The paper presents a fully automated method for reconstructing the surface mass density of galaxy clusters using photometry and gravitational lensing data. It introduces DarkClusters-15k, a benchmark dataset of 15,000 simulated clusters with paired mass and photometry maps across multiple redshifts and simulation frameworks. By training a diffusion prior on this dataset, the authors generate posterior samples constrained by weak- and strong-lensing observables, achieving accurate, physics‑guided reconstructions with well‑calibrated uncertainties in minutes.
By Diego Royo, Brandon Zhao, Adolfo Mu\~noz, Diego Gutierrez, Katherine L. Bouman
arXiv:2512. 11982v2 Announce Type: replace-cross Abstract: Finding scientifically interesting phenomena through slow manual labeling campaigns severely limits our ability to explore the billions of galaxy images produced by telescopes.
By Nolan Koblischke, Liam Parker, Francois Lanusse, Jo Bovy, Irina Espejo, Shirley Ho
arXiv:2510. 06200v4 Announce Type: replace-cross Abstract: Current time series foundation model (TSFM) training corpora largely omit data with certain complexities like irregular temporal sampling.
By Weijian Li, Hong-Yu Chen, Nabeel Rehemtulla, Ved G. Shah, Dongho Kim, Dennis Wu, Qinjie Lin, Adam A. Miller, Han Liu
We compare feature-based machine learning and end-to-end deep learning for variable star classification using the Zwicky Transient Facility (ZTF) StarEmbed dataset, which contains about 40,000 objects...
arXiv:2611. 06484v1 Announce Type: cross Abstract: Reported performance in small astronomical image datasets can reflect object reuse, field-of-view differences, acquisition settings, and catalogue-derived properties as well as astronomical morphology.
By Eraldo Pereira Marinho
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...
By Michael J. Smith, Shashwat Sourav
arXiv:2606. 10197v1 Announce Type: cross Abstract: Integral field unit (IFU) spectroscopy provides spatially resolved spectra across galaxies, offering crucial insights into their evolution.
By Zehao Peng, Biprateep Dey, Chris J. Maddison, Joshua S. Speagle
The paper presents a generative AI approach that enhances galaxy images from ground‑based sky surveys to match the detail level of space‑based telescopes. By training on space‑based data, the model converts weak signals into clear, detailed galaxy images, enabling the combination of high survey throughput with superior image quality. The authors provide source code, training data, a catalog of 63,202 enhanced galaxy images, and a software tool that encapsulates the entire pipeline.
By Sai Teja Erukude, Lior Shamir
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.
By Hubert Leterme, Andreas Tersenov, Jalal Fadili, Jean-Luc Starck
arXiv:2608. 08398v1 Announce Type: new Abstract: Astronomers classify galaxy morphology to investigate cosmic evolution.
By Kai Cheng, Ruoqi Wang, Qiong Luo
Graph Neural Networks (GNNs) are presented as a natural framework for modeling the relational structure inherent in astrophysical and cosmological data, such as galaxies in their environments or photons in detectors. The review argues that GNNs align with the native geometry of these domains, incorporate existing symmetries and prior knowledge, and remain flexible enough to capture unknown phenomena, offering greater interpretability than other deep‑learning models. It surveys applications across cosmology, galaxy formation, dynamics, and instrumentation, noting that only about one in six non‑instrumentation papers apply GNNs to real data rather than simulations, and provides best‑practice guidelines for when to use or avoid graph approaches.
By Christian Kragh Jespersen
arXiv:2606. 29952v1 Announce Type: cross Abstract: Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment.
By Seonghwan Park, Hyunji Jung, Dongyeop Lee, Namhoon Lee