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

James Alvey; Andrea Mitridate; Mauro Pieroni; Joseph D. Romano

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arXiv · 2610.12308

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

James Alvey·Andrea Mitridate·Mauro Pieroni·Joseph D. Romano

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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.

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James Alvey, Andrea Mitridate, Mauro Pieroni, Joseph D. Romano. 2026-10-08. Opening the Black Box: What Neural Networks Learn from Pulsar Timing Array Data. https://arxiv.org/abs/2610.12308

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