arXiv Machine Learning

Transformers with Physics-Informed Encodings and Simulation-Based Inference for Robust Detection of Eccentric Binary Black Holes in Pulsar Timing Array Data

arXiv:2607. 03904v1 Announce Type: new Abstract: Pulsar timing arrays (PTAs) provide a unique window into nanohertz gravitational waves (GWs), but extracting astrophysical parameters from noisy, long-baseline timing residuals remains computationally challenging with traditional Bayesian techniques due to the high dimensionality of the parameter space, complex and correlated noise models, and the cost of repeated likelihood evaluations.

arXiv Machine Learning

Jun 25

Flexible Gravitational-Wave Parameter Estimation with Transformers

arXiv:2512. 02968v2 Announce Type: replace-cross Abstract: Gravitational-wave data analysis relies on accurate and efficient methods to extract physical information from noisy detector signals, yet the increasing rate and complexity of observations represent a growing challenge.

By Annalena Kofler, Maximilian Dax, Stephen R. Green, Jonas Wildberger, Nihar Gupte, Jakob H. Macke, Jonathan Gair, Alessandra Buonanno, Bernhard Sch\"olkopf

llms

arXiv Machine Learning

Jun 15

Binary Black Hole Parameter Estimation with Hybrid CNN-Transformer Neural Networks

arXiv:2606. 13941v1 Announce Type: cross Abstract: The detection of gravitational waves has revolutionized our ability to explore fundamental aspects of the Universe.

By Panagiotis N. Sakellariou, Spiros V. Georgakopoulos, Sotiris Tasoulis, Vassilis P. Plagianakos

llms

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.

By Ludvig Doeser, Jens Jasche

arXiv Machine Learning

Jun 2

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP

arXiv:2605. 27527v2 Announce Type: replace-cross Abstract: Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves.

By Siddharth Chaini, Federica B. Bianco, Ashish Mahabal

benchmarks

arXiv AI

Jul 2

Calibrated Test-Time Guidance for Bayesian Inference

arXiv:2602. 22428v2 Announce Type: replace-cross Abstract: Test-time guidance is a widely used mechanism for steering pretrained diffusion models toward outcomes specified by a reward function.

By Daniel Geyfman, Felix Draxler, Jan Groeneveld, Hyunsoo Lee, Theofanis Karaletsos, Stephan Mandt

diffusion benchmarks

arXiv AI

Jun 17

ASTEROID: A Spatiotemporal Information Transformer for Forecasting Multi-Step Time Series of Molecular Dynamics

arXiv:2606. 17668v1 Announce Type: cross Abstract: Molecular dynamics (MD) simulation is computationally demanding, particularly for large-scale systems requiring long-term analysis.

By Kexin Wu, Luonan Chen, Renxiao Wang

llms benchmarks

arXiv Machine Learning

Jun 18

Modeling Doppler Shifts in Radial-Velocity Data with Deep Learning toward Earth-mass Exoplanet Detection

arXiv:2606. 18464v1 Announce Type: cross Abstract: Detecting the tiny Doppler shifts induced by Earth-mass planets in stellar radial-velocity measurements remains extremely challenging due to stellar activity.

By Isidro G\'omez-Vargas, Xavier Dumusque, Yinan Zhao, Khaled Al Moulla, Michael Cretignier

arXiv Machine Learning

Jun 16

GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation

arXiv:2606. 16035v1 Announce Type: cross Abstract: Modern particles physics experiments have demonstrated an increasing need for fast, high-fidelity detector simulation as detector components have improved and subsequent computational requirements approach the limits of available resources.

By Cole Granger, James Giroux, Richard Tyson, Maurizio Ungaro, Cristiano Fanelli

llms

arXiv Machine Learning

Jun 9

Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics

arXiv:2604. 09787v2 Announce Type: replace-cross Abstract: Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-dependent artifacts from the sensor or instrument.

By Pablo Mercader-Perez, Carolina Cuesta-Lazaro, Daniel Muthukrishna, Jeroen Audenaert, V. Ashley Villar, David W. Hogg, Marc Huertas-Company, William T. Freeman

multimodal

arXiv Machine Learning

Jul 21

Learning Spatio-Temporal Foundation Models from Pure Synthetic Data

arXiv:2607. 16251v1 Announce Type: new Abstract: Spatio-Temporal Foundation Models (STFMs) aim to learn generalizable representations of complex dynamical systems across space and time.

By Yutong Feng, Shiyuan Piao, Yutong Xia, Xu Liu, Wenqi Fan, Fugee Tsung, See-Kiong Ng, Yuxuan Liang

diffusion benchmarks safety

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.

By Hubert Leterme, Andreas Tersenov, Jalal Fadili, Jean-Luc Starck

benchmarks

arXiv Machine Learning

Aug 4

ReFP-AD: Rectified Flow Preconditioning for Energy-Based Anomaly Detection

arXiv:2608. 01793v1 Announce Type: new Abstract: Unified anomaly detection requires modeling highly heterogeneous normal data without access to anomalous samples.

By Camile Lendering, Erkut Akdag, Joaqu\'in Figueira, Egor Bondarev

rag diffusion benchmarks

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