AION-1: Omnimodal Foundation Model for Astronomical Sciences

TL;DR

AION-1 is a large-scale multimodal foundation model for astronomy that integrates diverse data types across multiple surveys, enabling a wide range of scientific tasks with high accuracy and scalability.

Contribution

The paper introduces AION-1, a novel multimodal foundation model that unifies heterogeneous astronomical data using a two-stage transformer architecture, advancing cross-modal scientific modeling.

Findings

Achieves strong performance on various astronomical tasks.

Supports models ranging from 300M to 3.1B parameters.

Provides open-source code and pretrained weights.

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.