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MOSFiT (Modular Open-Source Fitter for Transients) 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 helps 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.
SuperRAENN performs photometric classification of supernovae in the following categories: Type I superluminos supernovae, Type II, Type IIn, Type Ia and Type Ib/c. Though the code is optimized for use with complete (rather than realtime) light curves from the Pan-STARRS Medium Deep Survey, the classifier can be trained on other data. SuperRAENN can be used on a dataset containing both spectroscopically labelled and unlabelled SNe; all events will be used to train the RAENN, while labelled events will be used to train the random forest.
FLEET (Finding Luminous and Exotic Extragalactic Transients) is a machine-learning pipeline that predicts the probability of a transient to be a superluminous supernova. With light curve and contextual host galaxy information, it uses a random forest algorithm to rapidly identify SLSN-I without the need for redshift information.