Much of the progress made in time-domain astronomy is accomplished byrelating observational multi-wavelength time series data to models derived fromour understanding of physical laws. This goal is typically accomplished bydividing the task in two: collecting data (observing), and constructing modelsto represent that data (theorizing). Owing to the natural tendency forspecialization, a disconnect can develop between the best available theoriesand the best available data, potentially delaying advances in our understandingnew classes of transients. We introduce MOSFiT: the Modular Open-Source Fitterfor Transients, a Python-based package that downloads transient datasets fromopen online catalogs (e.g., the Open Supernova Catalog), generates Monte Carloensembles of semi-analytical light curve fits to those datasets and theirassociated Bayesian parameter posteriors, and optionally delivers the fittingresults back to those same catalogs to make them available to the rest of thecommunity. MOSFiT is designed to help bridge the gap between observations andtheory in time-domain astronomy; in addition to making the application ofexisting models and creation of new models as simple as possible, MOSFiT yieldsstatistically robust predictions for transient characteristics, with a standardoutput format that includes all the setup information necessary to reproduce agiven result. As large-scale surveys such as LSST discover entirely new classesof transients, tools such as MOSFiT will be critical for enabling rapidcomparison of models against data in statistically consistent, reproducible,and scientifically beneficial ways.
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