AION-1: Omnimodal Foundation Model for Astronomical Sciences
Authors
Liam Parker
Francois Lanusse
Jeff Shen
Ollie Liu
Tom Hehir
Leopoldo Sarra
Lucas Meyer
Micah Bowles
Sebastian Wagner-Carena
Helen Qu
Siavash Golkar
Alberto Bietti
Hatim Bourfoune
Nathan Casserau
Pierre Cornette
Keiya Hirashima
Geraud Krawezik
Ruben Ohana
Nicholas Lourie
Michael McCabe
Rudy Morel
Payel Mukhopadhyay
Mariel Pettee
Bruno Regaldo-Saint Blancard
Kyunghyun Cho
Miles Cranmer
Shirley Ho
Abstract
While foundation models have shown promise across a variety of fields, astronomy still lacks a unified framework for joint modeling across its highly diverse data modalities. In this paper, we present AION-1, a family of large-scale multimodal foundation models for astronomy. AION-1 integrates heterogeneous imaging, spectroscopic, and scalar data using a two-stage architecture: modality-specific tokenization followed by transformer-based masked modeling of cross-modal token sequences. The model is pretrained on five large-scale surveys: Legacy Survey, Hyper Suprime-Cam (HSC), Sloan Digital Sky Survey (SDSS), Dark Energy Spectroscopic Instrument (DESI), and Gaia. These span more than 200 million observations of stars, galaxies, and quasars. With a single frozen encoder, AION-1 achieves strong results on a broad suite of downstream tasks, including galaxy and stellar property estimation, galaxy morphology classification, similarity-based retrieval, galaxy image segmentation, and spectral super-resolution. We release AION-1 model variants ranging from 300 M to 3.1 B parameters. Beyond astronomy, AION-1 provides a scalable blueprint for multimodal scientific foundation models that can seamlessly integrate noisy, instrument-specific observations. All code, tokenizers, pretrained weights, and a lightweight evaluation suite are released under an open-source license.
