MOSFiT: Modular Open Source Fitter for Transients

Research output: Contribution to journalArticle


  • James Guillochon
  • V. Ashley Villar
  • Brenna Mockler
  • Gautham Narayan
  • Kaisey S. Mandel
  • Edo Berger
  • Peter K. G. Williams

Colleges, School and Institutes


Much of the progress made in time-domain astronomy is accomplished by relating observational multiwavelength 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 data sets from open online catalogs (e.g., the Open Supernova Catalog), generates Monte Carlo ensembles of semi-analytical light-curve fits to those data sets 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 that conducted with the Large Synoptic Survey Telescope (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....


Original languageEnglish
Article number6
JournalAstrophysical Journal. Supplement Series
Issue number1
Publication statusPublished - 11 May 2018


  • catalogs, methods: data analysis, methods: numerical, methods: statistical, supernovae: general