Calibration-Induced Systematics in SALT3 Training and Their Impact on Dark Energy Constraints from Stage IV Supernova Surveys

TL;DR

This study quantifies how calibration uncertainties in supernova surveys impact dark energy constraints, revealing that calibration during light-curve fitting significantly degrades the figure of merit.

Contribution

It demonstrates that calibration uncertainties during light-curve fitting dominate over those during model training, affecting dark energy constraints in supernova surveys.

Findings

Calibration shifts of 5 mmag reduce FoM by ~50%.

Calibration during light-curve fitting causes more degradation than during model training.

FoM dependence on calibration uncertainty is roughly linear.

Abstract

In the coming years, the Vera Rubin Observatory's Legacy Survey of Space and Time (Rubin-LSST) and the Nancy Grace Roman Space Telescope's (Roman) High Latitude Time Domain Survey (HLTDS) are expected to discover more than a million Type Ia supernovae (SNe Ia), several orders of magnitude more than current samples and with a tighter control on systematic uncertainties. One of the largest systematic uncertainties in cosmological analyses with SNe Ia is the accuracy of the spectro-photometric model for SNe Ia time series data, which depends on the photometric calibration of the surveys. To quantify the impact of this uncertainty, we analyze simulated Rubin-LSST and HLTDS data, perturb the photometric zero-points and filter mean wavelengths, and propagate these systematics to spectral model recovery, estimated distances, and dark energy figure of merit (FoM) based on the $w_0 w_a$CDM model. Zero-point shifts of 5 mmag and filter mean wavelength shifts of 5 angstrom lead to a $\sim 50\%$ decrease in the FoM relative to a statistical-only case when calibration uncertainties are propagated only through light-curve fitting. The same calibration shifts applied only during model training produce a smaller $\sim 13\%$ degradation. Contrary to previous analyses, calibration uncertainties in light-curve fitting dominate over those from model training. Their effect during light-curve fitting varies smoothly with redshift and is nearly degenerate with cosmology, preventing mitigation through self-calibration. Finally, we show that the FoM dependence on the size of the calibration uncertainties (in the range of expected sizes) is roughly linear.