Work

Opening the Black Box: What Neural Networks Learn from Pulsar Timing Array Data

Year: 2026

Type: preprint

Abstract: In recent years, simulation-based inference (SBI) methods have been proposed to address several data-analysis challenges faced by existing and planned gravitational-wave experiments. For example, SBI classification has recently been shown to significantly improve the prospects for detecting anisotropies in pulsar timing array (PTA) data. In this work, we use a simple toy model of a PTA to provide a more pedagogical explanation of how, and under which circumstances, SBI-based detection can improve on classical detection statistics for gravitational-wave background anisotropies.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.12308

Authors: Alvey, James, Mitridate, Andrea, Pieroni, Mauro, Romano, Joseph D.

Institutions: University of Cambridge, Imperial College London, Consejo Superior de Investigaciones Científicas, Instituto de Estructura de la Materia, The University of Texas Rio Grande Valley

Language: English

Topic: Pulsars and Gravitational Waves Research

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Comunidad de Madrid, University of Texas Rio Grande Valley, Imperial College London

Awards: G2247124888, The NANOGrav Physics Frontiers Center

OpenAlex ID: W7221132220

Cites: 30

Cited by:

Related to: 10