the halo bias is nearly constant for low-mass halos, galaxy clustering is generally not very sensitive to the satellite population residing in these low-mass systems.

astro-ph.GA↗

arXiv2025

Direct Measurement of Galaxy Assembly Bias using DESI DR1 Data

We report the first direct measurement of galaxy assembly bias, a critical systematic in cosmology, from the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey. We introduce a novel, cosmology-independent method to measure the halo occupation distribution (HOD) by combining a state-of-the-art group catalog with weak gravitational lensing. For groups binned by total luminosity, we determine the galaxy occupation number $N_{\rm gal}$ from group-galaxy cross-correlations, while weak lensing constrains the average halo mass $M_h$. Applying this to a volume-limited sample at $z{\in}[0.05,0.2]$, we measure the dependence of HOD, $N_{\rm gal}(M_h)$, on large-scale overdensity $δ_{g}$. Focusing on the satellite galaxies, we find an assembly bias parameter of $Q_{\rm sat}{=}0.05{\pm}0.14$, a result consistent with zero and in tension with many empirical galaxy formation models. Our method provides a robust approach for characterizing galaxy assembly bias to achieve precision cosmology with DESI and future Stage-V surveys.

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arXiv2024

Establishing HI mass v.s. stellar mass and halo mass scaling relations using an abundance matching method

We combined data from the Sloan Digital Sky Survey (SDSS) and the Arecibo Legacy Fast ALFA Survey (ALFALFA) to establish the HI mass vs. stellar mass and halo mass scaling relations using an abundance matching method that is free of the Malmquist bias. To enable abundance matching, a cross-match between the SDSS DR7 galaxy group sample and the ALFALFA HI sources provides a catalog of 16,520 HI-galaxy pairs within 14,270 galaxy groups (halos). By applying the observational completeness reductions for both optical and HI observations, we used the remaining 8,180 ALFALFA matched sources to construct the model constraints. Taking into account the dependence of HI mass on both the galaxy and group properties, we establish two sets of scaling relations: one with a combination of stellar mass, $({g-r})$ color and halo mass, and the other with stellar mass, specific star-formation rate ($\rm sSFR$), and halo mass. We demonstrate that our models can reproduce the HI mass component as both a stellar and halo mass. Additional tests showed that the conditional HI mass distributions as a function of the cosmic web type and the satellite fractions were well recovered.

astro-ph.GA↗

arXiv2024

Measuring the conditional luminosity and stellar mass functions of galaxies by combining the DESI LS DR9, SV3 and Y1 data

In this investigation, we leverage the combination of Dark Energy Spectroscopic Instrument Legacy imaging Surveys Data Release 9 (DESI LS DR9), Survey Validation 3 (SV3), and Year 1 (Y1) data sets to estimate the conditional luminosity and stellar mass functions (CLFs & CSMFs) of galaxies across various halo mass bins and redshift ranges. To support our analysis, we utilize a realistic DESI Mock Galaxy Redshift Survey (MGRS) generated from a high-resolution Jiutian simulation. An extended halo-based group finder is applied to both MGRS catalogs and DESI observation. By comparing the r and z-band luminosity functions (LFs) and stellar mass functions (SMFs) derived using both photometric and spectroscopic data, we quantified the impact of photometric redshift (photo-z) errors on the galaxy LFs and SMFs, especially in the low redshift bin at low luminosity/mass end. By conducting prior evaluations of the group finder using MGRS, we successfully obtain a set of CLF and CSMF measurements from observational data. We find that at low redshift the faint end slopes of CLFs and CSMFs below $10^{9}h^{-2}L_{\odot}$ (or $h^{-2}M_{\odot}$) evince a compelling concordance with the subhalo mass functions. After correcting the cosmic variance effect of our local Universe following arXiv:1809.00523, the faint end slopes of the LFs/SMFs turn out to be also in good agreement with the slope of the halo mass function.

astro-ph.GA↗

arXiv2024

CSST large-scale structure analysis pipeline: I. constructing reference mock galaxy redshift surveys

In this paper, we set out to construct a set of reference mock galaxy redshift surveys (MGRSs) for the future Chinese Space-station Survey Telescope (CSST) observation, where subsequent survey selection effects can be added and evaluated. This set of MGRSs is generated using the dark matter subhalos extracted from a high-resolution Jiutian $N$-body simulation of the standard $Λ$CDM cosmogony with $Ω_m=0.3111$, $Ω_Λ=0.6889$, and $σ_8=0.8102$. The simulation has a boxsize of $1~h^{-1} {\rm Gpc}$, and consists of $6144^3$ particles with mass resolution $3.723 \times 10^{8} h^{-1} M_\odot $. In order to take into account the effect of redshift evolution, we first use all 128 snapshots in the Jiutian simulation to generate a light-cone halo/subhalo catalog. Next, galaxy luminosities are assigned to the main and subhalo populations using the subhalo abundance matching (SHAM) method with the DESI $z$-band luminosity functions at different redshifts. Multi-band photometries, as well as images, are then assigned to each mock galaxy using a 3-dimensional parameter space nearest neighbor sampling of the DESI LS observational galaxies and groups. Finally, the CSST and DESI LS survey geometry and magnitude limit cuts are applied to generate the required MGRSs. As we have checked, this set of MGRSs can generally reproduce the observed galaxy luminosity/mass functions within 0.1 dex for galaxies with $L > 10^8 L_\odot$ (or $M_* > 10^{8.5} M_\odot$) and within 1-$σ$ level for galaxies with $L < 10^8L_\odot$ (or $M_* < 10^{8.5} M_\odot$). Together with the CSST slitless spectra and redshifts for our DESI LS seed galaxies that are under construction, we will set out to test various slitless observational selection effects in subsequent probes.

