Generating synthetic cosmological data with GalSampler

Hearin, Andrew; Korytov, Danila; Kovacs, Eve; Benson, Andrew; Aung, Han; Bradshaw, Christopher; Campbell, Duncan

doi:10.1093/mnras/staa1495

Title: Generating synthetic cosmological data with GalSampler

Journal Article · 02 June 2020 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/staa1495 · OSTI ID:1633138

Hearin, Andrew ^[1]; Korytov, Danila ^[2]; Kovacs, Eve ^[1];

^[3];

^[4];

^[5];

^[6]

Argonne National Lab. (ANL), Lemont, IL (United States)
Argonne National Lab. (ANL), Lemont, IL (United States); Univ. of Chicago, IL (United States)
Carnegie Observatories, Pasadena, CA (United States)
Yale Univ., New Haven, CT (United States)
Univ. of California, Santa Cruz, CA (United States)
Carnegie Mellon Univ., Pittsburgh, PA (United States)

As part of the effort to meet the needs of the Large Synoptic Survey Telescope Dark Energy Science Collaboration (LSST DESC) for accurate, realistically complex mock galaxy catalogues, in this work we have developed galsampler, an open-source python package that assists in generating large volumes of synthetic cosmological data. The key idea behind galsampler is to recast hydrodynamical simulations and semi-analytic models as physically motivated galaxy libraries. galsampler populates a new, larger volume halo catalogue with galaxies drawn from the baseline library; by using weighted sampling guided by empirical modelling techniques, galsampler inherits statistical accuracy from the empirical model and physically motivated complexity from the baseline library. We have recently used galsampler to produce the cosmoDC2 extragalactic catalogue made for the LSST DESC Data Challenge 2. Using cosmoDC2 as a guiding example, we outline how galsampler can continue to support ongoing and near-future galaxy surveys such as the Dark Energy Survey, the Dark Energy Spectroscopic Instrument, WFIRST, and Euclid.

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Research Organization:: Argonne National Laboratory (ANL), Argonne, IL (United States). Laboratory Computing Resource Center (LCRC); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)

Sponsoring Organization:: National Science Foundation (NSF); USDOE

Contributing Organization:: The LSST Dark Energy Science Collaboration

Grant/Contract Number:: AC02-06CH11357; PHY-1607611; AC02-05CH11231; AC02-76SF00515

OSTI ID:: 1633138

Alternate ID(s):: OSTI ID: 1660724

Journal Information:: Monthly Notices of the Royal Astronomical Society, Vol. 495, Issue 4; ISSN 0035-8711

Publisher:: Royal Astronomical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 13 works

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