Accelerating cosmological inference with Gaussian processes and neural networks - an application to LSST Y1 weak lensing and galaxy clustering

Supranta S. Boruah, Tim Eifler, Vivian Miranda, P. M.Sai Krishanth

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Studying the impact of systematic effects, optimizing survey strategies, assessing tensions between different probes and exploring synergies of different data sets require a large number of simulated likelihood analyses, each of which cost thousands of CPU hours. In this paper, we present a method to accelerate cosmological inference using emulators based on Gaussian process regression and neural networks. We iteratively acquire training samples in regions of high posterior probability which enables accurate emulation of data vectors even in high dimensional parameter spaces. We showcase the performance of our emulator with a simulated 3×2 point analysis of LSST-Y1 with realistic theoretical and systematics modelling. We show that our emulator leads to high-fidelity posterior contours, with an order of magnitude speed-up. Most importantly, the trained emulator can be re-used for extremely fast impact and optimization studies. We demonstrate this feature by studying baryonic physics effects in LSST-Y1 3×2 point analyses where each one of our MCMC runs takes approximately 5 min. This technique enables future cosmological analyses to map out the science return as a function of analysis choices and survey strategy.

Original languageEnglish (US)
Pages (from-to)4818-4831
Number of pages14
JournalMonthly Notices of the Royal Astronomical Society
Volume518
Issue number4
DOIs
StatePublished - Feb 1 2023

Keywords

  • cosmological parameters
  • gravitational lensing: weak
  • large-scale structure of Universe
  • methods: data analysis

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

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