Monte Carlo control loops for cosmic shear cosmology with DES Year 1 data

Kacprzak, Kacprzak T. and Herbel, J. and Desai, Shantanu and et al, . (2020) Monte Carlo control loops for cosmic shear cosmology with DES Year 1 data. Physical Review D, 101 (8). pp. 1-29. ISSN 2470-0010

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Abstract

Weak lensing by large-scale structure is a powerful probe of cosmology and of the dark universe. This cosmic shear technique relies on the accurate measurement of the shapes and redshifts of background galaxies and requires precise control of systematic errors. Monte Carlo control loops (MCCL) is a forward modeling method designed to tackle this problem. It relies on the ultra fast image generator (UFig) to produce simulated images tuned to match the target data statistically, followed by calibrations and tolerance loops. We present the first end-To-end application of this method, on the Dark Energy Survey (DES) Year 1 wide field imaging data. We simultaneously measure the shear power spectrum Câ"and the redshift distribution n(z) of the background galaxy sample. The method includes maps of the systematic sources, point spread function (PSF), an approximate Bayesian computation (ABC) inference of the simulation model parameters, a shear calibration scheme, and a fast method to estimate the covariance matrix. We find a close statistical agreement between the simulations and the DES Y1 data using an array of diagnostics. In a nontomographic setting, we derive a set of Câ"and n(z) curves that encode the cosmic shear measurement, as well as the systematic uncertainty. Following a blinding scheme, we measure the combination of ωm, σ8, and intrinsic alignment amplitude AIA, defined as S8DIA=σ8(ωm/0.3)0.5DIA, where DIA=1-0.11(AIA-1). We find S8DIA=0.895-0.039+0.054, where systematics are at the level of roughly 60% of the statistical errors. We discuss these results in the context of earlier cosmic shear analyses of the DES Y1 data. Our findings indicate that this method and its fast runtime offer good prospects for cosmic shear measurements with future wide-field surveys. © 2020 American Physical Society.

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IITH Creators:
IITH CreatorsORCiD
Desai, Shantanuhttp://orcid.org/0000-0002-0466-3288
Item Type: Article
Additional Information: We thank Joel Bergé, Chihway Chang and Lukas Gamper for crucial contributions to the development of ufig and the MCCL method. We thank Janis Fluri for helpful conversations and help with deep learning aspect of PSF modeling. We thank Uwe Schmitt and Jarunan Panyasantisuk for informatics support. We acknowledge support by Grant No. 200021_169130 from the Swiss National Science Foundation. We acknowledge the support of Euler cluster by High Performance Computing Group from ETHZ Scientific IT Services, as well as the support of the Piz Daint cluster by the Swiss National Supercomputing Center (CSCS). Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Ka ...
Uncontrolled Keywords: cosmology ,Monte Carlo control loops,statistical agreement
Subjects: Physics
Physics > Astronomy Astrophysics
Divisions: Department of Physics
Depositing User: . LibTrainee 2021
Date Deposited: 15 Nov 2022 05:56
Last Modified: 15 Nov 2022 05:56
URI: http://raiith.iith.ac.in/id/eprint/11256
Publisher URL: http://doi.org/10.1103/PhysRevD.101.082003
OA policy: https://v2.sherpa.ac.uk/id/publication/32263
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