Astrophysics

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39 papers6 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

Unsupervised Neural Operators for Rotating Relativistic Stars: High-Fidelity Equation of State Inference and Pulsar Spin Evolution at Scale
Nov 4, 2026Anarya RayAstrophysics Neural PDE Solvers
Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), Northwestern University, 1800 Sherman Ave, Evanston, IL 60201, USA · NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut Street, Chicago, IL 60611, USA
HLA-Net: A Physics-Guided Skeleton-Based Framework for Automatic Gravitational Arc Detection in Astronomical Images
Nov 3, 2026Don SAstrophysics
School of Computing, Amrita Vishwa Vidyapeetham, Kerala, India
Feature-Based and Deep Learning Methods for Astronomical Light Curve Classification
Nov 3, 2026V. Baymakanov, Kh. Khamitov, A. Raimova +7Time Series Classification Astrophysics
HSE University · Southern Federal University · Lomonosov Moscow State University +3
An automated photometric pipeline for measuring the corotation radius of spiral galaxies
Nov 3, 2026Kimia Yazdani, Sahel Mesforoush, Wayne B. HayesAstrophysics
Black Hole Love Numbers with Physics-Informed Neural Networks
Nov 1, 2026Maria J. Rodriguez, Luis Fernando Temoche, Pavlos ProtopapasAstrophysics Neural PDE Solvers
Department of Physics, Utah State University, 4415 Old Main Hill Road, UT 84322, USA · Instituto de Física Teórica UAM/CSIC, Universidad Autónoma de Madrid, 13-15 Calle Nicolás Cabrera, 28049 Madrid, Spain · John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
Contrastive Astromer: Two-Stage Contrastive Representation Learning for Astronomical Light Curve Classification
Oct 30, 2026Justin Minggan Wei, Pavlos Protopapas, Guillermo Cabrera-VivesContrastive Learning Self-Supervised Time Series Representation Learning
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, 02138, USA · Department of Computer Science, Universidad de Concepción, Edmundo Larenas 219, Concepción, Chile
OCCAMS: A Multi-Task Learning-Based Investigation of the CMB Power Spectra
Oct 29, 2026Ankit Mandal, Rahul Shah, Md. Wali Hossain +3Astrophysics Disentangled Representation Learning
Physics and Applied Mathematics Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata 700 108, India · Department of Physics, Jamia Millia Islamia, New Delhi 110025, India · Centre for Theoretical Physics, Jamia Millia Islamia, New Delhi 110025, India
UnEZ: A Fully Unsupervised Self-Templating Neural Network for Redshift and Line Strength Estimation
Oct 28, 2026Asha Abd, Karl Glazebrook, Colin JacobsAstrophysics Unsupervised Learning
Centre for Astrophysics and Supercomputing, Swinburne University of Technology, PO Box 218, Hawthorn, VIC 3122, Australia
Graph Neural Networks for Astrophysics and Cosmology
Oct 19, 2026Christian Kragh JespersenCosmology Graph Neural Networks
Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA
Analysis of Binary O-type Star Systems Using Recurrent Neural Networks
Oct 16, 2026Miguel Flores R., Luis J. Corral, Celia R. Fierro-Santillán +1Astrophysics
Centro Universitario de Ciencias Económico Administrativas (CUCEA), Universidad de Guadalajara, 45100 Zapopan, Jal., Mexico · Escuela Nacional Colegio de Ciencias y Humanidades, Plantel Sur (ENCCH Sur), Universidad Nacional Autónoma de México (UNAM), 04500, Mexico City, Mexico
Gestalt: a meta-foundation model for astronomy
Sep 29, 2026Michael J. Smith, Shashwat SouravEnsemble Learning AI-Assisted Scientific Research
AstroAI · Center for Astrophysics | Harvard & Smithsonian · UniverseTBD +1
Inductive Biases in Field-Level Cosmological Inference from Galaxy Catalogs
Sep 8, 2026James O. Baldwin, Shy Genel, Francisco Villaescusa-NavarroSimulation-Based Inference Graph Neural Networks
City University of New York Graduate Center, 365 5th Ave, New York, NY 10016 · Center for Computational Astrophysics, Flatiron Institute, 162 5th Avenue, New York, NY 10010, USA · Columbia Astrophysics Laboratory, Columbia University, 550 West 120th Street, New York, NY 10027, USA +1
AstroSpecLM: A Spectrum-Language Model for Evidence-Grounded Astronomical Spectral Analysis
Sep 7, 2026Jinghang Shi, Yanxia Zhang, Ali Luo +2Language Modeling Scientific QA
EncoTESS: Age-Sensitive Encodings from Raw TESS Light Curves
Aug 25, 2026Phil R. Van-Lane, Joshua S. Speagle, Ryan Cloutier +3Astrophysics Time Series Representation Learning
David A. Dunlap Department of Astronomy and Astrophysics, University of Toronto · Dunlap Institute for Astronomy and Astrophysics, University of Toronto · Department of Astronomy and Astrophysics, University of California San Diego +11
AI's Capability in Assisting Scientific Research in Physics, Astrophysics, and Cosmology I: Literature Review
Jul 28, 2026Anamaria Hell, Kateryna Vovk, Veena Krishnaraj +18Cosmology Astrophysics
