Large-kernel Convolutional Neural Networks for Wide Parameter-Space Searches of Continuous Gravitational Waves

TL;DR

This paper presents a novel deep neural network architecture for wide-parameter-space searches of continuous gravitational waves, achieving higher sensitivity than previous methods across multiple search scenarios.

Contribution

The authors adapt design principles to develop a DNN for wide parameter-space CW searches, demonstrating improved sensitivity and generalization over prior neural network approaches.

Findings

DNNs outperform previous sensitivity benchmarks in all test cases.

Training on the entire frequency band enables a single DNN to perform comprehensive searches.

The DNN shows good generalization to signals with varying amplitude, frequency, and sky position.

Abstract

The sensitivity of wide-parameter-space searches for continuous gravitational waves (CWs) is limited by their high computational cost. Deep learning is being studied as an alternative method to replace various aspects of a CW search. In previous work arXiv:2305.01057[gr-qc], new design principles were presented for deep neural network (DNN) search of CWs and such DNNs were trained to perform a targeted search with matched filtering sensitivity. In this paper, we adapt these design principles to build a DNN architecture for wide parameter-space searches in 10 days of data from two detectors (H1 and L1). We train a DNN for each of the benchmark cases: six all-sky searches and eight directed searches at different frequencies in the search band of 20 - 1000 Hz. We compare our results to the DNN sensitivity achieved from Dreissigacker and Prix arXiv:2005.04140[gr-qc] and find that our trained DNNs are more sensitive in all the cases. The absolute improvement in detection probability ranges from 6.5% at 20 Hz to 38% at 1000 Hz in the all-sky cases and from 1.5% at 20 Hz to 59.4% at 500 Hz in the directed cases. An all-sky DNN trained on the entire search band of 20 - 1000 Hz shows a high sensitivity at all frequencies providing a proof of concept for training a single DNN to perform the entire search. We also study the generalization of the DNN performance to signals with different signal amplitude, frequency and the dependence of the DNN sensitivity on sky position.