Characterizing the Sample Selection for Supernova Cosmology

Journal Article · 16 February 2021 · The Open Journal of Astrophysics

DOI: https://doi.org/10.21105/astro.2007.11100 · OSTI ID:1830087

Kim, Alex G.  ^[1]

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1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Type Ia supernovae (SNe Ia) are used as distance indicators to infer the cosmological parameters that specify the expansion history of the universe. Parameter inference depends on the criteria by which the analysis SN sample is selected. Only for the simplest selection criteria and population models can the likelihood be calculated analytically, otherwise it needs to be determined numerically, a process that inherently has error. Numerical errors in the likelihood lead to errors in parameter inference. This article presents toy examples where the distance modulus is inferred given a set of SNe at a single redshift. Parameter estimators and their uncertainties are calculated using Monte Carlo techniques. The relationship between the number of Monte Carlo realizations and numerical errors is presented. The procedure can be applied to more realistic models and used to determine the computational and data management requirements of the transient analysis pipeline.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• Contributing Organization: LSST Dark Energy Science Collaboration
• Grant/Contract Number: AC02-05CH11231; AC02-76SF00515
• OSTI ID: 1830087
• Journal Information: The Open Journal of Astrophysics, Journal Name: The Open Journal of Astrophysics Journal Issue: 1 Vol. 4; ISSN 2565-6120
• Publisher: Maynooth Academic PublishingCopyright Statement
• Country of Publication: United States
• Language: English