Learning how Stars Form: Harnessing AI to Identify Structures in Noisy Spectral Cubes.
Dr. Stella Offner and Dr. Josh Taylor

Abstract:
Star formation is messy! The process spans many orders of magnitude in scale and involves a variety of physical processes: gravity, magnetic fields, radiation, and turbulence. Young stars announce their presence by emitting radiation and ejecting high-velocity material, “stellar feedback,” which in turn shapes the surrounding natal environment.
Part 1: Stella will present the results from a 3-D convolutional neural network model, trained on full-physics numerical simulations, which can accurately identify stellar feedback features in molecular line spectral cubes. She will discuss the pros and cons of supervised approaches based on numerical simulations for data segmentation.
Part 2: Josh will present a new approach to identifying cohesive spectral structures in molecular line cubes. Multiview Prototype Embedding and Clustering (MPEC) is an integrated approach to simultaneously:

• Learn prototypes of spectral data for efficient sample size reduction,

• Embed these prototypes in a lower dimensional latent space generated by a Self-Organizing variant of UMAP, and

• Cluster a graph representation of (learned) prototype similarity in both feature and latent spaces.

All facets of MPEC are self-parameterized, making it feasible for pipeline processing of spectral cubes. Josh will also discuss noisy cluster identification for spectral data using standard tools from statistical time series analysis.

Listen to Part 1 of the seminar here