Evaluating the Calibration of SN Ia Anchor Datasets with a Bayesian Hierarchical Model

TL;DR

This paper introduces X-CALIBUR, a hierarchical Bayesian method for recalibrating SN Ia datasets by accounting for spatial and color variations, reducing systematic uncertainties in cosmological measurements.

Contribution

The paper presents a novel hierarchical Bayesian model for recalibrating SN Ia datasets, incorporating spatially variable zeropoints and multiple color calibrators to improve calibration accuracy.

Findings

Reduced calibration uncertainties in SN Ia datasets.

Quantified covariance of magnitude offsets and bandpass shifts.

Enhanced potential for precise cosmological measurements.

Abstract

Inter-survey calibration remains an important systematic uncertainty in cosmological studies using type Ia supernova (SNe Ia). Ideally, each survey would measure its system throughputs, for instance with bandpass measurements combined with observations of well-characterized spectrophotometric standard stars; however, many important nearby-SN surveys have not done this. We recalibrate these surveys by tying their tertiary survey stars to Pan-STARRS1 g, r, and i, and SDSS/CSP u. This improves upon previous recalibration efforts by taking the spatially variable zeropoints of each telescope/camera into account, and applying improved color transformations in the surveys' natural instrumental photometric systems. Our analysis uses a global hierarchical model of the data which produces a covariance matrix of magnitude offsets and bandpass shifts, quantifying and reducing the systematic uncertainties in the calibration. We call our method CROSS-CALIBration with a Uniform Reanalysis (X-CALIBUR). This approach gains not only from a sophisticated analysis, but also from simply tying our calibration to more color calibrators, rather than just the one color calibrator (BD+17 4708) as many previous efforts have done. The results presented here have the potential to help understand and improve calibration uncertainties upcoming SN Ia cosmological analyses.