arXiv AI
Aug 11
Estimating Uncertainty in Galaxy Morphology Classification
arXiv:2608. 08398v1 Announce Type: new Abstract: Astronomers classify galaxy morphology to investigate cosmic evolution.
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.
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arXiv Machine Learning
Jul 16
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.
arXiv Machine Learning
Jun 2
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.
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.
arXiv Machine Learning
Jun 11
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.
arXiv Machine Learning
Jun 10
Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions
arXiv:2606. 10023v1 Announce Type: cross Abstract: Accurate posterior estimation is central to scientific inference, as uncertainties determine what can be reliably learned from observational data.
arXiv Machine Learning
Jun 25
Efficient Analytic Uncertainty Quantification for Multi-Modal Regression
arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.
arXiv Machine Learning
Jun 24
Machine Learning and Deep Learning for Exoplanet Detection and Atmospheric Characterization with JWST and the Upcoming Ariel Mission
arXiv:2606. 23766v1 Announce Type: cross Abstract: The detection and atmospheric characterization of exoplanets have entered a new data-intensive era driven by the James Webb Space Telescope and the upcoming Ariel mission.
arXiv Machine Learning
Jul 1
Accelerating Conformal Prediction via Approximate Leave-One-Out
arXiv:2606. 31915v1 Announce Type: cross Abstract: While conformal prediction provides a general framework for uncertainty quantification in predictive inference, its application is often limited by computational cost.
arXiv Machine Learning
Jul 7
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
arXiv:2507. 08150v4 Announce Type: replace-cross Abstract: Accurate uncertainty quantification is critical for reliable predictive modeling.
arXiv AI
Jun 10
Conformal Prediction for Neural Operators: Distribution-Free Uncertainty Quantification in Physics Simulation
arXiv:2606. 09923v1 Announce Type: cross Abstract: Neural operators such as the Fourier Neural Operator (FNO) have emerged as powerful surrogates for solving partial differential equations (PDEs), achieving speedups of several orders of magnitude over traditional numerical solvers.
arXiv Machine Learning
Jun 24
Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks
arXiv:2602. 10330v3 Announce Type: replace-cross Abstract: Context: The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision.