Predicting the Age of Astronomical Transients from Real-Time Multivariate Time Series
Abstract
Astronomical transients, such as supernovae and other rare stellar explosions, have been instrumental in some of the most significant discoveries in astronomy. New astronomical sky surveys will soon record unprecedented numbers of transients as sparsely and irregularly sampled multivariate time series. To improve our understanding of the physical mechanisms of transients and their progenitor systems, early-time measurements are necessary. Prioritizing the follow-up of transients based on their age along with their class is crucial for new surveys. To meet this demand, we present the first method of predicting the age of transients in real-time from multi-wavelength time-series observations. We build a Bayesian probabilistic recurrent neural network. Our method can accurately predict the age of a transient with robust uncertainties as soon as it is initially triggered by a survey telescope. This work will be essential for the advancement of our understanding of the numerous young transients being detected by ongoing and upcoming astronomical surveys.
Keywords
Cite
@article{arxiv.2311.17143,
title = {Predicting the Age of Astronomical Transients from Real-Time Multivariate Time Series},
author = {Hali Huang and Daniel Muthukrishna and Prajna Nair and Zimi Zhang and Michael Fausnaugh and Torsha Majumder and Ryan J. Foley and George R. Ricker},
journal= {arXiv preprint arXiv:2311.17143},
year = {2023}
}
Comments
6 pages, 4 figures. Accepted at the NeurIPS 2023 Machine Learning and the Physical Sciences workshop