arXiv Machine Learning July 7, 2026 By Subhajit Dandapat, Alvin J. K. Chua

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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