TL;DR
PISCOLA is a novel open-source, data-driven light-curve fitter using Gaussian Processes, capable of estimating supernova light curves without templates, validated on simulations and real data, and providing comparable results to existing methods.
Contribution
Introduces PISCOLA, a new Gaussian Process-based light-curve fitting method that does not rely on templates, validated on simulations and real supernova data, and offers a new parametrization and colour law.
Findings
Successfully retrieves rest-frame peak magnitudes for surveys with up to 7-day cadence.
Shows small but significant differences compared to SALT2 on real data.
Derives a SN Ia colour law consistent with SALT2 and reddening laws.
Abstract
Forthcoming time-domain surveys, such as the Rubin Observatory Legacy Survey of Space and Time, will vastly increase samples of supernovae (SNe) and other optical transients, requiring new data-driven techniques to analyse their photometric light curves. Here, we present the "Python for Intelligent Supernova-COsmology Light-curve Analysis" (PISCOLA), an open source data-driven light-curve fitter using Gaussian Processes that can estimate rest-frame light curves of transients without the need for an underlying light-curve template. We test PISCOLA on large-scale simulations of type Ia SNe (SNe Ia) to validate its performance, and show it successfully retrieves rest-frame peak magnitudes for average survey cadences of up to 7 days. We also compare to the existing SN Ia light-curve fitter SALT2 on real data, and find only small (but significant) disagreements for different light-curve…
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