arXiv subjects

ChangHoon Hahn

Publications and source records attributed to ChangHoon Hahn.

At least 73 records · Page 4Linked to original sources

arXiv2022

DESI Bright Galaxy Survey: Final Target Selection, Design, and Validation

Over the next five years, the Dark Energy Spectroscopic Instrument (DESI) will use 10 spectrographs with 5000 fibers on the 4m Mayall Telescope at Kitt Peak National Observatory to conduct the first Stage-IV dark energy galaxy survey. At $z < 0.6$, the DESI Bright Galaxy Survey (BGS) will produce the most detailed map of the Universe during the dark energy dominated epoch with redshifts of >10 million galaxies over 14,000 deg$^2$. In this work, we present and validate the final BGS target selection and survey design. From the Legacy Surveys, BGS will target a $r < 19.5$ magnitude-limited sample (BGS Bright); a fainter $19.5 < r < 20.175$ sample, color-selected to have high redshift efficiency (BGS Faint); and a smaller low-z quasar sample. BGS will observe these targets using exposure times, scaled to achieve uniform completeness, and visit each point on the footprint three times. We use observations from the Survey Validation programs conducted prior to the main survey along with realistic simulations to show that BGS can complete its strategy and make optimal use of `bright' time. We demonstrate that BGS targets have stellar contamination <1% and that their densities do not depend strongly on imaging properties. We also confirm that BGS Bright will achieve >80% fiber assignment efficiency. Finally, we show that BGS Bright and Faint will achieve >95% redshift success rates with no significant dependence on observing conditions. BGS meets the requirements for an extensive range of scientific applications. BGS will yield the most precise Baryon Acoustic Oscillations and Redshift-Space Distortions measurements at $z < 0.4$. It also presents opportunities to exploit new methods that require highly complete and dense galaxy samples (e.g. N-point statistics, multi-tracers). BGS further provides a powerful tool to study galaxy populations and the relations between galaxies and dark matter.

astro-ph.CO↗

arXiv2022

Cosmological Information in the Marked Power Spectrum of the Galaxy Field

Marked power spectra are two-point statistics of a marked field obtained by weighting each location with a function that depends on the local density around that point. We consider marked power spectra of the galaxy field in redshift space that up-weight low density regions, and perform a Fisher matrix analysis to assess the information content of this type of statistics using the Molino mock catalogs built upon the Quijote simulations. We identify four different ways to up-weight the galaxy field, and compare the Fisher information contained in their marked power spectra to the one of the standard galaxy power spectrum, when considering monopole and quadrupole of each statistic. Our results show that each of the four marked power spectra can tighten the standard power spectrum constraints on the cosmological parameters $Ω_{\rm m}$, $Ω_{\rm b}$, $h$, $n_s$, $M_ν$ by $15-25\%$ and on $σ_8$ by a factor of 2. The same analysis performed by combining the standard and four marked power spectra shows a substantial improvement compared to the power spectrum constraints that is equal to a factor of 6 for $σ_8$ and $2.5-3$ for the other parameters. Our constraints may be conservative, since the galaxy number density in the Molino catalogs is much lower than the ones in future galaxy surveys, which will allow them to probe lower density regions of the large-scale structure.

astro-ph.CO↗

arXiv2022

Wavelet Moments for Cosmological Parameter Estimation

Extracting non-Gaussian information from the non-linear regime of structure formation is key to fully exploiting the rich data from upcoming cosmological surveys probing the large-scale structure of the universe. However, due to theoretical and computational complexities, this remains one of the main challenges in analyzing observational data. We present a set of summary statistics for cosmological matter fields based on 3D wavelets to tackle this challenge. These statistics are computed as the spatial average of the complex modulus of the 3D wavelet transform raised to a power $q$ and are therefore known as invariant wavelet moments. The 3D wavelets are constructed to be radially band-limited and separable on a spherical polar grid and come in three types: isotropic, oriented, and harmonic. In the Fisher forecast framework, we evaluate the performance of these summary statistics on matter fields from the Quijote suite, where they are shown to reach state-of-the-art parameter constraints on the base $Λ$CDM parameters, as well as the sum of neutrino masses. We show that we can improve constraints by a factor 5 to 10 in all parameters with respect to the power spectrum baseline.

