Reducing sample variance: halo biasing, non-linearity and stochasticity
H. Gil-Mar\'in, C. Wagner, L. Verde, R. Jimenez, A. F. Heavens

TL;DR
This paper develops a formalism to optimize the use of biased tracers in galaxy surveys to reduce sample variance and improve cosmological parameter measurements, accounting for non-linearity and stochasticity.
Contribution
It generalizes previous models by including bias non-linearities and stochasticity, providing a framework to optimize survey design and tracer selection for minimal error.
Findings
Sample variance reduction factor up to 0.6 for realistic haloes
Splitting tracers offers limited gain comparable to larger surveys
Realistic galaxy-halo relations may reduce the potential benefits
Abstract
Comparing clustering of differently biased tracers of the dark matter distribution offers the opportunity to reduce the cosmic variance error in the measurement of certain cosmological parameters. We develop a formalism that includes bias non-linearities and stochasticity. Our formalism is general enough that can be used to optimise survey design and tracers selection and optimally split (or combine) tracers to minimise the error on the cosmologically interesting quantities. Our approach generalises the one presented by McDonald & Seljak (2009) of circumventing sample variance in the measurement of . We analyse how the bias, the noise, the non-linearity and stochasticity affect the measurements of and explore in which signal-to-noise regime it is significantly advantageous to split a galaxy sample in two differently-biased tracers. We use N-body simulations…
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