Matteo Zennaro

Abstract:

The Euclid mission and other next-generation large-scale structure surveys will enable high-precision measurements of the cosmic matter distribution. Understanding the impact of baryonic processes such as star formation and active galactic nuclei (AGN) feedback on matter clustering is crucial to ensure precise and unbiased cosmological inference. Most theoretical models of baryonic effects to date focus on two-point statistics, neglecting higher-order contributions. This work develops a fast and accurate emulator for baryonic effects on the matter bispectrum, a key non-Gaussian statistic in the nonlinear regime. We employ high-resolution N -body simulations from the BACCO suite and apply a combination of cutting-edge techniques such as cosmology scaling and baryonification to efficiently span a large cosmological and astrophysical parameter space. A deep neural network is trained to emulate baryonic effects on the matter bispectrum measured in simulations, capturing modifications across various scales and redshifts relevant to Euclid . We validate the emulator accuracy and robustness using an analysis of Euclid mock data, employing predictions from the state-of-the-art FLAMINGO hydrodynamical simulations. The emulator reproduces baryonic suppression in the bispectrum to better than 2% for the 68% percentile across most triangle configurations for k ∈ [0.01, 20] h Mpc −1 and ensures consistency between cosmological posteriors inferred from second- and third-order weak lensing statistics. These results demonstrate that our emulator meets the high-precision requirements of the Euclid mission for at least the first data release and provides reliable forecasts of the cosmological information contained in the small-scale matter bispectrum. This underscores the potential of emulation techniques to bridge the gap between complex baryonic physics and observational data, maximising the scientific output of Euclid .

Abstract:

The clustering of galaxies and galaxy–galaxy lensing are two of the main observational probes in Stage-IV large-scale structure surveys, such as Euclid and LSST. Unfortunately, the complicated relationship between galaxies and matter greatly limits the exploitation of this data. Sophisticated theoretical galaxy bias models–such as the hybrid Lagrangian bias expansion – allow describing galaxy clustering down to scales as small as . However, the galaxy–matter cross-power spectra are already affected by baryons on these scales, directly impacting the modelling of galaxy–galaxy lensing. In this work, we propose a way to extend state-of-the-art models of the galaxy–matter cross-power spectrum (currently only accounting for dark matter) by including a baryonic correction term inferred from the matter component [the suppression ], so that . We use the FLAMINGO hydrodynamical simulations to measure the effect of baryons on the galaxy–matter cross-power spectrum and to assess the performance of our model. Specifically, we perform a Bayesian analysis of synthetic data, implementing a model based on BACCO’s hybrid Lagrangian bias expansion (for the non-linear galaxy bias) and Baryon Correction Model (for the baryon suppression of the matter power spectrum). Ignoring the effect of baryons on the galaxy–matter cross-power spectrum leads to a biased inference of the galaxy bias parameters, while ignoring baryons in both the galaxy–matter and matter–matter power spectra leads to a biased inference of both the galaxy bias and cosmological parameters. In contrast, our method is 1 per cent accurate compared to all physics variations in FLAMINGO and on all scales described by hybrid perturbative models (). Moreover, our model leads to inferred bias and cosmological parameters compatible within 1 with their reference values. We anticipate that our method will be a promising candidate for analysing forthcoming Stage-IV survey data.

Abstract:

Standard cosmological weak lensing analyses using cosmic shear are inevitably sensitive to small-scale, non-linear clustering from low-redshift structures. The need to adequately model the clustering of matter on this non-linear regime, accounting for both gravitational and baryonic effects, adds significant uncertainty to weak lensing studies, particularly in the context of near-future Stage-IV datasets. In this paper, inspired by previous work on so-called “nulling” techniques, we present a general method that selects the linear combinations of a given tomographic cosmic shear dataset that are least sensitive to small-scale non-linearities, by essentially suppressing the contribution from low-redshift structures. We apply this method to the latest public cosmic shear data from the Dark Energy Survey, DES-Y3, that corresponds to 3 years of observation, and show: a) that a large fraction of the signal is dominated by the single mode that is most affected by non-linear scales, and b) that removing this mode leads to a ∼ 1σ upwards shift in the preferred value of S 8 ≡ σ 8√(ΩM/0.3), alleviating the tension with current CMB data. However, the removal of the most contaminated mode also results in a significant increase in the statistical uncertainties. Taking this into account, we find this shift to be compatible with a random fluctuation caused by removing this most-contaminated mode at the ∼ 1.4σ level. We also show that this technique may be used by future Stage-IV surveys to mitigate the sensitivity of the final constraints to baryonic effects, trading precision for robustness.

Abstract:

The past few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime and the canonical two-point (2pt) statistics. We test and benchmark some of these new techniques in a community data challenge named “Beyond-2pt,” initiated during the Aspen 2022 Summer Program “Large-Scale Structure Cosmology beyond 2-Point Statistics,” whose first round of results we present here. The challenge data set consists of high-precision mock galaxy catalogs for clustering in real space, in redshift space, and on a light cone. Participants in the challenge have developed end-to-end pipelines to analyze mock catalogs and extract unknown (“masked”) cosmological parameters of the underlying ΛCDM models with their methods. The methods represented are density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using both EFT and simulation-based inference. In this work, we review the results of the challenge, focusing on problems solved, lessons learned, and future research needed to perfect the emerging beyond-2pt approaches. The unbiased parameter recovery demonstrated in this challenge by multiple statistics and the associated modeling and inference frameworks supports the credibility of cosmology constraints from these methods. The challenge data set is publicly available, and we welcome future submissions from methods that are not yet represented.