Evidence for galaxy assembly bias in BOSS CMASS redshift-space galaxy correlation function
02 Mar 2021-Monthly Notices of the Royal Astronomical Society (Oxford Academic)-Vol. 502, Iss: 3, pp 3582-3598
TL;DR: In this article, an extended halo occupation distribution model (HOD) is proposed that includes both a concentration-based assembly bias term and an environment-based bias term, and it achieves a good fit (chi 2/DoF = 1.35) on the 2D redshift-space 2-point correlation function (2PCF) of the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample.
Abstract: Building accurate and flexible galaxy-halo connection models is crucial in modeling galaxy clustering on non-linear scales. Recent studies have found that halo concentration by itself cannot capture the full galaxy assembly bias effect and that the local environment of the halo can be an excellent indicator of galaxy assembly bias. In this paper, we propose an extended halo occupation distribution model (HOD) that includes both a concentration-based assembly bias term and an environment-based assembly bias term. We use this model to achieve a good fit (chi^2/DoF = 1.35) on the 2D redshift-space 2-point correlation function (2PCF) of the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample. We find that the inclusion of both assembly bias terms is strongly favored by the data and the standard 5-parameter HOD is strongly rejected. More interestingly, the redshift-space 2PCF drives the assembly bias parameters in a way that preferentially assigns galaxies to lower mass halos. This results in galaxy-galaxy lensing predictions that are within 1sigma agreement with the observation, alleviating the perceived tension between galaxy clustering and lensing. We also showcase a consistent 3-5sigma preference for a positive environment-based assembly bias that persists over variations in the fit. We speculate that the environmental dependence might be driven by underlying processes such as mergers and feedback, but might also be indicative of a larger halo boundaries such as the splashback radius. Regardless, this work highlights the importance of building flexible galaxy-halo connection models and demonstrates the extra constraining power of the redshift-space 2PCF.
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TL;DR: The first cosmology results from large-scale structure in the Dark Energy Survey (DES) spanning 5000 deg$^2 were presented in this paper , where the authors performed an analysis combining three two-point correlation functions (3$\times$2pt): (i) cosmic shear using 100 million source galaxies, (ii) galaxy clustering, and (iii) cross-correlation of source galaxy shear with lens galaxy positions.
Abstract: We present the first cosmology results from large-scale structure in the Dark Energy Survey (DES) spanning 5000 deg$^2$. We perform an analysis combining three two-point correlation functions (3$\times$2pt): (i) cosmic shear using 100 million source galaxies, (ii) galaxy clustering, and (iii) the cross-correlation of source galaxy shear with lens galaxy positions. The analysis was designed to mitigate confirmation or observer bias; we describe specific changes made to the lens galaxy sample following unblinding of the results. We model the data within the flat $\Lambda$CDM and $w$CDM cosmological models. We find consistent cosmological results between the three two-point correlation functions; their combination yields clustering amplitude $S_8=0.776^{+0.017}_{-0.017}$ and matter density $\Omega_{\mathrm{m}} = 0.339^{+0.032}_{-0.031}$ in $\Lambda$CDM, mean with 68% confidence limits; $S_8=0.775^{+0.026}_{-0.024}$, $\Omega_{\mathrm{m}} = 0.352^{+0.035}_{-0.041}$, and dark energy equation-of-state parameter $w=-0.98^{+0.32}_{-0.20}$ in $w$CDM. This combination of DES data is consistent with the prediction of the model favored by the Planck 2018 cosmic microwave background (CMB) primary anisotropy data, which is quantified with a probability-to-exceed $p=0.13$ to $0.48$. When combining DES 3$\times$2pt data with available baryon acoustic oscillation, redshift-space distortion, and type Ia supernovae data, we find $p=0.34$. Combining all of these data sets with Planck CMB lensing yields joint parameter constraints of $S_8 = 0.812^{+0.008}_{-0.008}$, $\Omega_{\mathrm{m}} = 0.306^{+0.004}_{-0.005}$, $h=0.680^{+0.004}_{-0.003}$, and $\sum m_{
u}<0.13 \;\mathrm{eV\; (95\% \;CL)}$ in $\Lambda$CDM; $S_8 = 0.812^{+0.008}_{-0.008}$, $\Omega_{\mathrm{m}} = 0.302^{+0.006}_{-0.006}$, $h=0.687^{+0.006}_{-0.007}$, and $w=-1.031^{+0.030}_{-0.027}$ in $w$CDM. (abridged)
272 citations
TL;DR: In this paper, the authors examined the role of measurements of the growth rate in arbitrating the $S_8$ discrepancy, considering measurements of $f\sigma_8(z)$ from Redshift-Space Distortions (RSD) from Baryon Acoustic Oscillations (BAO) and Type Ia Supernovae (SNeIa).
