# Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders

## TL;DR

This paper introduces EDRDAE, a novel deep recurrent auto-encoder with a signal amplifier and curriculum learning, significantly improving denoising of gravitational wave signals in noisy, non-Gaussian environments.

## Contribution

The paper presents EDRDAE, a new model that enhances denoising performance for gravitational waves in challenging noise conditions, with better generalization to complex signals.

## Key findings

- EDRDAE outperforms existing denoising methods in low SNR environments.
- The model effectively denoises complex gravitational wave signals unseen during training.
- Curriculum learning improves denoising accuracy across varying noise levels.

## Abstract

Denoising of time domain data is a crucial task for many applications such as communication, translation, virtual assistants etc. For this task, a combination of a recurrent neural net (RNNs) with a Denoising Auto-Encoder (DAEs) has shown promising results. However, this combined model is challenged when operating with low signal-to-noise ratio (SNR) data embedded in non-Gaussian and non-stationary noise. To address this issue, we design a novel model, referred to as 'Enhanced Deep Recurrent Denoising Auto-Encoder' (EDRDAE), that incorporates a signal amplifier layer, and applies curriculum learning by first denoising high SNR signals, before gradually decreasing the SNR until the signals become noise dominated. We showcase the performance of EDRDAE using time-series data that describes gravitational waves embedded in very noisy backgrounds. In addition, we show that EDRDAE can accurately denoise signals whose topology is significantly more complex than those used for training, demonstrating that our model generalizes to new classes of gravitational waves that are beyond the scope of established denoising algorithms.