Improved deep learning techniques in gravitational-wave data analysis

TL;DR

This paper explores the application of optimized deep learning models, particularly CNNs with batch normalization and dropout, for gravitational-wave detection, demonstrating improved efficiency and robustness over traditional methods.

Contribution

The study introduces optimization techniques into CNN models for GW detection and evaluates their robustness across different parameter ranges, highlighting advantages over matched-filtering.

Findings

CNN models with batch normalization and dropout improve detection efficiency.

Deep learning models show robustness to parameter variations.

CNNs outperform traditional matched-filtering in flexibility.

Abstract

In recent years, convolutional neural network (CNN) and other deep learning models have been gradually introduced into the area of gravitational-wave (GW) data processing. Compared with the traditional matched-filtering techniques, CNN has significant advantages in efficiency in GW signal detection tasks. In addition, matched-filtering techniques are based on the template bank of the existing theoretical waveform, which makes it difficult to find GW signals beyond theoretical expectation. In this paper, based on the task of GW detection of binary black holes, we introduce the optimization techniques of deep learning, such as batch normalization and dropout, to CNN models. Detailed studies of model performance are carried out. Through this study, we recommend to use batch normalization and dropout techniques in CNN models in GW signal detection tasks. Furthermore, we investigate the generalization ability of CNN models on different parameter ranges of GW signals. We point out that CNN models are robust to the variation of the parameter range of the GW waveform. This is a major advantage of deep learning models over matched-filtering techniques.