# A Deep Transfer Learning Approach on Identifying Glitch Wave-form in Gravitational Wave Data

arXiv: https://arxiv.org/abs/2107.01863 · HTML: https://mlsanity.com/paper/2107.01863/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 5 Jul 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO](https://mlsanity.com/paper/1706.07446/) (2017)
- [Deep Multi-view Models for Glitch Classification](https://mlsanity.com/paper/1705.00034/) (2017)
- [Neural Networks, Artificial Intelligence and the Computational Brain](https://mlsanity.com/paper/2101.08635/) (2020)

## Built on by (most cited first, 1 of 1)

- [Efficient Machine Learning Ensemble Methods for Detecting Gravitational Wave Glitches in LIGO Time Series](https://mlsanity.com/paper/2311.02106/) (2023)

## Abstract (arXiv)

LIGO interferometer is considered the most sensitive and complicated gravitational experimental equipment ever built. Its main objective is to detect the gravitational wave from the strongest events in the universe by observing if the length of its 4-kilometer arms change by a distance 10,000 times smaller than the diameter of a proton. Due to its sensitivity, interferometer is prone to the disturbance of external noises which affects the data being collected to detect the gravitational wave. These noises are commonly called by the gravitational-wave community as glitches. This study focuses on identifying those glitches using different deep transfer learning algorithms. The extensive experiment shows that algorithm with architecture VGG19 recorded the highest AUC-ROC among other experimented algorithm with 0.9898. While all of the experimented algorithm achieved a considerably high AUC-ROC, some of the algorithm suffered from class imbalance of the dataset which has a detrimental effect when identifying other classes.