Predicting the Age of Astronomical Transients from Real-Time
  Multivariate Time Series
Hali Huang Daniel Muthukrishna Prajna Nair Zimi Zhang Michael Fausnaugh Torsha Majumder Ryan J. Foley George R. Ricker
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.