GOPREAUX I: Open-source Code and Data to Model Multi-wavelength Emission of Extragalactic Transients using Gaussian Processes

TL;DR

GOPREAUX is an open-source Python package that uses Gaussian Process Regression to model and interpolate multi-wavelength light curves of extragalactic transients, aiding classification and physical analysis.

Contribution

It introduces a non-parametric, data-driven modeling approach for transient emission across phase and wavelength, enabling predictions at higher redshifts.

Findings

Aggregated a sample of 1,300 transients with over 146,000 observations.

Provided multi-wavelength light curves and spectral templates from the models.

Made the code and data publicly available as open-source resources.

Abstract

Contemporary all-sky surveys have observed thousands of extragalactic transients in the nearby universe, and upcoming surveys will discover exponentially more at higher redshifts. With these large samples, population-level analysis of the photometric behavior of different transient classes is now possible, allowing for photometric classification and physical parameter inference from relatively sparse individual light curves. To enable such studies, we introduce Gaussian process Optimized Photometric Regression of Extragalactic Archival Ultraviolet-infrared eXplosions, a.k.a GOPREAUX--a Python package for Gaussian Process Regression of multi-wavelength transient photometry. Our modeling is unique in that it interpolates transient emission across phase and wavelength in a non-parametric, data-driven way. This allows for predictions of light curves and spectra at higher redshifts, where the rest-frame ultraviolet (UV) emission is redshifted into the observer-frame optical or infrared. To this end, we aggregate a sample of almost 1,300 transients observed in the UV and optical with the Neil Gehrels Swift Telescope, complemented with additional optical and infrared coverage from surveys such as ZTF and open-source data releases. Our sample includes 275 Type II SNe, 172 stripped-envelope SNe, 72 superluminous SNe, and 58 tidal disruption events, among other classes. Our code and reduced photometry--comprising over 146,000 photometric observations--are available as open-source software and data products. Here we discuss our sample criteria, data reduction and modeling methodologies, the multi-wavelength light curves and spectral templates produced by our models, and the future directions in photometric classification and physical parameter inference this code and data repository enables.