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Abstract Precise and accurate predictions of the halo mass function for cluster mass scales inwνCDM cosmologies are crucial for extracting robust and unbiased cosmological information from upcoming galaxy cluster surveys.Here, we present a halo mass function emulator for cluster mass scales (≳ 10¹³M_⊙/h) up to redshiftz= 2 with comprehensive support for the parameter space ofwνCDM cosmologies allowed by current data.Based on theAemulusνsuite of simulations, the emulator marks a significant improvement in the precision of halo mass function predictions by incorporating both massive neutrinos and non-standard dark energy equation of state models.This allows for accurate modeling of the cosmology dependence in large-scale structure and galaxy cluster studies.We show that the emulator, designed using Gaussian Process Regression, has negligible theoretical uncertainties compared to dominant sources of error in future cluster abundance studies.Our emulator is publicly available (https://github.com/DelonShen/aemulusnu_hmf), providing the community with a crucial tool for upcoming cosmological surveys such as LSST and Euclid. 

Abstract Active galactic nuclei (AGN) are the signposts of black hole growth, and likely play an important role in galaxy evolution. An outstanding question is whether AGN of different spectral types indicate different evolutionary stages in the coevolution of black holes and galaxies. We present the angular correlation function between an AGN sample selected from Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) optical photometry and Wide-field Infrared Survey Explorer mid-IR photometry and a luminous red galaxy (LRG) sample from HSC-SSP. We investigate AGN clustering strength as a function of luminosity and spectral features across three independent HSC fields totaling ∼600 deg², forz∈ 0.6 −1.2 and AGN withL_6μm> 3 × 10⁴⁴erg s⁻¹. There are ∼28,500 AGN and ∼1.5 million LRGs in our primary analysis. We determine the average halo mass for the full AGN sample (M_h≈ 10^12.9h⁻¹M_⊙), and note that it does not evolve significantly as a function of redshift (over this narrow range) or luminosity. We find that, on average, unobscured AGN (M_h≈ 10^13.3h⁻¹M_⊙) occupy ∼4.5× more massive halos than obscured AGN (M_h≈ 10^12.6h⁻¹M_⊙), at 5σstatistical significance using 1D uncertainties, and at 3σusing the full covariance matrix, suggesting a physical difference between unobscured and obscured AGN, beyond the line-of-sight viewing angle. Furthermore, we find evidence for a halo mass dependence on reddening level within the Type I AGN population, which could support the existence of a dust-obscured phase. However, we also find that quite small systematic shifts in the redshift distributions of the AGN sample could explain current and previously observed differences inM_h. 

Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the Rubin Observatory Legacy Survey of Space and Time (LSST) bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Year 10 (Y10) data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian perturbation theory (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. We consider a variety of different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale ofk_max = 0.4 Mpc^-1for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data. 

Abstract We present theAemulusνsimulations: a suite of 150 (1.05 h^-1Gpc)³N-body simulations with a mass resolution of 3.51 × 10¹⁰Ω_cb/0.3  h^-1M_⊙in awνCDM cosmological parameter space. The simulations have been explicitly designed to span a broad range inσ₈to facilitate investigations of tension between large scale structure and cosmic microwave background cosmological probes. Neutrinos are treated as a second particle species to ensure accuracy to 0.5 eV, the maximum neutrino mass that we have simulated. By employing Zel'dovich control variates, we increase the effective volume of our simulations by factors of 10-10⁵depending on the statistic in question. As a first application of these simulations, we build new hybrid effective field theory and matter power spectrum surrogate models, demonstrating that they achieve ≤ 1% accuracy fork≤ 1hMpc^-1and 0 ≤z≤ 3, and ≤ 2% accuracy fork≤ 4hMpc^-1for the matter power spectrum. We publicly release the trained surrogate models, and estimates of the surrogate model errors in the hope that they will be broadly applicable to a range of cosmological analyses for many years to come. 

ABSTRACT We implement a model for the two-point statistics of biased tracers that combines dark matter dynamics from N-body simulations with an analytic Lagrangian bias expansion. Using Aemulus, a suite of N-body simulations built for emulation of cosmological observables, we emulate the cosmology dependence of these non-linear spectra from redshifts z = 0 to z = 2. We quantify the accuracy of our emulation procedure, which is sub-per cent at $$k=1\, h \,{\rm Mpc}^{-1}$$ for the redshifts probed by upcoming surveys and improves at higher redshifts. We demonstrate its ability to describe the statistics of complex tracer samples, including those with assembly bias and baryonic effects, reliably fitting the clustering and lensing statistics of such samples at redshift z ≃ 0.4 to scales of $$k_{\rm max} \approx 0.6\, h\,\mathrm{Mpc}^{-1}$$. We show that the emulator can be used for unbiased cosmological parameter inference in simulated joint clustering and galaxy–galaxy lensing analyses with data drawn from an independent N-body simulation. These results indicate that our emulator is a promising tool that can be readily applied to the analysis of current and upcoming data sets from galaxy surveys. 

Abstract We use luminous red galaxies selected from the imaging surveys that are being used for targeting by the Dark Energy Spectroscopic Instrument (DESI) in combination with CMB lensing maps from the Planck collaboration to probe the amplitude of large-scale structure over 0.4 ≤  z  ≤ 1. Our galaxy sample, with an angular number density of approximately 500 deg -2 over 18,000 sq.deg., is divided into 4 tomographic bins by photometric redshift and the redshift distributions are calibrated using spectroscopy from DESI. We fit the galaxy autospectra and galaxy-convergence cross-spectra using models based on cosmological perturbation theory, restricting to large scales that are expected to be well described by such models. Within the context of ΛCDM, combining all 4 samples and using priors on the background cosmology from supernova and baryon acoustic oscillation measurements, we find S 8  = σ 8 (Ω m /0.3) 0.5  = 0.73 ± 0.03. This result is lower than the prediction of the ΛCDM model conditioned on the Planck data. Our data prefer a slower growth of structure at low redshift than the model predictions, though at only modest significance.