Search for: All records

Abstract There is untapped cosmological information in galaxy redshift surveys in the nonlinear regime. In this work, we use theAemulussuite of cosmologicalN-body simulations to construct Gaussian process emulators of galaxy clustering statistics at small scales (0.1–50h⁻¹Mpc) in order to constrain cosmological and galaxy bias parameters. In addition to standard statistics—the projected correlation functionw_p(r_p), the redshift-space monopole of the correlation functionξ₀(s), and the quadrupoleξ₂(s)—we emulate statistics that include information about the local environment, namely the underdensity probability functionP_U(s) and the density-marked correlation functionM(s). This extends the model ofAemulusIII for redshift-space distortions by including new statistics sensitive to galaxy assembly bias. In recovery tests, we find that the beyond-standard statistics significantly increase the constraining power on cosmological parameters of interest: includingP_U(s) andM(s) improves the precision of our constraints on Ω_mby 27%,σ₈by 19%, and the growth of structure parameter,fσ₈, by 12% compared to standard statistics. We additionally find that scales below ∼6h⁻¹Mpc contain as much information as larger scales. The density-sensitive statistics also contribute to constraining halo occupation distribution parameters and a flexible environment-dependent assembly bias model, which is important for extracting the small-scale cosmological information as well as understanding the galaxy–halo connection. This analysis demonstrates the potential of emulating beyond-standard clustering statistics at small scales to constrain the growth of structure as a test of cosmic acceleration. 

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 analyze clustering measurements of BOSS galaxies using a simulation-based emulator of two-point statistics. We focus on the monopole and quadrupole of the redshift-space correlation function, and the projected correlation function, at scales of 0.1 ∼ 60h⁻¹Mpc. Although our simulations are based onwCDM with general relativity (GR), we include a scaling parameter of the halo velocity field,γ_f, defined as the amplitude of the halo velocity field relative to the GR prediction. We divide the BOSS data into three redshift bins. After marginalizing over other cosmological parameters, galaxy bias parameters, and the velocity scaling parameter, we findfσ₈(z= 0.25) = 0.413 ± 0.031,fσ₈(z= 0.4) = 0.470 ± 0.026, andfσ₈(z= 0.55) = 0.396 ± 0.022. Compared with Planck observations using a flat Lambda cold dark matter model, our results are lower by 1.9σ, 0.3σ, and 3.4σ, respectively. These results are consistent with other recent simulation-based results at nonlinear scales, including weak lensing measurements of BOSS LOWZ galaxies, two-point clustering of eBOSS LRGs, and an independent clustering analysis of BOSS LOWZ. All these results are generally consistent with a combination of ${\gamma }_f^{1/2}{\sigma }_8\approx 0.75$. We note, however, that the BOSS data is well fit assuming GR, i.e.,γ_f= 1. We cannot rule out an unknown systematic error in the galaxy bias model at nonlinear scales, but near-future data and modeling will enhance our understanding of the galaxy–halo connection, and provide a strong test of new physics beyond the standard model. 

ABSTRACT With the advent of future big-data surveys, automated tools for unsupervised discovery are becoming ever more necessary. In this work, we explore the ability of deep generative networks for detecting outliers in astronomical imaging data sets. The main advantage of such generative models is that they are able to learn complex representations directly from the pixel space. Therefore, these methods enable us to look for subtle morphological deviations which are typically missed by more traditional moment-based approaches. We use a generative model to learn a representation of expected data defined by the training set and then look for deviations from the learned representation by looking for the best reconstruction of a given object. In this first proof-of-concept work, we apply our method to two different test cases. We first show that from a set of simulated galaxies, we are able to detect $${\sim}90{{\ \rm per\ cent}}$$ of merging galaxies if we train our network only with a sample of isolated ones. We then explore how the presented approach can be used to compare observations and hydrodynamic simulations by identifying observed galaxies not well represented in the models. The code used in this is available at https://github.com/carlamb/astronomical-outliers-WGAN.