📝 SummarXiv

Abstract

Precision cosmology with Type Ia supernovae (SNe Ia) requires robust quality control of large, heterogeneous datasets. Current data processing often relies on manual, subjective rejection of photometric data, a practice that is not scalable for forthcoming surveys like the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). We present a Bayesian framework that automates this step by integrating anomaly detection directly into the light curve fitting process. While the framework is model-agnostic and compatible with any spectral energy distribution (SED) model, here we demonstrate its application with the SALT3 model, implemented fully on GPU using \texttt{JAX-bandflux} for computational efficiency. Our method models the probability of each photometric measurement being anomalous with respect to the model, simultaneously inferring the physical supernova parameters and each data point's posterior probability of being a contaminant. Applying this framework to the Hawaii Supernova Flows dataset, we demonstrate its three principal capabilities: (i) robust mitigation of isolated outliers; (ii) automated identification and rejection of entirely corrupted bandpasses; and (iii) preservation of valid data by flagging only specific anomalous points within otherwise usable filters. We also find that contaminants are systematically brighter and bluer, which if uncorrected could bias subsequent cosmological inference.