IPMU
Co-addition and Subtraction of Undersampled Images
Jul 20, 2026Matan Schlanger, Barak ZackayImage Reconstruction Astrophysics
Department of Particle Physics and Astrophysics Weizmann Institute of Science 76100 Rehovot, Israel
ReLaTS: a Reinforcement Learning-based method for dynamically determining the coupling Time Step in multi-scale simulations of self-gravitating systems
Jun 18, 2026Veronica Saz Ulibarrena, Simon Portegies ZwartReinforcement Learning Scientific ML
Leiden Observatory, Leiden University, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
Classification of Astronomical Spectra Using PCA-Compressed Flux and Inverse-Variance Features
Jun 11, 2026Bruno Santos Meneses Barreto, Marcio EisencraftAstrophysics
Departamento de Engenharia de Telecomunicações e Controle, Universidade de São Paulo, São Paulo-SP
Beyond Point Estimates: Benchmarking Uncertainty Quantification Methods on the AION-1 Astronomical Foundation Model
Jun 5, 2026Karla Tame-Narvaez, Aleksandra Ćiprijanović, Shubhendu TrivediUncertainty Quantification Astrophysics
Scientific Computing Division Fermi National Accelerator Laboratory Batavia, IL 60510 · Department of Astronomy and Astrophysics University of Chicago Chicago, IL 60637 · Fermi National Accelerator Laboratory Batavia, IL 60510 +1
Generative Diffusion Priors for 3D Mapping of the Dark Universe
May 30, 2026Brandon Zhao, Diana Scognamiglio, Olivier Doré +13D Reconstruction Bayesian Inverse Problems
Department of Computing and Mathematical Sciences, California Institute of Technology · Jet Propulsion Laboratory, California Institute of Technology · Department of Physics, Duke University +1
Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction
May 20, 2026Tilman Tröster, David Mirkovic, Veronika Oehl +1Equivariant Neural Networks Equivariant GNNs
Department of Physics ETH Zürich
Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid
May 18, 2026Aritra Ghosh, Drew Oldag, Michael Tauraso +29Scientific ML Astrophysics
Dept. of Astronomy & the DiRAC Institute, University of Washington, Box 351580, Seattle, WA 98195, USA · eScience Institute, University of Washington, Seattle, WA, U.S.A. · School of Physics and Astronomy, University of Minnesota, Minneapolis, MN 55455, USA +6
FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution
May 5, 2026Shuhong Liu, Xining Ge, Quanfeng Xu +9Flow Matching Image Super-Resolution
The University of Tokyo · I2WM · Tohoku University +1
Stable but Wrong: An Inference Limit in Galactic Archaeology
Apr 30, 2026Zhipeng ZhangUncertainty Calibration Astrophysics
China Mobile Research Institute, Beijing 100053, China · China Mobile GBA (Greater Bay Area) Innovation Institute, Guangzhou 510656, China
Plausible but Wrong: A case study on Agentic Failures in Astrophysical Workflows
Apr 28, 2026Shivam Rawat, Lucie FlekAgent Failure Analysis AI for Science
Bonn-Aachen International Center for Information Technology, University of Bonn, Germany; Lamarr Institute for Machine Learning and Artificial Intelligence, Germany.
spectroxide: A code package for computing cosmic microwave background spectral distortions
Apr 27, 2026Ethan Baker, Hongwan Liu, Siddharth Mishra-SharmaAI-Assisted Scientific Research Scientific Code Generation
Physics Department, Boston University, Boston, MA 02215, USA · Faculty of Computing & Data Sciences, Boston University, Boston, MA 02215, USA
AstroSURE: Learning to Remove Noise from Astronomical Images Without Ground Truth Data
Apr 18, 2026Omid Vaheb, Sebastien Fabbro, Stark DraperSelf-Supervised Image Denoising Noise Robustness
Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, ON M5S 3G4, Canada · National Research Council Herzberg Astronomy an Astrophysics Research Centre, 5071 West Saanich Road, Victoria, BC V9E 2E7, Canada · Department of Computer Science, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics
Apr 10, 2026Pablo Mercader-Perez, Carolina Cuesta-Lazaro, Daniel Muthukrishna +5Astrophysics Multimodal Disentangled Representation Learning
Massachusetts Institute of Technology · Flatiron Institute, Simons Foundation · Institute for Advanced Studies +5
Learning the Stellar Structure Equations via Self-supervised Physics-Informed Neural Networks
Apr 6, 2026Manuel Ballester, Santiago Lopez-Tapia, Seth Gossage +9Astrophysics Neural PDE Solvers
SkAI Institute (NSF–Simons AI Institute for the Sky), Chicago, IL, USA · Department of Electrical and Computer Engineering, Northwestern University, Chicago, IL, USA · CIERA, Northwestern University, Chicago, IL, USA +4
AstroConcepts: A Large-Scale Multi-Label Classification Corpus for Astrophysics
Apr 2, 2026Atilla Kaan Alkan, Felix Grezes, Sergi Blanco-Cuaresma +5Astrophysics Multi-Label Classification
Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, USA · Faculty of Psychology, UniDistance Suisse, Brig, Switzerland

