The impact of galaxy bias on cross-correlation tomography
Sara Maleubre, Matteo Zennaro, David Alonso, Ian McCarthy, Matthieu Schaller, Joop Schaye

TL;DR
This paper demonstrates that robust, bias-independent estimators for tomographic measurements of galaxy and large-scale structure can be constructed, enabling high-precision reconstruction of cosmic quantities across redshifts using simulations.
Contribution
It introduces a method to extract galaxy bias-independent tomographic measurements, validated with simulations, improving the interpretation of large-scale structure data.
Findings
Reconstructed $P_e$ and $ ho_{ m SFR}$ with 1-3% accuracy across redshifts.
Developed bias-independent estimators for galaxy clustering measurements.
Validated the approach using FLAMINGO hydrodynamic simulations.
Abstract
The cross-correlation of galaxies at different redshifts with other tracers of the large-scale structure can be used to reconstruct the cosmic mean of key physical quantities, and their evolution over billions of years, at high precision. However, a correct interpretation of these measurements must ensure that they are independent of the clustering properties of the galaxy sample used. In this paper we explore different prescriptions to extract tomographic reconstruction measurements and use the FLAMINGO hydrodynamic simulations to show that a robust estimator, independent of the small-scale galaxy bias, can be constructed. We focus on the tomographic reconstruction of the halo bias-weighted electron pressure and star-formation density , which can be reconstructed from tomographic analysis of Sunyaev-Zel'dovich and cosmic infrared…
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