Over the past 30 years, numerous large-scale photometric astronomical surveys have been conducted, including SDSS, Pan-STARRS, Gaia,2MASS, WISE, and others. These surveys provide extensive photometric measurements that can be used to infer a wide range of physical parameters of astronomical objects. Traditionally, Bayesian approaches, such as Markov Chain Monte Carlo (MCMC) sampling have been employed for such inference tasks. However, these methods tend to be computationally intensive and often require manual tuning or expert supervision. In this work, we propose a novel machine learning model designed to perform automatic and robust inference from photometric data, offering a scalable and efficient alternative to conventional techniques.
@article{arxiv.2510.26964,
title = {IrisML: Neural Posterior Estimation for the Spectral Energy Distribution fitting},
author = {Mateusz Kapusta},
journal= {arXiv preprint arXiv:2510.26964},
year = {2025}
}
Comments
Accepted at the 2025 ICML Workshop on Machine Learning for Astrophysics. Github code: https://github.com/Wesenheit/Iris-ML