Inferring the Impacts of Baryonic Feedback from Kinetic Sunyaev-Zeldovich Cross-Correlations

TL;DR

This paper introduces a data-driven neural network emulator that uses the kSZ effect to accurately infer the impact of baryonic feedback on the matter power spectrum, aiding understanding of galaxy evolution and cosmology.

Contribution

We develop the first fully data-driven power spectrum emulator using kSZ measurements, trained on hydrodynamical simulations, achieving sub-percent accuracy across various feedback models.

Findings

Emulator reconstructs matter power spectrum with sub-percent accuracy for relevant scales and redshifts.

Model remains robust for unseen feedback and cosmological parameters, maintaining percent-level precision.

Forecasts demonstrate potential for combined CMB and galaxy survey data to probe baryonic feedback effects.

Abstract

The complex processes of baryonic feedback associated with galaxy evolution are still poorly understood, and their impact on the clustering of matter on small scales remains difficult to quantify. While many fitting functions and emulators exist to model the matter power spectrum, their input parameters are not directly observable. However, recent studies using hydrodynamical simulations have identified a promising correlation between the gas content of halos and changes to the matter power spectrum from feedback. Building on these findings, we create the first fully data-driven power spectrum emulator. We utilize the kinematic Sunyaev-Zeldovich (kSZ) effect, a secondary anisotropy in the cosmic microwave background, as a tracer of free electrons in and around halos. We train a neural network to learn the mapping between the suppression of the matter power spectrum and the shape of the kSZ power spectrum extracted with a radial velocity template. We train and validate our algorithm using the FLAMINGO suite of hydrodynamical simulations, which encompasses a wide range of feedback models. Our emulator can reconstruct the matter power spectrum at the sub-percent level for scales $k\leq 5\;h/$Mpc and $0.2\leq z \leq 1.25$ directly from the data. Our model is robust and retains percent-level accuracy even for feedback models and cosmological parameter values not seen during training (except in a few extreme cases drastically different from the fiducial model). Due to its robustness, our algorithm offers a new way to identify the sources of suppression in the matter power spectrum, breaking the degeneracies between baryonic feedback and new physics. Finally, we present a forecast for reconstruction of the matter power spectrum combining maps of the microwave background anisotropies from a Simons Observatory-like experiment and galaxy catalogs from the Dark Energy Spectroscopic Instrument.