Beyond Point Estimates: Benchmarking Uncertainty Quantification Methods on the AION-1 Astronomical Foundation Model
arXiv:2606.07771v1 Announce Type: cross Abstract: Foundation models for astronomical surveys offer powerful learned representations that can be transferred to downstream regression tasks such as galaxy property estimation. However, point predictions alone are insufficient for scientific inference; reliable uncertainty quantification (UQ) is essential. We compare seven UQ methods on galaxy property regression using frozen AION-1 foundation-model embeddings, predicting redshift, stellar mass, stellar-population age, gas-phase metallicity, and specific star-formation rate, from Legacy Survey phot
The proliferation of foundation models across scientific domains necessitates robust uncertainty quantification to ensure reliability and scientific rigor, driving current research in this area.
Reliable uncertainty quantification in foundation models is critical for scientific discovery and high-stakes applications, moving beyond mere point predictions to interpretable and trustworthy AI outputs.
The focus on benchmarking Uncertainty Quantification (UQ) methods signifies a maturing foundation model landscape where trustworthiness and interpretability are becoming as important as predictive power in scientific AI.
- · Astronomers and astrophysicists
- · AI researchers in explainable AI and UQ
- · Scientific instrument manufacturers
- · Data scientists working with large-scale scientific datasets
- · AI models that provide only point predictions
- · Scientific fields relying solely on black-box AI outputs
- · Methods that cannot quantify uncertainty effectively
- · Platforms lacking UQ integration
Increased adoption of UQ methods in scientific AI applications.
Development of new AI models specifically designed with inherent uncertainty quantification capabilities.
Enhanced scientific discovery and breakthroughs due to more reliable and interpretable AI-driven insights from astronomical and other scientific surveys.
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Read at arXiv cs.AI