Generating synthetic cosmological data with GalSampler

Journal Article · 01 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]; Benson, Andrew  ^[3]; Aung, Han  ^[4]; Bradshaw, Christopher  ^[5]; Campbell, Duncan  ^[6]

---

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

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.

• 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: AC02-05CH11231; AC02-06CH11357; AC02-76SF00515
• OSTI ID: 1633138
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 4 Vol. 495; ISSN 0035-8711
• Publisher: Royal Astronomical SocietyCopyright Statement
• Country of Publication: United Kingdom
• Language: English