AION-1: Omnimodal Foundation Model for Astronomical Sciences
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.
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Liam Parker
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Francois Lanusse
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2025-10-23T15:00:27.479Z
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Francois Lanusse
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EiffL
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Jeff Shen
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Ollie Liu
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2025-10-22T15:44:37.632Z
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Ollie Liu
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oliu-io
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Tom Hehir
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Leopoldo Sarra
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Lucas Meyer
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Micah Bowles
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Sebastian Wagner-Carena
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Helen Qu
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Siavash Golkar
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Alberto Bietti
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Hatim Bourfoune
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Nathan Casserau
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Pierre Cornette
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Keiya Hirashima
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Geraud Krawezik
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Ruben Ohana
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Nicholas Lourie
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Michael McCabe
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Rudy Morel
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Payel Mukhopadhyay
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Mariel Pettee
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Bruno Regaldo-Saint Blancard
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Kyunghyun Cho
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Miles Cranmer
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Shirley Ho
AION-1: Omnimodal Foundation Model for Astronomical Sciences
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.
AION-1, a family of large-scale multimodal foundation models, integrates diverse astronomical data using tokenization and transformer-based modeling, achieving strong performance across various downstream tasks.
multimodal foundation models tokenization transformer-based masked modeling cross-modal token sequences pretrained galaxy and stellar property estimation galaxy morphology classification similarity-based retrieval galaxy image segmentation spectral super-resolution