Abstract

Fulfilling the rich promise of rapid advances in time-domain astronomy is only possible through confronting our observations with physical models and extracting the parameters that best describe what we see at an individual object level and hierarchically, at a population level. In this talk, I will describe Redback, a Bayesian inference software package for electromagnetic transients. Redback provides an object-orientated Python interface to over 12 different samplers and over 100 different models for kilonovae, supernovae, gamma-ray burst afterglows, tidal disruption events, engine-driven transients, among other explosive transients enabling inference at scale on single objects and populations. Redback also serves as an engine to simulate observations from real surveys such as LSST and ZTF and hypothetical user-constructed surveys to enable survey optimisation and design. I will showcase some features and typical use cases of the Redback software and highlight results already enabled by this package for TDEs, supernovae, and GRBs.