Abstract: Within the $\Lambda$CDM model, measurements from recent Cosmic Microwave Background (CMB) and weak lensing (WL) surveys have uncovered a $\sim 3\sigma$ disagreement in the inferred value of the parameter $S_8 \equiv \sigma_8\sqrt{\Omega_m/0.3}$, quantifying the amplitude of late-time matter fluctuations. Before questioning whether the $S_8$ discrepancy calls for new physics, it is important to assess the view of measurements other than CMB and WL ones on the discrepancy. Here, we examine the role of measurements of the growth rate $f(z)$ in arbitrating the $S_8$ discrepancy, considering measurements of $f\sigma_8(z)$ from Redshift-Space Distortions (RSD). Our baseline analysis combines RSD measurements with geometrical measurements from Baryon Acoustic Oscillations (BAO) and Type Ia Supernovae (SNeIa), given the key role of the latter in constraining $\Omega_m$. From this combination and within the $\Lambda$CDM model we find $S_8 = 0.762^{+0.030}_{-0.025}$, and quantify the agreement between RSD+BAO+SNeIa and \textit{Planck} to be at the $2.2\sigma$ level: the mild disagreement is therefore compatible with a statistical fluctuation. We discuss combinations of RSD measurements with other datasets, including the $E_G$ statistic. This combination increases the discrepancy with \textit{Planck}, but we deem it significantly less robust. Our earlier results are stable against an extension where we allow the dark energy equation of state $w$ to vary. We conclude that, from the point of view of combined growth rate and geometrical measurements, there are hints, but no strong evidence yet, for the \textit{Planck}$\Lambda$CDM cosmology over-predicting the amplitude of matter fluctuations at redshifts $z \lesssim 1$. From this perspective, it might therefore still be premature to claim the need for new physics from the $S_8$ discrepancy.
64 citations
01 Jan 2016
TL;DR: In this article, the scale-dependent assembly bias of the two-point clustering of dark matter halos is studied and shown to be influenced by halo properties besides mass, a phenomenon referred to as halo assembly bias.
Abstract: The two-point clustering of dark matter halos is influenced by halo properties besides mass, a phenomenon referred to as halo assembly bias. Using the depth of the gravitational potential well, Vmax; as our secondary halo property, in this paper we present the first study of the scale-dependence assembly bias. In the large-scale linear regime, r & 10Mpc=h; our findings are in keeping with previous results. In particular, at the low-mass end (Mvir < Mcoll 10 12:5 M =h), halos with high-Vmax show stronger large-scale clustering relative to halos with low-Vmax of the same mass; this trend weakens and reverses for Mvir & Mcoll: In the nonlinear regime, assembly bias in low-mass halos exhibits a pronounced scale-dependent “bump” at 500kpc=h 5Mpc=h; a new result. This feature weakens and eventually vanishes for halos of higher mass. We show that this scale-dependent signature can primarily be attributed to a special subpopulation of ejected halos, defined as present-day host halos that were previously members of a higher-mass halo at some point in their past history. A corollary of our results is that galaxy clustering on scales of r 1 2Mpc=h can be impacted by up to 15% by the choice of the halo property used in the halo model, even for stellar mass-limited samples.
37 citations
TL;DR: In this article, the authors employ the hydrodynamical simulation IllustrisTNG-300-1 to explore the halo occupation distribution and environmental dependence of luminous star-forming emission-line galaxies (ELGs) at $z \sim 1$.
