AION-1: Omnimodal Foundation Model for Astronomical Sciences
Authors: name: Liam Parker; name: Francois Lanusse; name: Jeff Shen; name: Ollie Liu; name: Tom Hehir; name: Leopoldo Sarra; name: Lucas Meyer; name: Micah Bowles; name: Sebastian Wagner-Carena; name: Helen Qu; name: Siavash Golkar; name: Alberto Bietti; name: Hatim Bourfoune; name: Nathan Casserau; name: Pierre Cornette; name: Keiya Hirashima; name: Geraud Krawezik; name: Ruben Ohana; name: Nicholas Lourie; name: Michael McCabe; name: Rudy Morel; name: Payel Mukhopadhyay; name: Mariel Pettee; name: Bruno Regaldo-Saint Blancard; name: Kyunghyun Cho; name: Miles Cranmer; name: Shirley Ho
Published: 2025-10-20 18:00:00

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.