  Much of the progress made in time-domain astronomy is accomplished by
relating observational multi-wavelength time series data to models derived from
our understanding of physical laws. This goal is typically accomplished by
dividing the task in two: collecting data (observing), and constructing models
to represent that data (theorizing). Owing to the natural tendency for
specialization, a disconnect can develop between the best available theories
and the best available data, potentially delaying advances in our understanding
new classes of transients. We introduce MOSFiT: the Modular Open-Source Fitter
for Transients, a Python-based package that downloads transient datasets from
open online catalogs (e.g., the Open Supernova Catalog), generates Monte Carlo
ensembles of semi-analytical light curve fits to those datasets and their
associated Bayesian parameter posteriors, and optionally delivers the fitting
results back to those same catalogs to make them available to the rest of the
community. MOSFiT is designed to help bridge the gap between observations and
theory in time-domain astronomy; in addition to making the application of
existing models and creation of new models as simple as possible, MOSFiT yields
statistically robust predictions for transient characteristics, with a standard
output format that includes all the setup information necessary to reproduce a
given result. As large-scale surveys such as LSST discover entirely new classes
of transients, tools such as MOSFiT will be critical for enabling rapid
comparison of models against data in statistically consistent, reproducible,
and scientifically beneficial ways.