astro-ph.CO↗

arXiv2022

Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation

State-of-the-art spectral energy distribution (SED) analyses use a Bayesian framework to infer the physical properties of galaxies from observed photometry or spectra. They require sampling from a high-dimensional space of SED model parameters and take $>10-100$ CPU hours per galaxy, which renders them practically infeasible for analyzing the $billions$ of galaxies that will be observed by upcoming galaxy surveys ($e.g.$ DESI, PFS, Rubin, Webb, and Roman). In this work, we present an alternative scalable approach to rigorous Bayesian inference using Amortized Neural Posterior Estimation (ANPE). ANPE is a simulation-based inference method that employs neural networks to estimate the posterior probability distribution over the full range of observations. Once trained, it requires no additional model evaluations to estimate the posterior. We present, and publicly release, ${\rm SED}{flow}$, an ANPE method to produce posteriors of the recent Hahn et al. (2022) SED model from optical photometry. ${\rm SED}{flow}$ takes ${\sim}1$ $second~per~galaxy$ to obtain the posterior distributions of 12 model parameters, all of which are in excellent agreement with traditional Markov Chain Monte Carlo sampling results. We also apply ${\rm SED}{flow}$ to 33,884 galaxies in the NASA-Sloan Atlas and publicly release their posteriors: see https://changhoonhahn.github.io/SEDflow.

astro-ph.GA↗

arXiv2022

The DESI PRObabilistic Value-Added Bright Galaxy Survey (PROVABGS) Mock Challenge

The PRObabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will provide measurements of galaxy properties, such as stellar mass ($M_*$), star formation rate (${\rm SFR}$), stellar metallicity ($Z_{\rm MW}$), and stellar age ($t_{\rm age, MW}$), for >10 million galaxies of the DESI Bright Galaxy Survey. Full posterior distributions of the galaxy properties will be inferred using state-of-the-art Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and Legacy Surveys photometry. In this work, we present the SED model, Bayesian inference framework, and methodology of PROVABGS. Furthermore, we apply the PROVABGS SED modeling on realistic synthetic DESI spectra and photometry, constructed using the L-GALAXIES semi-analytic model. We compare the inferred galaxy properties to the true galaxy properties of the simulation using a hierarchical Bayesian framework to quantify accuracy and precision. Overall, we accurately infer the true $M_*$, ${\rm SFR}$, $Z_{\rm MW}$, and $t_{\rm age, MW}$ of the simulated galaxies. However, the priors on galaxy properties induced by the SED model have a significant impact on the posteriors. They impose a ${\rm SFR}{>}10^{-1} M_\odot/{\rm yr}$ lower bound on ${\rm SFR}$, a ${\sim}0.3$ dex bias on $\log Z_{\rm MW}$ for galaxies with low spectral signal-to-noise, and $t_{\rm age, MW} < 8\,{\rm Gyr}$ upper bound on stellar age. This work also demonstrates that a joint analysis of spectra and photometry significantly improves the constraints on galaxy properties over photometry alone and is necessary to mitigate the impact of the priors. With the methodology presented and validated in this work, PROVABGS will maximize information extracted from DESI observations and provide a probabilistic value-added galaxy catalog that will extend current galaxy studies to new regimes and unlock cutting-edge probabilistic analyses.

astro-ph.GA↗

arXiv2022

The CAMELS project: public data release

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-$α$ spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at \url{https://camels.readthedocs.io}.

astro-ph.CO↗

arXiv2021

The Quijote simulations

The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7,000 cosmological models in the $\{Ω_{\rm m}, Ω_{\rm b}, h, n_s, σ_8, M_ν, w \}$ hyperplane. At a single redshift the simulations contain more than 8.5 trillions of particles over a combined volume of 44,100 $(h^{-1}{\rm Gpc})^3$; each simulation follow the evolution of $256^3$, $512^3$ or $1024^3$ particles in a box of $1~h^{-1}{\rm Gpc}$ length. Billions of dark matter halos and cosmic voids have been identified in the simulations, whose runs required more than 35 million core hours. The Quijote simulations have been designed for two main purposes: 1) to quantify the information content on cosmological observables, and 2) to provide enough data to train machine learning algorithms. In this paper we describe the simulations and show a few of their applications. We also release the Petabyte of data generated, comprising hundreds of thousands of simulation snapshots at multiple redshifts, halo and void catalogs, together with millions of summary statistics such as power spectra, bispectra, correlation functions, marked power spectra, and estimated probability density functions.

