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Title: Generating synthetic cosmological data with GalSampler

Abstract

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.

Authors:
 [1];  [2];  [1]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [5]; ORCiD logo [6];  [7]
  1. Argonne National Laboratory, Lemont, IL 60439, USA
  2. Argonne National Laboratory, Lemont, IL 60439, USA, Department of Physics, University of Chicago, Chicago, IL 60637, USA
  3. Carnegie Observatories, 813 Santa Barbara Street, Pasadena, CA 91101, USA
  4. Department of Physics, Yale University, P.O. Box 208120, New Haven, CT 06520, USA
  5. Department of Astronomy and Astrophysics, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064, USA
  6. McWilliams Center for Cosmology and Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA
  7. (
Publication Date:
Research Org.:
Argonne National Laboratory (ANL), Argonne, IL (United States). Laboratory Computing Resource Center (LCRC); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
National Science Foundation (NSF); USDOE
Contributing Org.:
The LSST Dark Energy Science Collaboration
OSTI Identifier:
1633138
Alternate Identifier(s):
OSTI ID: 1660724
Grant/Contract Number:  
AC02-06CH11357; AC02-05CH11231; AC02-76SF00515; PHY-1607611
Resource Type:
Published Article
Journal Name:
Monthly Notices of the Royal Astronomical Society
Additional Journal Information:
Journal Name: Monthly Notices of the Royal Astronomical Society Journal Volume: 495 Journal Issue: 4; Journal ID: ISSN 0035-8711
Publisher:
Royal Astronomical Society
Country of Publication:
United Kingdom
Language:
English
Subject:
70 PLASMA PHYSICS AND FUSION TECHNOLOGY; 79 ASTRONOMY AND ASTROPHYSICS; cosmology: large-scale structure of Universe

Citation Formats

Hearin, Andrew, Korytov, Danila, Kovacs, Eve, Benson, Andrew, Aung, Han, Bradshaw, Christopher, Campbell, Duncan, and The LSST Dark Energy Science Collaboration). Generating synthetic cosmological data with GalSampler. United Kingdom: N. p., 2020. Web. doi:10.1093/mnras/staa1495.
Hearin, Andrew, Korytov, Danila, Kovacs, Eve, Benson, Andrew, Aung, Han, Bradshaw, Christopher, Campbell, Duncan, & The LSST Dark Energy Science Collaboration). Generating synthetic cosmological data with GalSampler. United Kingdom. doi:https://doi.org/10.1093/mnras/staa1495
Hearin, Andrew, Korytov, Danila, Kovacs, Eve, Benson, Andrew, Aung, Han, Bradshaw, Christopher, Campbell, Duncan, and The LSST Dark Energy Science Collaboration). Tue . "Generating synthetic cosmological data with GalSampler". United Kingdom. doi:https://doi.org/10.1093/mnras/staa1495.
@article{osti_1633138,
title = {Generating synthetic cosmological data with GalSampler},
author = {Hearin, Andrew and Korytov, Danila and Kovacs, Eve and Benson, Andrew and Aung, Han and Bradshaw, Christopher and Campbell, Duncan and The LSST Dark Energy Science Collaboration)},
abstractNote = {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.},
doi = {10.1093/mnras/staa1495},
journal = {Monthly Notices of the Royal Astronomical Society},
number = 4,
volume = 495,
place = {United Kingdom},
year = {2020},
month = {6}
}

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DOI: https://doi.org/10.1093/mnras/staa1495

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