Orateur
Description
Inverse problems in astronomy are often computationally expensive, and Markov Chain Monte Carlo (MCMC) routines become impractical for massive, heterogeneous datasets or when only limited data are available. With the advent of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the astronomical community will receive millions of transient alerts daily, many of which evolve on very short timescales. Classifying and characterizing these events as early as possible is therefore crucial. In this work, we present a Physics-Informed Neural Network framework to model astronomical time-series. It combines physical assumptions from two different transient classes in order to guide the model development in the absence of abundant data. Using the Type II-P supernova SN 2022acko as a test case, we show that our method can correctly infer physical properties of the transient progenitor system, achieving reliable results within only a few days of observations. This approach offers a scalable path toward extracting physical parameters from the large volume of early-time data expected in the LSST era, enabling automatic physics-based characterization of transients.