Abstract: We employ the hydrodynamical simulation IllustrisTNG-300-1 to explore the halo occupation distribution (HOD) and environmental dependence of luminous star-forming emission-line galaxies (ELGs) at $z \sim 1$. Such galaxies are key targets for current and upcoming cosmological surveys. We select model galaxies through cuts in colour-colour space allowing for a direct comparison with the Extended Baryon Oscillation Spectroscopic Survey and the Dark Energy Spectroscopic Instrument (DESI) surveys and then compare them with galaxies selected based on specific star-formation rate (sSFR) and stellar mass. We demonstrate that the ELG populations are twice more likely to reside in lower-density regions (sheets) compared with the mass-selected populations and twice less likely to occupy the densest regions of the cosmic web (knots). We also show that the colour-selected and sSFR-selected ELGs exhibit very similar occupation and clustering statistics, finding that the agreement is best for lower redshifts. In contrast with the mass-selected sample, the occupation of haloes by a central ELG peaks at $\sim$20\%. We furthermore explore the dependence of the HOD and the auto-correlation on environment, noticing that at fixed halo mass, galaxies in high-density regions cluster about 10 times more strongly than low-density ones. This result suggests that we should model carefully the galaxy-halo relation and implement assembly bias effects into our models (estimated at $\sim$4\% of the clustering of the DESI colour-selected sample at $z = 0.8$). Finally, we apply a simple mock recipe to recover the clustering on large scales ($r \gtrsim 1 \ {\rm Mpc}/h$) to within 1\% by augmenting the HOD model with an environment dependence, demonstrating the power of adopting flexible population models.
34 citations
TL;DR: In this article , the authors evaluate the consistency between lensing and clustering based on measurements from BOSS combined with galaxy-galaxy lensing from DES-Y3, HSC-Y1, KiDS-1000.
Abstract:
We evaluate the consistency between lensing and clustering based on measurements from BOSS combined with galaxy–galaxy lensing from DES-Y3, HSC-Y1, KiDS-1000. We find good agreement between these lensing datasets. We model the observations using the Dark Emulator and fit the data at two fixed cosmologies: Planck (S8 = 0.83), and a Lensing cosmology (S8 = 0.76). For a joint analysis limited to large scales, we find that both cosmologies provide an acceptable fit to the data. Full utilisation of the higher signal–to–noise small-scale measurements is hindered by uncertainty in the impact of baryon feedback and assembly bias, which we account for with a reasoned theoretical error budget. We incorporate a systematic inconsistency parameter for each redshift bin, A, that decouples the lensing and clustering. With a wide range of scales, we find different results for the consistency between the two cosmologies. Limiting the analysis to the bins for which the impact of the lens sample selection is expected to be minimal, for the Lensing cosmology, the measurements are consistent with A=1; A = 0.91 ± 0.04 (A = 0.97 ± 0.06) using DES+KiDS (HSC). For the Planck case, we find a discrepancy: A = 0.79 ± 0.03 (A = 0.84 ± 0.05) using DES+KiDS (HSC). We demonstrate that a kSZ-based estimate for baryonic effects alleviates some of the discrepancy in the Planck cosmology. This analysis demonstrates the statistical power of small-scale measurements, but caution is still warranted given modelling uncertainties and foreground sample selection effects.
32 citations
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TL;DR: In this paper, the authors present results based on full-mission Planck observations of temperature and polarization anisotropies of the CMB, which are consistent with the six-parameter inflationary LCDM cosmology.
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9,745 citations
TL;DR: In this article, the authors used high-resolution N-body simulations to study the equilibrium density profiles of dark matter halos in hierarchically clustering universes, and they found that all such profiles have the same shape, independent of the halo mass, the initial density fluctuation spectrum, and the values of the cosmological parameters.
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9,729 citations
"Evidence for galaxy assembly bias i..." refers methods in this paper
...Traditionally, the satellites in each halo are distributed according to a Navarro–Frenk–White profile (NFW) profile (Navarro et al. 1997)....
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TL;DR: The emcee algorithm as mentioned in this paper is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010).
Abstract: We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to ~N2 for a traditional algorithm in an N-dimensional parameter space. In this document, we describe the algorithm and the details of our implementation. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at http://dan.iel.fm/emcee under the GNU General Public License v2.
8,805 citations
TL;DR: This document introduces a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010).
Abstract: We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to $\sim N^2$ for a traditional algorithm in an N-dimensional parameter space. In this document, we describe the algorithm and the details of our implementation and API. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at this http URL under the MIT License.
5,293 citations
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