Percent-level constraints on baryonic feedback with spectral distortion measurements
Leander Thiele, Digvijay Wadekar, J. Colin Hill, Nicholas Battaglia,, Jens Chluba, Francisco Villaescusa-Navarro, Lars Hernquist, Mark, Vogelsberger, Daniel Angl\'es-Alc\'azar, Federico Marinacci

TL;DR
Spectral distortion measurements of the CMB can tightly constrain baryonic feedback processes, with machine learning and halo modeling reducing cosmic variance in simulation-based forecasts.
Contribution
This study introduces a novel approach combining hydrodynamic simulations, machine learning, and halo modeling to forecast constraints on baryonic feedback parameters from spectral distortion data.
Findings
Achieves ~2% constraint on feedback parameters in IllustrisTNG model.
Achieves ~0.2% constraint on feedback ratio in SIMBA model.
Demonstrates spectral distortions can significantly constrain baryonic feedback models.
Abstract
High-significance measurements of the monopole thermal Sunyaev-Zel'dovich CMB spectral distortions have the potential to tightly constrain poorly understood baryonic feedback processes. The sky-averaged Compton-y distortion and its relativistic correction are measures of the total thermal energy in electrons in the observable universe and their mean temperature. We use the CAMELS suite of hydrodynamic simulations to explore possible constraints on parameters describing the subgrid implementation of feedback from active galactic nuclei and supernovae, assuming a PIXIE-like measurement. The small 25 Mpc/h CAMELS boxes present challenges due to the significant cosmic variance. We utilize machine learning to construct interpolators through the noisy simulation data. Using the halo model, we translate the simulation halo mass functions into correction factors to reduce cosmic variance where…
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