astro-ph.CO↗

arXiv2021

The PDF perspective on the tracer-matter connection: Lagrangian bias and non-Poissonian shot noise

We study the connection of matter density and its tracers from the PDF perspective. One aspect of this connection is the conditional expectation value $\langle δ_{\mathrm{tracer}}|δ_m\rangle$ when averaging both tracer and matter density over some scale. We present a new way to incorporate a Lagrangian bias expansion of this expectation value into standard frameworks for modelling the PDF of density fluctuations and counts-in-cells statistics. Using N-body simulations and mock galaxy catalogs we confirm the accuracy of this expansion and compare it to the more commonly used Eulerian parametrization. For halos hosting typical luminous red galaxies, the Lagrangian model provides a significantly better description of $\langle δ_{\mathrm{tracer}}|δ_m\rangle$ at second order in perturbations. A second aspect of the matter-tracer connection is shot-noise, \ie the scatter of tracer density around $\langle δ_{\mathrm{tracer}}|δ_m\rangle$. It is well known that this noise can be significantly non-Poissonian and we validate the performance of a more general, two-parameter shot-noise model for different tracers and simulations. Both parts of our analysis are meant to pave the way for forthcoming applications to survey data.

astro-ph.CO↗

arXiv2021

IQ Collaboratory III: The Empirical Dust Attenuation Framework -- Taking Hydrodynamical Simulations with a Grain of Dust

We present the Empirical Dust Attenuation (EDA) framework -- a flexible prescription for assigning realistic dust attenuation to simulated galaxies based on their physical properties. We use the EDA to forward model synthetic observations for three state-of-the-art large-scale cosmological hydrodynamical simulations: SIMBA, IllustrisTNG, and EAGLE. We then compare the optical and UV color-magnitude relations, $(g-r) - M_r$ and $(FUV-NUV)-M_r$, of the simulations to a $M_r < -20$ and UV complete SDSS galaxy sample using likelihood-free inference. Without dust, none of the simulations match observations, as expected. With the EDA, however, we can reproduce the observed color-magnitude with all three simulations. Furthermore, the attenuation curves predicted by our dust prescription are in good agreement with the observed attenuation-slope relations and attenuation curves of star-forming galaxies. However, the EDA does not predict star-forming galaxies with low $A_V$ since simulated star-forming galaxies are intrinsically much brighter than observations. Additionally, the EDA provides, for the first time, predictions on the attenuation curves of quiescent galaxies, which are challenging to measure observationally. Simulated quiescent galaxies require shallower attenuation curves with lower amplitude than star-forming galaxies. The EDA, combined with forward modeling, provides an effective approach for shedding light on dust in galaxies and probing hydrodynamical simulations. This work also illustrates a major limitation in comparing galaxy formation models: by adjusting dust attenuation, simulations that predict significantly different galaxy populations can reproduce the same UV and optical observations.

astro-ph.GA↗

arXiv2021

Characterising the target selection pipeline for the Dark Energy Spectroscopic Instrument Bright Galaxy Survey

We present the steps taken to produce a reliable and complete input galaxy catalogue for the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey (BGS) using the photometric Legacy Survey DR8 DECam. We analyse some of the main issues faced in the selection of targets for the DESI BGS, such as star-galaxy separation, contamination by fragmented stars and bright galaxies. Our pipeline utilizes a new way to select BGS galaxies using Gaia photometry and we implement geometrical and photometric masks that reduce the number of spurious objects. The resulting catalogue is cross-matched with the Galaxy And Mass Assembly (GAMA) survey to assess the completeness of the galaxy catalogue and the performance of the target selection. We also validate the clustering of the sources in our BGS catalogue by comparing with mock catalogues and the Sloan Digital Sky Survey (SDSS) data. Finally, the robustness of the BGS selection criteria is assessed by quantifying the dependence of the target galaxy density on imaging and other properties. The largest systematic correlation we find is a 7 per cent suppression of the target density in regions of high stellar density.

