Identifying extra high frequency gravitational waves generated from oscillons with cuspy potentials using deep neural networks

TL;DR

This paper demonstrates the use of deep neural networks, specifically CNNs, to effectively identify and classify high frequency gravitational wave signals generated from oscillons with cuspy potentials, improving detection efficiency.

Contribution

It introduces a novel deep learning-based data processing scheme for detecting high frequency gravitational waves, enhancing accuracy and overcoming traditional method limitations.

Findings

CNN achieves up to 100% classification accuracy at certain sample ratios.

Deep learning improves detection efficiency and parameter estimation for HFGWs.

Anti-overfitting techniques enhance model performance.

Abstract

During oscillations of cosmology inflation around the minimum of a cuspy potential after inflation, the existence of extra high frequency gravitational waves (HFGWs) (GHz) has been proven effectively recently. Based on the electromagnetic resonance system for detecting such extra HFGWs, we adopt a new data processing scheme to identify the corresponding GW signal, which is the transverse perturbative photon fluxes (PPF). In order to overcome the problems of low efficiency and high interference in traditional data processing methods, we adopt deep learning to extract PPF and make some source parameters estimation. Deep learning is able to provide an effective method to realize classification and prediction tasks. Meanwhile, we also adopt anti-overfitting technique and make adjustment of some hyperparameters in the course of study, which improve the performance of classifier and predictor to a certain extent. Here the convolutional neural network (CNN) is used to implement deep learning process concretely. In this case, we investigate the classification accuracy varying with the ratio between the number of positive and negative samples. When such ratio exceeds to 0.11, the accuracy could reach up to 100%.