astro-ph.GA↗

arXiv2023

Conditional Colour-Magnitude Distribution of Central Galaxies in Galaxy Formation Models

We investigate the conditional colour-magnitude distribution (CCMD), namely the colour-magnitude distribution at fixed halo mass, of the central galaxies in semi-analytic galaxy formation model (SAM) and hydrodynamic simulations. We analyse the CCMD of central galaxies in each halo mass bin with the Gaussian mixture model and find that it can be decomposed into red and blue components nearly orthogonal to each other, a red component narrow in colour and extended in magnitude and a blue component narrow in magnitude and extended in colour. We focus on the SAM galaxies to explore the origin of the CCMD components by studying the relation between central galaxy colour and halo or galaxy properties. Central galaxy colour is correlated with halo assembly properties for low mass haloes and independent of them for high mass haloes. Galaxy properties such as central supermassive black hole mass, cold gas mass, and gas specific angular momentum can all impact central galaxy colour. These results are corroborated by an alternative machine learning analysis in which we attempt to predict central galaxy colour with halo and galaxy properties. We find that the prediction for colours of central galaxies can be significantly improved using both halo and galaxy properties as input compared to using halo properties alone. With the halo and galaxy properties considered here, we find that subtle discrepancies remain between predicted and original colour distribution for low mass haloes and that no significant determining properties are identified in massive haloes, suggesting modulations by additional stochastic processes in galaxy formation.

astro-ph.GA↗

arXiv2023

Connection Between SDSS Galaxies and ELUCID Subhaloes in the Eye of Machine Learning

We explore the feasibility of learning the connection between SDSS galaxies and ELUCID subhaloes with random forest (RF). ELUCID is a constrained $N$-body simulation constructed using the matter density field of SDSS. Based on an SDSS-ELUCID matched catalogue, we build RF models that predict $M_r$ magnitude, colour, stellar mass $M_*$, and specific star formation rate (sSFR) with several subhalo properties. While the RF can predict $M_r$ and $M_*$ with reasonable accuracy, the prediction accuracy of colour and sSFR is low, which could be due to the mismatch between galaxies and subhaloes. To test this, we shuffle the galaxies in subhaloes of narrow mass bins in the local neighbourhood using galaxies of a semi-analytic model (SAM) and the TNG hydrodynamic simulation. We find that the shuffling only slightly reduces the colour prediction accuracy in SAM and TNG, which is still considerably higher than that of the SDSS. This suggests that the true connection between SDSS colour and subhalo properties could be weaker than that in the SAM and TNG without the mismatch effect. We also measure the Pearson correlation coefficient between galaxy properties and the subhalo properties in SDSS, SAM, and TNG. Similar to the RF results, we find that the colour-subhalo correlation in SDSS is lower than both the SAM and TNG. We also show that the galaxy-subhalo correlations depend on subhalo mass in the galaxy formation models. Advanced surveys with more fainter galaxies will provide new insights into the galaxy-subhalo relation in the real Universe.

astro-ph.GA↗

arXiv2021

SHAM through the lens of a hydrodynamical simulation

We use the IllustrisTNG100 hydrodynamical simulation to study the dependence of the galaxy two-point correlation function on a broad range of secondary subhalo and galactic properties. We construct galaxy mock catalogues adopting a standard sub-halo abundance matching scheme coupled with a secondary assignment between galaxy colour or specific star formation rate and the following subhalo properties: starvation redshift z$_{\rm starve}$, concentration at infall, overdensity $δ_R^{\rm env}$, tidal anisotropy $α_R$, and tidal overdensity $δ_R$. The last two quantities allow us to fully characterise the tidal field of our subhaloes, acting as mediators between their internal and large-scale properties. The resulting mock catalogues overall return good agreement with the IllustrisTNG100 measurements. The accuracy of each model strongly depends on the correlation between the secondary galaxy and subhalo properties employed. Among all the subhalo proxies tested, we find that z$_{\rm starve}$ and $c_{\rm infall}$ are the ones that best trace the large-scale structure, producing robust clustering predictions for different samples of red/blue and quenched/star-forming galaxies.