astro-ph.GA↗

arXiv2020

Constraining $M_ν$ with the Bispectrum II: The Total Information Content of the Galaxy Bispectrum

Massive neutrinos suppress the growth of structure on small scales and leave an imprint on large-scale structure that can be measured to constrain their total mass, $M_ν$. With standard analyses of two-point clustering statistics, $M_ν$ constraints are severely limited by parameter degeneracies. Hahn et al.(2020) demonstrated that the bispectrum, the next higher-order statistic, can break these degeneracies and dramatically improve constraints on $M_ν$ and other cosmological parameters. In this paper, we present the constraining power of the redshift-space galaxy bispectrum, $B_0^g$. We construct the Molino suite of 75,000 mock galaxy catalogs from the Quijote $N$-body simulations using the halo occupation distribution (HOD) model, which provides a galaxy bias framework well-suited for simulation-based approaches. Using these mocks, we present Fisher matrix forecasts for $\{Ω_m,Ω_b,h,n_s,σ_8, M_ν\}$ and quantify, for the first time, the total information content of $B_0^g$ down to nonlinear scales. For $k_{\rm max}=0.5h/Mpc$, $B_0^g$ improves constraints on $Ω_m,Ω_b,h,n_s,σ_8$, and $M_ν$ by 2.8, 3.1, 3.8, 4.2, 4.2, and $4.6\times$ over the power spectrum, after marginalizing over HOD parameters. Even with priors from $Planck$, $B_0^g$ improves all of the cosmological constraints by $\gtrsim2\times$. In fact, for $P_\ell^g$ and $B_0^g$ out to $k_{\rm max}=0.5h/Mpc$ with $Planck$ priors, we achieve a $1σ$ $M_ν$ constraint of 0.048 eV, which is tighter than the current best cosmological constraint. While effects such as survey geometry and assembly bias will have an impact, these constraints are derived for $(1h^{-1}{\rm Gpc})^3$, a substantially smaller volume than upcoming surveys. Therefore, we conclude that the galaxy bispectrum will significantly improve cosmological constraints for upcoming galaxy surveys -- especially for $M_ν$.

astro-ph.CO↗

arXiv2020

Preliminary Target Selection for the DESI Bright Galaxy Survey (BGS)

The Dark Energy Spectroscopic Instrument (DESI) will execute a nearly magnitude-limited survey of low redshift galaxies ($0.05 \leq z \leq 0.4$, median $z \approx 0.2$). Clustering analyses of this Bright Galaxy Survey (BGS) will yield the most precise measurements to date of baryon acoustic oscillations and redshift-space distortions at low redshift. DESI BGS will comprise two target classes: (i) BRIGHT ($r<19.5$~mag), and (ii) FAINT ($19.5<r<20$~mag). Here we present a summary of the star-galaxy separation, and different photometric and geometrical masks, used in BGS to reduce the number of spurious targets. The selection results in a total density of $\sim 800$ objects/deg$^2$ for the BRIGHT and $\sim 600$ objects/deg$^2$ for the FAINT selections.A full characterization of the BGS selection can be found in Ruiz-Macias et al. (2020).

astro-ph.GA↗

arXiv2020

IQ Collaboratory II: The Quiescent Fraction of Isolated, Low Mass Galaxies Across Simulations and Observations

We compare three major large-scale hydrodynamical galaxy simulations (EAGLE, Illustris-TNG, and SIMBA) by forward modeling simulated galaxies into observational space and computing the fraction of isolated and quiescent low mass galaxies as a function of stellar mass. Using SDSS as our observational template, we create mock surveys and synthetic spectroscopic and photometric observations of each simulation, adding realistic noise and observational limits. All three simulations show a decrease in the number of quiescent, isolated galaxies in the mass range $\mathrm{M}_* = 10^{9-10} \ \mathrm{M}_\odot$, in broad agreement with observations. However, even after accounting for observational and selection biases, none of the simulations reproduce the observed absence of quiescent field galaxies below $\mathrm{M}_*=10^{9} \ \mathrm{M}_\odot$. We find that the low mass quiescent populations selected via synthetic observations have consistent quenching timescales, despite apparent variation in the late time star formation histories. The effect of increased numerical resolution is not uniform across simulations and cannot fully mitigate the differences between the simulations and the observations. The framework presented here demonstrates a path towards more robust and accurate comparisons between theoretical simulations and galaxy survey observations, while the quenching threshold serves as a sensitive probe of feedback implementations.

