Project 2

Project 2: A Foundation AI Model to Infer the Physics of Transients

Lead PIs: Gautham Narayan (UIUC; gsn at illinois.edu), Noelle Samia (NU)
Co-PIs: Daniel Apley (NU), Matthew Krafczyk (UIUC), Adam Miller (NU)
Collaborators: Jesus Carabello Anaya [Massachusetts Institute of Technology (MIT)], Arjun Chainani (UIUC), Alex Gagliano (MIT), Lanqing Jia (NU), Jennifer Li (UIUC), Michael Lukaszyk (UIUC), Daniel Muthukrishna (MIT), Jack O’Brien (UIUC), Este Padilla Gonzalez (Space Telescope Science Institute), Haille Perkins (UIUC), Nabeel Rehmatulla (NU), Ved Shah (NU), Amanda Wasserman (UIUC), Jiezhong Wu (NU)
Project Summary: This project aims to build a foundational AI model that would form a core part of a community pipeline for Rubin to identify (including anomalies) and characterize transients and set priorities for follow-up observations. The foundation model will be based on a new custom variational autoencoder (VAE) architecture, optimized for the sparse, multimodal and irregular data, and both observational data and emulated data from (physical) transient models. In year 2, the VAE will be replaced by a transformer.