Multi-tracer beyond linear theory

Abstract

The multi-tracer (MT) technique has been shown to outperform single-tracer analyses in the context of galaxy clustering. In this paper, we conduct a series of Fisher analyses to further explore MT information gains within the framework of non-linear bias expansion. We examine how MT performance depends on the bias parameters of the subtracers, showing that directly splitting the non-linear bias generally leads to smaller error bars in $A_s$, $h$, and $\omega_{\rm cdm}$ compared to a simple split in $b_1$. This finding opens the door to identifying subsample splits that do not necessarily rely on very distinct linear biases. We discuss different total and subtracer number density scenarios, as well as the possibility of splitting into more than two tracers. Additionally, we consider how different Fingers-of-God suppression scales for the subsamples can be translated into different $k_{\rm max}$ values. Finally, we present forecasts for ongoing and future galaxy surveys.