Likelihood-free inference with neural compression of DES SV weak lensing map statistics

Advanced search
Browse by:

Department | Year

UCL Theses | Latest

Deposit your research

Likelihood-free inference with neural compression of DES SV weak lensing map statistics

Jeffrey, N; Alsing, J; Lanusse, F; (2021) Likelihood-free inference with neural compression of DES SV weak lensing map statistics. Monthly Notices of the Royal Astronomical Society , 501 (1) pp. 954-969. 10.1093/mnras/staa3594. Green open access

Preview

Text
staa3594.pdf - Published Version
Download (3MB) | Preview

Abstract

In many cosmological inference problems, the likelihood (the probability of the observed data as a function of the unknown parameters) is unknown or intractable. This necessitates approximations and assumptions, which can lead to incorrect inference of cosmological parameters, including the nature of dark matter and dark energy, or create artificial model tensions. Likelihood-free inference covers a novel family of methods to rigorously estimate posterior distributions of parameters using forward modelling of mock data. We present likelihood-free cosmological parameter inference using weak lensing maps from the Dark Energy Survey (DES) Science Verification data, using neural data compression of weak lensing map summary statistics. We explore combinations of the power spectra, peak counts, and neural compressed summaries of the lensing mass map using deep convolution neural networks. We demonstrate methods to validate the inference process, for both the data modelling and the probability density estimation steps. Likelihood-free inference provides a robust and scalable alternative for rigorous large-scale cosmological inference with galaxy survey data (for DES, Euclid, and LSST). We have made our simulated lensing maps publicly available.

Type:	Article
Title:	Likelihood-free inference with neural compression of DES SV weak lensing map statistics
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1093/mnras/staa3594
Publisher version:	http://doi.org/10.1093/mnras/staa3594
Language:	English
Additional information:	This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Science & Technology, Physical Sciences, Astronomy & Astrophysics, gravitational lensing: weak, methods: statistical, large-scale structure of Universe, KIDS-450 COSMOLOGICAL CONSTRAINTS, DARK-MATTER, PEAK STATISTICS, POWER SPECTRUM, COSMIC SHEAR, MODEL, RECONSTRUCTIONS, MAGNIFICATION, GALAXIES, CODE
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy
URI:	https://discovery.ucl.ac.uk/id/eprint/10141386