Multi-passband lightcurve estimation and machine learning classification for big data surveys

by Dr Etienne Sédick Russeil

Friday 24 Jan 2025, 10:30 → 11:30 Europe/Stockholm

FC61 (AlbaNova Main Building)

Rainbow is a physically motivated parametric framework which enables simultaneous multi-band light curve fitting. Assuming the electromagnetic radiation emission from the transient can be approximated by a black-body, it allows the user to construct a 2-dimensional continuous surface across wavelength and time, even in situations where the number of observations in each filter is significantly limited.

The Vera-C.-Rubin Observatory Large Survey of Space and Time (LSST) will detect 10 million transient events across 6 passbands each night. In this context, Rainbow constitutes a crucial tool to systematically and quickly extract meaningful information from alerts. Its ability to exploit measurements from every wavelength allows it to manipulate sparse data and produce earlier fit than if filters were considered independently. We observe that machine learning classification pipelines benefit from the usage of features extracted with the Rainbow framework. This talk will provide an overview of the method, along with several applications for classifying transients within large astronomical datasets, including ZTF data and LSST simulations.