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Argus: JAX state-space filtering for gravitational wave detection with a pulsar timing array

Year: 2025

Type: preprint

Abstract: Argus is a high-performance Python package for detecting and characterising nanohertz gravitational waves in pulsar timing array data. The package provides a complete Bayesian inference framework based on state-space models, using Kalman filtering for efficient likelihood evaluation. Argus leverages JAX for just-in-time compilation, GPU acceleration, and automatic differentiation, facilitating rapid Bayesian inference with gradient-based samplers. The state-space approach provides a computationally efficient alternative to traditional frequency-domain methods, offering linear scaling with the number of pulse times-of-arrival, and natural handling of non-stationary processes.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2510.11077

Authors: Kimpson, Tom, O'Neill, Nicholas J., Meyers, Patrick M., Melatos, Andrew

Institutions: Australian Research Council, The University of Melbourne, ARC Centre of Excellence for Gravitational Wave Discovery, California Institute of Technology

Language: English

Topic: Pulsars and Gravitational Waves Research

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: Astronomy Australia Limited, Australian Government, Swinburne University of Technology, State Government of Victoria

OpenAlex ID: W7090816618

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