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Supernova Cosmology in the Big Data Era

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Handbook of Supernovae

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Abstract

Here we describe large “Big Data” Supernova (SN) Ia surveys, past and present, used to make precision measurements of cosmological parameters that describe the expansion history of the universe. In particular, we focus on surveys designed to measure the dark energy equation of state parameter w and its dependence on cosmic time. These large surveys have at least four photometric bands, and they use a rolling search strategy in which the same instrument is used for both discovery and photometric follow-up observations. These surveys include the Supernova Legacy Survey (SNLS), Sloan Digital Sky Survey II (SDSS-II), Pan-STARRS 1 (PS1), Dark Energy Survey (DES), and Large Synoptic Survey Telescope (LSST). We discuss the development of how systematic uncertainties are evaluated, and how methods to reduce them play a major role is designing new surveys. The key systematic effects that we discuss are (1) calibration, measuring the telescope efficiency in each filter band, (2) biases from a magnitude-limited survey and from the analysis, and (3) photometric SN classification for current surveys that don’t have enough resources to spectroscopically confirm each SN candidate.

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Notes

  1. 1.

    Most of the low-z SNe Ia used in cosmology analyses are from the Center for Astrophysics (Hicken et al. 2009, CfA), Carnegie Supernova Project (Contreras et al. 2010, CSP), and Lick Observatory Supernova Search (Ganeshalingam et al. 2013, LOSS).

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Authors and Affiliations

  1. Department of Astronomy and Astrophysics, Kavli Institute for Cosmological Physics, University of Chicago, Chicago, IL, USA

    Richard Kessler

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  1. Richard Kessler
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  1. University College London Observatory, University College London, London, United Kingdom

    Athem W. Alsabti

  2. Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom

    Paul Murdin

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Kessler, R. (2017). Supernova Cosmology in the Big Data Era. In: Alsabti, A., Murdin, P. (eds) Handbook of Supernovae. Springer, Cham. https://doi.org/10.1007/978-3-319-21846-5_107

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