# Reducci'on de ruido por medio de autoencoders: caso de estudio con la se~nal GW150914

arXiv: https://arxiv.org/abs/2510.00873 · HTML: https://mlsanity.com/paper/2510.00873/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 1 Oct 2025.

## Builds on (most cited first, 3 of 3)

- [Convolutional Deep Denoising Autoencoders for Radio Astronomical Images](https://mlsanity.com/paper/2110.08618/) (2021)
- [Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders](https://mlsanity.com/paper/2107.12698/) (2021)
- [A Generative Adversarial Approach To ECG Synthesis And Denoising](https://mlsanity.com/paper/2009.02700/) (2020)

## Abstract (arXiv)

This brief study focuses on the application of autoencoders to improve the quality of low-amplitude signals, such as gravitational events. A pre-existing autoencoder was trained using cosmic event data, optimizing its architecture and parameters. The results show a significant increase in the signal-to-noise ratio of the processed signals, demonstrating the potential of autoencoders in the analysis of small signals with multiple sources of interference.