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

TL;DR

This paper demonstrates that unbinned, unsmoothed covariance matrices significantly improve the self-calibration of systematic uncertainties in Type Ia Supernova cosmological analyses, especially with large datasets from upcoming surveys.

Contribution

It introduces a self-calibration approach that matches covariance-matrix methods, showing that avoiding binning and smoothing enhances systematic error reduction.

Findings

Unbinned covariance matrices reduce systematic uncertainties by ~1.5×.

Self-calibration effectiveness increases with dataset size.

Binning and smoothing hinder the self-calibration process.

Abstract

Recent cosmological analyses (e.g., JLA, Pantheon) of Type Ia Supernova (SNIa) have propagated systematic uncertainties into a covariance matrix and either binned or smoothed the systematic vectors in redshift space. We demonstrate that systematic error budgets of these analyses can be improved by a factor of $\sim1.5\times$ 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 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 consistent with varying the cosmological model and whether or not the systematic can be described by additional correlations between SN properties and luminosity. Furthermore, we show that the power of self-calibration increases with the size of the dataset, 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 methodologies.