astro-ph.GA↗

arXiv2020

SPECULATOR: Emulating stellar population synthesis for fast and accurate galaxy spectra and photometry

We present SPECULATOR - a fast, accurate, and flexible framework for emulating stellar population synthesis (SPS) models for predicting galaxy spectra and photometry. For emulating spectra, we use principal component analysis to construct a set of basis functions, and neural networks to learn the basis coefficients as a function of the SPS model parameters. For photometry, we parameterize the magnitudes (for the filters of interest) as a function of SPS parameters by a neural network. The resulting emulators are able to predict spectra and photometry under both simple and complicated SPS model parameterizations to percent-level accuracy, giving a factor of $10^3$-$10^4$ speed up over direct SPS computation. They have readily-computable derivatives, making them amenable to gradient-based inference and optimization methods. The emulators are also straightforward to call from a GPU, giving an additional order-of-magnitude speed-up. Rapid SPS computations delivered by emulation offers a massive reduction in the computational resources required to infer the physical properties of galaxies from observed spectra or photometry and simulate galaxy populations under SPS models, whilst maintaining the accuracy required for a range of applications.

astro-ph.IM↗

arXiv2020

Constraining $M_ν$ with the Bispectrum I: Breaking Parameter Degeneracies

Massive neutrinos suppress the growth of structure below their free-streaming scale and leave an imprint on large-scale structure. Measuring this imprint allows us to constrain the sum of neutrino masses, $M_ν$, a key parameter in particle physics beyond the Standard Model. However, degeneracies among cosmological parameters, especially between $M_ν$ and $σ_8$, limit the constraining power of standard two-point clustering statistics. In this work, we investigate whether we can break these degeneracies and constrain $\smnu$ with the next higher-order correlation function --- the bispectrum. We first examine the redshift-space halo bispectrum of $800$ $N$-body simulations from the HADES suite and demonstrate that the bispectrum helps break the $M_ν$--$σ_8$ degeneracy. Then using 22,000 $N$-body simulations of the Quijote suite, we quantify for the first time the full information content of the redshift-space halo bispectrum down to nonlinear scales using a Fisher matrix forecast of $\{Ω_m$, $Ω_b$, $h$, $n_s$, $σ_8$, $M_ν\}$. For $k_{\rm max}{=}0.5~h/{\rm Mpc}$, the bispectrum provides $Ω_m$, $Ω_b$, $h$, $n_s$, and $σ_8$ constraints 1.9, 2.6, 3.1, 3.6, and 2.6 times tighter than the power spectrum. For $M_ν$, the bispectrum improves the 1$σ$ constraint from 0.2968 to 0.0572 eV --- over 5 times tighter than the power spectrum. Even with priors from {\em Planck}, the bispectrum improves $M_ν$ constraints by a factor of 1.8. Although we reserve marginalizing over a more complete set of bias parameters to the next paper of the series, these constraints are derived for a $(1~h^{-1}{\rm Gpc})^3$ box, a substantially smaller volume than upcoming surveys. Thus, our results demonstrate that the bispectrum offers significant improvements over the power spectrum, especially for constraining $M_ν$.

astro-ph.CO↗

arXiv2019

Constraining Star Formation Histories of Blue Galaxies using the Scatter between Stellar Mass and Halo Mass

