High-significance measurements of the monopole thermal Sunyaev-Zel'dovich cosmic microwave background 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 h???1 Mpc CAMELS boxes present challenges due to significant sample 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 sample variance where required. Our results depend on the subgrid model. In the case of IllustrisTNG, we find that the best-determined parameter combination can be measured to ~2% and corresponds to a product of active galactic nuclei (AGN) and supernova (SN) feedback. In the case of SIMBA, the tightest constraint is ~0.2% on a ratio between AGN and SN feedback. A second orthogonal parameter combination can be measured to ~8%. Our results demonstrate the significant constraining power a measurement of the late-time spectral distortion monopoles would have for baryonic feedback models.

Percent-level constraints on baryonic feedback with spectral distortion measurements

Marinacci, F
2022

Abstract

High-significance measurements of the monopole thermal Sunyaev-Zel'dovich cosmic microwave background 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 h???1 Mpc CAMELS boxes present challenges due to significant sample 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 sample variance where required. Our results depend on the subgrid model. In the case of IllustrisTNG, we find that the best-determined parameter combination can be measured to ~2% and corresponds to a product of active galactic nuclei (AGN) and supernova (SN) feedback. In the case of SIMBA, the tightest constraint is ~0.2% on a ratio between AGN and SN feedback. A second orthogonal parameter combination can be measured to ~8%. Our results demonstrate the significant constraining power a measurement of the late-time spectral distortion monopoles would have for baryonic feedback models.
Thiele, L; Wadekar, D; Hill, JC; Battaglia, N; Chluba, J; Villaescusa-Navarro, F; Hernquist, L; Vogelsberger, M; Angles-Alcazar, D; Marinacci, F
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/900661
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