The Effective Field Theory of Large-Scale Structure and Multi-tracer

Abstract

We study the performance of the perturbative bias expansion when combined with the multi-tracer technique, and their impact on the extraction of cosmological parameters. We consider two populations of tracers of large-scale structure and perform a series of Markov chain Monte Carlo analysis for those two tracers separately. The constraints in ${\omega }_{\mathrm{cdm}}$ and $h$ using multi-tracer are less biased and approximately $60\%$ better than those obtained for a single tracer. The multi-tracer approach also provides stronger constraints on the bias expansion parameters, breaking degeneracies between them and with their error being typically half of the single-tracer case. Finally, we studied the impacts caused in parameter extraction when including a correlation between the stochastic field of distinct tracers. We also include a study with galaxies showing that multi-tracer still lead to substantial gains in the cosmological parameters.

32 pages, 14 figures. The new appendix includes galaxy analysis

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• Full-shape BOSS constraints on dark matter interacting with dark radiation and lifting the $S_8$ tension
• Learning to Concentrate: Multi-tracer Forecasts on Local Primordial Non-Gaussianity with Machine-Learned Bias
• Multi-tracer power spectra and bispectra: Formalism
• Cosmology before noon with multiple galaxy populations
• A multitracer analysis for the eBOSS galaxy sample based on the effective field theory of large-scale structure
• Selecting samples of galaxies with fewer Fingers-of-God
• Machine Learning Post-Minkowskian Integrals
• Detecting Relativistic Doppler by Multi-Tracing a Single Galaxy Population
• Towards cosmology with Void Lensing: how to find voids sensitive to weak-lensing and numerically interpret them
• Fisher matrix for multiple tracers: the information in the cross-spectra