2026 workshop program
Extracting Spectroscopic Information from Imaging and Photometry using Probabilistic Machine Learning
The work
Abstract
The growing number of galaxies discovered in modern surveys has produced an unprecedented volume of imaging and photometric data. Extracting their physical properties usually relies on spectroscopy, which is computationally expensive and limits applications to very large datasets.
In this work, we explore how much spectroscopic information is already contained in imaging and photometry, and how we can extract it using probabilistic machine learning models. We use a Variational Autoencoder (VAE) combined with Normalizing Flows to predict a range of galaxy properties at z≲0.3 from SDSS ugriz imaging and photometry.
Our model predicts stellar mass, star formation rate (SFR), specific SFR, dust parameters, age, star formation timescale (τ), redshift, and velocity dispersion. We also predict key emission line fluxes (Hα, Hβ, [N II], and [O III]), which we use to construct BPT diagrams and classify galaxies into star-forming and AGN populations without spectroscopy.
We further test the effect of adding infrared photometry (WISE bands), which shows promising improvements for certain subclasses of galaxies, especially in constraining the age–dust degeneracy. We also examine the learned latent space to see if it captures meaningful structure that could provide insights into galaxy evolutionary pathways.
Our results show that imaging and photometry already contain a significant amount of information about galaxy properties, and that probabilistic ML models can be used to extract it in a scalable way for upcoming surveys like Roman and Rubin LSST.