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Unsupervised Neural Operators for Rotating Relativistic Stars: High-Fidelity Equation of State Inference and Pulsar Spin Evolution at Scale

Anarya Ray

Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA), Northwestern University, 1800 Sherman Ave, Evanston, IL 60201, USA · NSF-Simons AI Institute for the Sky (SkAI), 172 E. Chestnut Street, Chicago, IL 60611, USA

Astrophysics Neural PDE Solvers Physics-Informed Neural Operators

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Observed populations of rotating neutron stars~(NSs) with millisecond-order periods are powerful probes of fundamental physics. Mass-radius measurements inform poorly constrained properties of degenerate nuclear matter, whereas high-fidelity population synthesis connects unknowns in NS formation and binary evolution to large radio-timing surveys. However, repeated structure calculations are prohibitive due to the computational cost of solving the highly coupled partial differential equations of axisymmetric general relativistic hydrostatic equilibrium that govern rotating NS interiors. Non-rotating calculations or approximate rotational corrections can bias equation of state inference and limit the fidelity of population synthesis in tracing the evolutionary history of progenitors and propagating equation of state uncertainties. In this Letter, we develop physics-informed neural operators that can solve the axisymmetric problem with high accuracy and negligible computational cost. \textit{Our solvers are trained in a fully unsupervised setting, with the training objective informed solely by the partial differential equations, and achieve a

10^3\times

speed-up with negligible accuracy loss}. These solvers enable scalable EoS inference that self-consistently accounts for rotation and repeated stellar-structure calculations during binary evolution, making spin predictions that account for rotational structure and EoS uncertainties computationally accessible to population synthesis.