Papers › CombineHarvesterFlow: Joint Probe Analysis Made Easy with Normalizing Flows
CombineHarvesterFlow: Joint Probe Analysis Made Easy with Normalizing Flows
Peter L. Taylor, Andrei Cuceu, Chun-Hao To, Erik A. Zaborowski
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We show how to efficiently sample the joint posterior of two non-covariant experiments with a large set of nuisance parameters. Specifically, we train an ensemble of normalizing flows to learn the posterior distribution of both experiments. Once trained, we can use the flows to reweight 𝒪 (10⁹) samples from both measurements to compute the joint posterior in seconds -- saving up to 𝒪(1) ton of CO₂ per Monte Carlo run. Using this new technique we find joint constraints between the Dark Energy Survey 3 ×2 point measurement, South Pole Telescope and Planck CMB lensing and a BOSS direct fit full shape analyses, for the first time. We find Ωₘ = 0.32^(+0.01)_(-0.01) and S₈ = 0.79 ^(+0.01)_(-0.01). We release a public package called {\tt CombineHarvesterFlow} (https://github.com/pltaylor16/CombineHarvesterFlow) which performs these calculations.
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