Project 5

Project 5: Automating Bayesian Inference of Millimeter Source Association

Lead PIs: William Moses (UIUC), Joaquin Vieira (UIUC)
Co-PIs: Tom Crawford (UC), Laura Trouille (Adler), Michael Zevin (Adler)
Collaborators: Melanie Archipley (UC), Jennifer Li (UIUC), Sydnee O’Donnell (UIUC), Kedar Phadke (UIUC; kphadke2 at illinois.edu), Siyuan Brant Qian (UIUC), Vimarsh Sathia (UIUC)
Project Summary: This project aims to develop an automated Bayesian inference tool for millimeter source association [cross-matching South Pole Telescope (SPT) sources with other surveys]. Correct associations to observations in different wavelengths are necessary to reconstruct spectral energy distributions (SEDs) and infer properties and redshifts of astrophysical objects. Bayesian inference is one of the methods to do this, but due to large numbers of objects that will be available from the current and future surveys, faster methods are necessary. This project aims to use machine learning (ML) to automate Bayesian inference, focusing on advancements in differentiable and probabilistic programming. Unlike existing differential programming problems, this project will incorporate the use of discrete variables, which are not handled well by existing automatic differentiation (AD) frameworks. The team plans to build a Zooniverse project to confirm source associations via visual inspection by volunteers.