CombineHarvesterFlow: Joint Probe Analysis Made Easy with Normalizing Flows

Abstract

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 $\mathcal{O}(10^9)$ samples from both measurements to compute the joint posterior in seconds -- saving up to $\mathcal{O}(1)$ ton of ${\text{CO}}_2$ per Monte Carlo run. Using this new technique we find joint constraints between the Dark Energy Survey $3\times 2$ point measurement, South Pole Telescope and Planck CMB lensing and a BOSS direct fit full shape analyses, for the first time. We find ${\Omega }_{\mathrm{m}}=0.32_{-0.01}^{+0.01}$ and $S_8=0.79_{-0.01}^{+0.01}$. We release a public package called {\tt CombineHarvesterFlow} (https://github.com/pltaylor16/CombineHarvesterFlow) which performs these calculations.

10 pages, 6 figures. Accepted to The Open Journal Of Astrophysics

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• DESI DR2 Results II: Measurements of Baryon Acoustic Oscillations and Cosmological Constraints
• DESI 2024 VII: Cosmological Constraints from the Full-Shape Modeling of Clustering Measurements
• Interacting dark energy constraints from the full-shape analyses of BOSS DR12 and DES Year 3 measurements
• Constraining cosmological parameters using void statistics from the SDSS survey
• $H_0$ Without the Sound Horizon (or Supernovae): A 2% Measurement in DESI DR1
• How to embed any likelihood into SBI: Application to Planck + Stage IV galaxy surveys and Dynamical Dark Energy