arXiv:2608. 08398v1 Announce Type: new Abstract: Astronomers classify galaxy morphology to investigate cosmic evolution.
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.
arXiv:2609.38312v1 Announce Type: cross Abstract: The Platonic Representation Hypothesis predicts that sufficiently scaled foundation models converge on a shared representation of the world. As each...
The paper presents a probabilistic machine‑learning framework for classifying low‑ and high‑resolution stellar and extragalactic spectra, targeting the upcoming 4MOST survey. Four approaches were evaluated—CNNs, Dirichlet distribution, Monte Carlo dropout (MCD), and Bayesian neural networks with variational inference—using SDSS data and a 4MOST mock dataset. The MCD‑augmented CNN achieved the highest accuracies (92.6% on SDSS, 93.9% on mock data) while also delivering well‑calibrated uncertainty estimates with minimal extra computational cost.
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.
The paper introduces a method for estimating uncertainty in hierarchical taxonomic reasoning produced by black‑box large language models (LLMs). By extracting proxy features with an open‑source tool and training lightweight supervised estimators that incorporate hierarchy‑aware supervision, the authors predict rank‑wise correctness. Across three LLMs, these estimators outperform token‑likelihood baselines, raising micro AUROC from 0.57 to 0.75–0.80, with a rank‑specific multi‑head design delivering the best results.
The paper presents a spectral hierarchy for classifying the cosmic web by applying scale-weighting kernels to the density field before using eigenvalue-based methods. This framework unifies existing web definitions—potential/tidal, curvature-based, and higher-derivative levels—into a single, interpretable hierarchy that captures structure from large to small scales. The authors quantify the hierarchy’s information content by correlating a web contrast field with halo distributions, showing that it retains significant tracer-relevant information across scales, especially at nonlinear levels.
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:2606. 10197v1 Announce Type: cross Abstract: Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution.
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.
The paper investigates how pre‑training strategy, dataset size, and domain affect uncertainty estimation in vision medical foundation models. It compares point‑prediction calibration with conformal (region) prediction across retinal, histopathological, and chest X‑ray models, finding that domain‑specific, self‑supervised pre‑training yields better calibration and more efficient conformal sets. The study shows that standard recalibration alone cannot fully reconcile uncertainty differences between models trained on different data sources.
arXiv:2604. 08648v2 Announce Type: replace-cross Abstract: We motivate the use of differentiable probabilistic programming techniques in order to account for the large model-space inherent to astrophysical $\gamma$-ray analyses.
arXiv:2606. 17413v1 Announce Type: new Abstract: Space-based monitoring of atmospheric carbon dioxide (CO2) is essential for constraining the global carbon budget.