# Unsupervised Learning Architecture for Classifying the Transient Noise of Interferometric Gravitational-wave Detectors

arXiv: https://arxiv.org/abs/2111.10053 · HTML: https://mlsanity.com/paper/2111.10053/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 55 references that are in the map.
- First posted on arXiv 19 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning](https://mlsanity.com/paper/1611.08331/) (2016)
- [Deep Multi-view Models for Glitch Classification](https://mlsanity.com/paper/1705.00034/) (2017)
- [DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data](https://mlsanity.com/paper/1805.02296/) (2018)

## Built on by (most cited first, 4 of 4)

- [A review of unsupervised learning in astronomy](https://mlsanity.com/paper/2406.17316/) (2024)
- [Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset](https://mlsanity.com/paper/2208.03623/) (2022)
- [Efficient Machine Learning Ensemble Methods for Detecting Gravitational Wave Glitches in LIGO Time Series](https://mlsanity.com/paper/2311.02106/) (2023)
- [Enhancing the reliability of machine learning for gravitational wave parameter estimation with attention-based models](https://mlsanity.com/paper/2501.10486/) (2025)

## Abstract (arXiv)

In the data obtained by laser interferometric gravitational wave detectors, transient noise with non-stationary and non-Gaussian features occurs at a high rate. This often results in problems such as detector instability and the hiding and/or imitation of gravitational-wave signals. This transient noise has various characteristics in the time--frequency representation, which is considered to be associated with environmental and instrumental origins. Classification of transient noise can offer clues for exploring its origin and improving the performance of the detector. One approach for accomplishing this is supervised learning. However, in general, supervised learning requires annotation of the training data, and there are issues with ensuring objectivity in the classification and its corresponding new classes. By contrast, unsupervised learning can reduce the annotation work for the training data and ensure objectivity in the classification and its corresponding new classes. In this study, we propose an unsupervised learning architecture for the classification of transient noise that combines a variational autoencoder and invariant information clustering. To evaluate the effectiveness of the proposed architecture, we used the dataset (time--frequency two-dimensional spectrogram images and labels) of the Laser Interferometer Gravitational-wave Observatory (LIGO) first observation run prepared by the Gravity Spy project. The classes provided by our proposed unsupervised learning architecture were consistent with the labels annotated by the Gravity Spy project, which manifests the potential for the existence of unrevealed classes.