Speaker
Description
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. In particular, cross-correlating redshift-sliced galaxy samples with thermal Sunyaev–Zel’dovich (tSZ) and cosmic infrared background (CIB) maps enables measurements of the halo bias–weighted electron pressure ⟨bPe⟩ and star-formation rate density ⟨bρSFR⟩ as a function of redshift.
A key obstacle in this program is the dependence of reconstructed quantities on the clustering properties of the galaxy sample. I will present a systematic exploration of tomographic estimators and demonstrate, using the FLAMINGO hydrodynamic simulations, that a bias-independent estimator can be constructed. This estimator is explicitly insensitive to small-scale galaxy bias and remains robust across different galaxy selections. I will show that ⟨bPe⟩ and ⟨bρSFR⟩ can be reconstructed with 1–3% accuracy over a broad redshift range, and that their interpretation is well described by a halo-model framework provided accurate inputs for the halo mass function and large-scale bias are available.
Beyond a single fiducial cosmology, I will also discuss how tomographic tSZ cross-correlations respond to changes in halo abundance and structure growth. By extending the analysis to multiple cosmological models, this framework becomes a controlled laboratory to simultaneously test baryonic physics and cosmology. These results establish tomographic cross-correlations as a precision, bias-robust probe for next-generation surveys.