arXiv AI June 9, 2026 By Karla Tame-Narvaez, Aleksandra \'Ciprijanovi\'c, Shubhendu Trivedi

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

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Estimating Uncertainty in Galaxy Morphology Classification

arXiv:2608. 08398v1 Announce Type: new Abstract: Astronomers classify galaxy morphology to investigate cosmic evolution.

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arXiv Machine Learning

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A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping

arXiv:2603. 22006v2 Announce Type: replace-cross Abstract: Upcoming stage-IV surveys such as Euclid and Rubin will deliver vast amounts of high-precision data, opening new opportunities to constrain cosmological models with unprecedented accuracy.

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arXiv Machine Learning

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21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables

arXiv:2606. 00219v1 Announce Type: cross Abstract: We are witnessing a surge in observations of the cosmic dawn (CD) and epoch of reionisation (EoR), driving an increasing demand for fast and robust theoretical interpretation frameworks.

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arXiv Machine Learning

Aug 6

Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models

arXiv:2512. 10222v2 Announce Type: replace-cross Abstract: Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions.

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arXiv Machine Learning

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Operational Range Bounding in Spectroscopy: A Safety Cage Framework for Machine Learning Models

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arXiv Machine Learning

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PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework

arXiv:2505. 08784v2 Announce Type: replace-cross Abstract: As machine learning (ML) enters high-stakes domains, trustworthy uncertainty quantification (UQ) is essential for safety.

By Abhineet Agarwal, Fange Xiao, Rebecca Barter, Omer Ronen, Boyu Fan, Bin Yu

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Related stories

Estimating Uncertainty in Galaxy Morphology Classification

A plug-and-play approach with fast uncertainty quantification for weak lensing mass mapping

21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables

Galaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models

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PCS-UQ: Uncertainty Quantification via the Predictability-Computability-Stability Framework