Workshop: Machine Learning and the Physical Sciences

DIGS: Deep Inference of Galaxy Spectra with Neural Posterior Estimation

Gourav Khullar · Brian Nord · Aleksandra Ciprijanovic · Jason Poh · Fei Xu · Ashwin Samudre


With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of Simulation-Based inference (SBI) and amortized Neural Posterior Estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently.Here, we demonstrate a proof-of-concept study of spectra that is a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with MCMC methods. We show that SBI is capable of inferring very accurate galaxy stellar masses and metallicities. Our methodology also a) produces uncertainty constraints that are comparable to or moderately weaker than traditional inverse-modeling with Bayesian MCMC methods (e.g., 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively), and b) conducts rapid SED inference (~10^5 galaxy spectra via SBI/SNPE at the cost of 1 MCMC-based fit); this efficiency is needed in the era of JWST and Roman Telescope.

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