Euclid: An emulator for baryonic effects on the matter bispectrum
P. A. Burger (1), G. Aric\`o (2, 3), L. Linke (4), R. E. Angulo (5, 6), J. C. Broxterman (7, 8), J. Schaye (8), M. Schaller (8, 7), M. Zennaro (9), A. Halder (10, 11, 12, 13), L. Porth (14), S. Heydenreich (15, 14), M. J. Hudson (16, 1, 17), A. Amara (18), S. Andreon (19)

TL;DR
This paper introduces a fast neural network emulator for baryonic effects on the matter bispectrum, enabling precise modeling of non-Gaussian statistics crucial for Euclid's cosmological analyses.
Contribution
It develops the first high-accuracy emulator for baryonic impacts on the matter bispectrum using advanced simulation techniques and neural networks, covering a wide parameter space.
Findings
Emulator achieves better than 2% accuracy for most configurations.
Validates robustness with Euclid mock data and FLAMINGO simulations.
Supports unbiased cosmological inference from small-scale bispectrum data.
Abstract
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 -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…
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