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
Organization: Polymathic AI

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.