astro-ph.GA↗

arXiv2021

Predicting halo occupation and galaxy assembly bias with machine learning

Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the halo occupations and recover galaxy clustering and assembly bias in a semi-analytic galaxy formation model. For stellar-mass selected samples, we train a Random Forest algorithm on the number of central and satellite galaxies in each dark matter halo. With the predicted occupations, we create mock galaxy catalogues and measure the clustering and assembly bias. Using a range of halo and environment properties, we find that the machine learning predictions of the occupancy variations with secondary properties, galaxy clustering and assembly bias are all in excellent agreement with those of our target galaxy formation model. Internal halo properties are most important for the central galaxies prediction, while environment plays a critical role for the satellites. Our machine learning models are all provided in a usable format. We demonstrate that machine learning is a powerful tool for modelling the galaxy-halo connection, and can be used to create realistic mock galaxy catalogues which accurately recover the expected occupancy variations, galaxy clustering and galaxy assembly bias, imperative for cosmological analyses of upcoming surveys.

astro-ph.CO↗

arXiv2021

Dissecting and Modelling Galaxy Assembly Bias

Understanding the galaxy-halo connection is fundamental for contemporary models of galaxy clustering. The extent to which the haloes' assembly history and environment impact galaxy clustering (a.k.a. galaxy assembly bias; GAB), remains a complex and challenging problem. Using a semi-analytic galaxy formation model, we study the individual contributions of different secondary halo properties to the GAB signal. These are obtained by comparing the clustering of stellar-mass selected samples to that of shuffled samples where the galaxies are randomly reassigned to haloes of fixed mass and a specified secondary halo property. We explore a large range of internal halo properties and environmental measures. We find that commonly used properties like halo age or concentration amount to only 20-30 per cent of the signal, while the smoothed matter density or the tidal anisotropy can account for the full level of GAB (though care should be given to the specific definition). For the "successful" measures, we examine the occupancy variations and the associated changes in the halo occupation function parameters. These are used to create mock catalogues that reproduce the full level of GAB. Finally, we propose a practical modification of the standard halo occupation distribution model, which can be tuned to any level of assembly bias. Fitting the parameters to our semi-analytic model, we demonstrate that the corresponding mock catalogue recovers the target level of GAB as well as the occupancy variations. Our results enable producing realistic mock catalogues and directly inform theoretical modelling of assembly bias and attempts to detect it in the Universe.

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arXiv2020

Galaxy assembly bias of central galaxies in the Illustris simulation

Galaxy assembly bias, the correlation between galaxy properties and halo properties at fixed halo mass, could be an important ingredient in halo-based modelling of galaxy clustering. We investigate the central galaxy assembly bias by studying the relation between various galaxy and halo properties in the Illustris hydrodynamic galaxy formation simulation. Galaxy stellar mass $\Mstar$ is found to have a tighter correlation with peak maximum halo circular velocity $\Vp$ than with halo mass $\Mh$. Once the correlation with $\Vp$ is accounted for, $\Mstar$ has nearly no dependence on any other halo assembly variables. The correlations between galaxy properties related to star formation history and halo assembly properties also show a cleaner form as a function of $\Vp$ than as a function of $\Mh$, with the main correlation being with halo formation time and to a less extent halo concentration. Based on the galaxy-halo relation, we present a simple model to relate the bias factors of a central galaxy sample and the corresponding halo sample, both selected based on assembly-related properties. It is found that they are connected by the correlation coefficient of the galaxy and halo properties used to define the two samples, which provides a reasonable description for the samples in the simulation and suggests a simple prescription to incorporate galaxy assembly bias into the halo model. By applying the model to the local galaxy clustering measurements in Lin et al. (2016), we infer that the correlation between star formation history or specific star formation rate and halo formation time is consistent with being weak.

astro-ph.GA↗

arXiv2018

Dependence of Halo Bias and Kinematics on Assembly Variables

Using dark matter haloes identified in a large $N$-body simulation, we study halo assembly bias, with halo formation time, peak maximum circular velocity, concentration, and spin as the assembly variables. Instead of grouping haloes at fixed mass into different percentiles of each assembly variable, we present the joint dependence of halo bias on the {\it values} of halo mass and each assembly variable. In the plane of halo mass and one assembly variable, the joint dependence can be largely described as halo bias increasing outward from a global minimum. We find it unlikely to have a combination of halo variables to absorb all assembly bias effects. We then present the joint dependence of halo bias on two assembly variables at fixed halo mass. The gradient of halo bias does not necessarily follow the correlation direction of the two assembly variables and it varies with halo mass. Therefore in general for two correlated assembly variables one cannot be used as a proxy for the other in predicting halo assembly bias trend. Finally, halo assembly is found to affect the kinematics of haloes. Low-mass haloes formed earlier can have much higher pairwise velocity dispersion than those of massive haloes. In general, halo assembly leads to a correlation between halo bias and halo pairwise velocity distribution, with more strongly clustered haloes having higher pairwise velocity and velocity dispersion. However, the correlation is not tight, and the kinematics of haloes at fixed halo bias still depends on halo mass and assembly variables.

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