A Data-Driven Prism: Multi-View Source Separation with Diffusion Model Priors

Sebastian Wagner-Carena, Aizhan Akhmetzhanova, Sydney Erickson

Keywords: Diffusion, Source Separation, Score-Based, Linear Mixing Models, Expectation-Maximization, Unsupervised Methods

TL;DR: Diffusion models disentangle latent sources from multiple, noisy, incomplete observations without source-specific assumptions, yielding generative priors and posterior inference across synthetic tasks and real galaxy data.

Abstract: In the natural sciences, a common challenge is to disentangle distinct, unknown sources from observations. Examples of this source separation task include deblending galaxies in a crowded field, distinguishing the activity of individual neurons from overlapping signals, and separating seismic events from the ambient background. Traditional analyses often rely on simplified source models that fail to accurately reproduce the data. Recent advances have shown that diffusion models can directly learn complex prior distributions from noisy, incomplete data. In this work, we show that diffusion models can solve the source separation problem without explicit assumptions about the source. Our method relies only on multiple views, or the property that different sets of observations contain different linear transformations of the unknown sources. We show that our method succeeds even when no source is individually observed and the observations are noisy, incomplete, and vary in resolution. The learned diffusion models enable us to sample from the source priors, evaluate the probability of candidate sources, and draw from the joint posterior of our sources given an observation. We demonstrate the effectiveness of our method on a range of synthetic problems as well as real-world galaxy observations.

Primary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)

Submission Number: 18319