We present constraints on the timescale of star formation variability and the correlation between star formation and host halo accretion histories of star-forming (SF) central galaxies from the scatter of the stellar-to-halo mass relation (SHMR). SF galaxies are found to have a tight relationship between their star formation rates and stellar masses on the so-called "star-forming sequence" (SFS), which characterizes their SF histories and $M_*$ growths. Meanwhile, observed SHMR constraints connect $M_*$ growth to halo accretion history. Combining these observed trends with a cosmological $N$-body simulation, we present flexible models that track the SF, $M_*$, and halo accretion histories of SF central galaxies at $z<1$ while reproducing the observed stellar mass function and SFS in SDSS DR7. We find that the scatter in SHMR at $M_h=10^{12}M_\odot$, $σ_{M_*|M_h=10^{12}M_\odot}$, is sensitive to the timescale of star formation variability, $t_{\rm duty}$, and the correlation coefficient, $r$, between SF and halo accretion histories: shorter $t_{\rm duty}$ and higher $r$ both result in tighter $σ_{M_*|M_h=10^{12}M_\odot}$. To reproduce a constant 0.2 dex scatter over $z=1-0$, our models require $t_{\rm duty}\leq1.5$ Gyr for $r=0.99$ or $r>0.6$ for $t_{\rm duty}=0.1$ Gyr. For $r\sim0.6$, as found in the literature, $t_{\rm duty}<0.2$ Gyr is necessary. Meanwhile, to reproduce the tightening of $σ_{M_*|M_h=10^{12}M_\odot}=0.35$ to 0.2 dex from $z=1-0$ in hydrodynamical simulations, our models require $t_{\rm duty}=0.1$ Gyr for $r>0.5$. Although, the lack of consensus on $σ_{M_*|M_h=10^{12}M_\odot}$ at $M_h=10^{12}M_\odot$ and at $z=1$ from observations and galaxy formation models remains the main bottleneck in precisely constraining $r$ and $t_{\rm duty}$, we demonstrate that SHMR can be used to constrain the SF and host halo accretion histories of SF central galaxies.

astro-ph.GA↗

arXiv2019

Inflation and Dark Energy from spectroscopy at $z > 2$

The expansion of the Universe is understood to have accelerated during two epochs: in its very first moments during a period of Inflation and much more recently, at $z < 1$, when Dark Energy is hypothesized to drive cosmic acceleration. The undiscovered mechanisms behind these two epochs represent some of the most important open problems in fundamental physics. The large cosmological volume at $2 < z < 5$, together with the ability to efficiently target high-$z$ galaxies with known techniques, enables large gains in the study of Inflation and Dark Energy. A future spectroscopic survey can test the Gaussianity of the initial conditions up to a factor of ~50 better than our current bounds, crossing the crucial theoretical threshold of $σ(f_{NL}^{\rm local})$ of order unity that separates single field and multi-field models. Simultaneously, it can measure the fraction of Dark Energy at the percent level up to $z = 5$, thus serving as an unprecedented test of the standard model and opening up a tremendous discovery space.

astro-ph.CO↗

arXiv2018

How are galaxies assigned to halos? Searching for assembly bias in the SDSS galaxy clustering

Clustering of dark matter halos has been shown to depend on halo properties beyond mass such as halo concentration, a phenomenon referred to as halo assembly bias. Standard halo occupation models (HOD) in large scale structure studies assume that halo mass alone is sufficient in characterizing the connection between galaxies and halos. Modeling of galaxy clustering can face systematic effects if the number of galaxies within a halo is correlated with other halo properties. Using the Small MultiDark-Planck high resolution $N$-body simulation and the clustering measurements of the Sloan Digital Sky Survey (SDSS) DR7 main galaxy sample, we investigate the extent to which the concentration-dependence of halo occupation can be constrained. Furthermore, we study how allowing for the concentration dependence can improve our modeling of galaxy clustering. Our constraints on HOD with assembly bias suggest that satellite population is not correlated with halo concentration at fixed halo mass. At fixed halo mass, our constraints favor lack of correlation between the occupation of centrals and halo concentration in the most luminous samples ($M_{\rm r}<-21.5,-21$), and modest correlation in the $M_{\rm r}<-20.5,-20, -19.5$ samples. We show that in comparison with abundance-matching mock catalogs, our findings suggest qualitatively similar but modest levels of the impact of halo assembly bias on galaxy clustering. The effect is only present in the central occupation and becomes less significant in brighter galaxy samples. Furthermore, by performing model comparison based on information criteria, we find that in most cases, the standard mass-only HOD model is still favored by the observations.

astro-ph.CO↗