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.

SM-Net: Learning a Continuous Spectral Manifold from Multiple Stellar Libraries
Mar 25, 2026Omar Anwar, Aaron S. G. Robotham, Luca Cortese +1Astrophysics Neural Surrogate Modeling
International Centre for Radio Astronomy Research (ICRAR) The University of Western Australia (UWA), 35 Stirling Hwy, Crawley WA 6009, Australia
BHCast: Unlocking Black Hole Plasma Dynamics from a Single Blurry Image with Long-Term Forecasting
Mar 24, 2026Renbo Tu, Ali SaraerToosi, Nicholas S. Conroy +2Astrophysics Spatiotemporal Forecasting
University of Toronto · Vector Institute · University of Illinois at Urbana-Champaign +1
Spectral Hierarchy of the Cosmic Web
Mar 16, 2026Francisco-Shu Kitaura, Francesco SinigagliaHierarchical Classification Spectral Methods
Instituto de Astrofísica de Canarias, s/n, E-38205, La Laguna, Tenerife, Spain · Departamento de Astrofísica, Universidad de La Laguna, E-38206, La Laguna, Tenerife, Spain · Institute for Fundamental Physics of the Universe, Via Beirut 2, I-34151 Trieste, Italy +3
Mapping Dark-Matter Clusters via Physics-Guided Diffusion Models
Mar 15, 2026Diego Royo, Brandon Zhao, Adolfo Muñoz +2Astrophysics Diffusion-Based Inverse Problems
Universidad de Zaragoza–I3A, Spain · California Institute of Technology, USA
AS-Bridge: A Bidirectional Generative Framework Bridging Next-Generation Astronomical Surveys
Mar 12, 2026Dichang Zhang, Yixuan Shao, Simon Birrer +1Conditional Generative Modeling Image-to-Image Translation
Stony Brook University, Stony Brook, NY, USA
SELDON: Supernova Explosions Learned by Deep ODE Networks
Mar 4, 2026Jiezhong Wu, Jack O'Brien, Jennifer Li +6Astrophysics Irregular Time-Series Modeling
NSF-Simons AI Institute for the Sky (SkAI), Chicago, IL, USA · Department of Industrial Engineering and Management Sciences, Northwestern University · Department of Astronomy, University of Illinois Urbana-Champaign +4
Deeper detection limits in astronomical imaging using self-supervised spatiotemporal denoising
Feb 19, 2026Yuduo Guo, Hao Zhang, Mingyu Li +10Image Denoising Self-Supervised Image Denoising
Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, China · Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China · Department of Astronomy, Tsinghua University, Beijing, China +6
Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging
Jan 30, 2026Shuhong Liu, Xining Ge, Ziying Gu +7Synthetic Data Augmentation Image Denoising
The University of Tokyo · I2WM · Shanghai Astronomical Observatory +2
A Deep Learning Framework for TTV Characterization: A Case Study of the Kepler-88 System
Sep 6, 2024Chen Chen, Jiapeng Gao, Gongjie Li +2Parameter Estimation Astrophysics
School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, 30332, USA · Center for Relativistic Astrophysics, School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA · School of Mathematics, Georgia Institute of Technology, Atlanta, GA, 30332, USA +1

Mar 25, 2026J/K move · Enter open · S save

SM-Net: Learning a Continuous Spectral Manifold from Multiple Stellar Libraries

Omar Anwar, Aaron S. G. Robotham, Luca Cortese, Kevin Vinsen

International Centre for Radio Astronomy Research (ICRAR) The University of Western Australia (UWA), 35 Stirling Hwy, Crawley WA 6009, Australia

Astrophysics Neural Surrogate Modeling Conditional Generative Modeling

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We present SM-Net, a machine-learning model that learns a continuous spectral manifold from multiple high-resolution stellar libraries. SM-Net generates stellar spectra directly from the fundamental stellar parameters effective temperature (Teff), surface gravity (log g), and metallicity (log Z). It is trained on a combined grid derived from the PHOENIX-Husser, C3K-Conroy, OB-PoWR, and TMAP-Werner libraries. By combining their parameter spaces, we construct a composite dataset that spans a broader and more continuous region of stellar parameter space than any individual library. The unified grid covers Teff = 2,000-190,000 K, log g = -1 to 9, and log Z = -4 to 1, with spectra spanning 3,000-100,000 Angstrom. Within this domain, SM-Net provides smooth interpolation across heterogeneous library boundaries. Outside the sampled region, it can produce numerically smooth exploratory predictions, although these extrapolations are not directly validated against reference models. Zero or masked flux values are treated as unknowns rather than physical zeros, allowing the network to infer missing regions using correlations learned from neighbouring grid points. Across 3,538 training and 11,530 test spectra, SM-Net achieves mean squared errors of 1.47 x 10^-5 on the training set and 2.34 x 10^-5 on the test set in the transformed log1p-scaled flux representation. Inference throughput exceeds 14,000 spectra per second on a single GPU. We also release the model together with an interactive web dashboard for real-time spectral generation and visualisation. SM-Net provides a fast, robust, and flexible data-driven complement to traditional stellar population synthesis libraries.