Binning is Sinning (Supernova Version): The Impact of Self-calibration in Cosmological Analyses with Type Ia Supernovae

Brout, Dillon; Hinton, Samuel R.; Scolnic, Dan

doi:10.3847/2041-8213/abf4db

Title: Binning is Sinning (Supernova Version): The Impact of Self-calibration in Cosmological Analyses with Type Ia Supernovae

Journal Article · Mon May 10 00:00:00 EDT 2021 · The Astrophysical Journal. Letters

DOI:https://doi.org/10.3847/2041-8213/abf4db· OSTI ID:1851272

^[1];

^[2]; Scolnic, Dan ^[3]

Harvard & Smithsonian, Cambridge (United States). Center for Astrophysics.
University of Queensland, Australia. School of Mathematics and Physics
Duke Univ., Durham, NC (United States). Department of Physics

Recent cosmological analyses (e.g., JLA, Pantheon) of Type Ia supernovae (SNe Ia) have propagated systematic uncertainties into a covariance matrix and either binned or smoothed the systematic uncertainty vectors in redshift space. We demonstrate that systematic error budgets of these analyses can be improved by a factor of ~ 1.5 × with the use of unbinned and unsmoothed covariance matrices. To understand this, we employ a separate approach that simultaneously fits for cosmological parameters and additional self-calibrating scale parameters that constrain the size of each systematic. We show that the covariance-matrix approach and scale-parameter approach indeed yield equivalent results, implying that in both cases the data can self-calibrate certain systematic uncertainties, but that this ability is hindered when information is binned or smoothed in redshift space. We review the top systematic uncertainties in current analyses and find that the reduction of systematic uncertainties in the unbinned case depends on whether a systematic is solely degenerate with the cosmological model in redshift space or whether it can be described by additional correlations between supernova properties and luminosity. Furthermore, we show that the power of self-calibration increases with the size of the data set, which presents a tremendous opportunity for upcoming analyses of photometrically classified samples, like those of Legacy Survey of Space and Time (LSST) and the Nancy Grace Roman Telescope (NGRST). However, to take advantage of self-calibration in large, photometrically classified samples, we must first address the issue that binning is required in currently used photometric analysis methods.

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Research Organization:: Duke Univ., Durham, NC (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: SC0010007

OSTI ID:: 1851272

Journal Information:: The Astrophysical Journal. Letters, Vol. 912, Issue 2; ISSN 2041-8205

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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