Summary of Cross-temporal Spectrogram Autoencoder (ctsae): Unsupervised Dimensionality Reduction For Clustering Gravitational Wave Glitches, by Yi Li et al.
Cross-Temporal Spectrogram Autoencoder (CTSAE): Unsupervised Dimensionality Reduction for Clustering Gravitational Wave Glitches
by Yi Li, Yunan Wu, Aggelos K. Katsaggelos
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG); General Relativity and Quantum Cosmology (gr-qc)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The advancement of LIGO has significantly enhanced the feasibility and reliability of gravitational wave detection, but its high sensitivity makes it susceptible to transient noises known as glitches. Traditional approaches predominantly employ fully supervised or semi-supervised algorithms for glitch classification and clustering. However, in the future task of identifying and classifying glitches across main and auxiliary channels, it is impractical to build a dataset with manually labeled ground-truth. To address this challenge, we introduce the Cross-Temporal Spectrogram Autoencoder (CTSAE), a pioneering unsupervised method for dimensionality reduction and clustering of gravitational wave glitches. CTSAE integrates a novel four-branch autoencoder with a hybrid of Convolutional Neural Networks (CNN) and Vision Transformers (ViT). Our model, trained and evaluated on the GravitySpy O3 dataset on the main channel, demonstrates superior performance in clustering tasks when compared to state-of-the-art semi-supervised learning methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LIGO has gotten really good at detecting gravitational waves, but that makes it more prone to noise. We need a way to figure out what’s real and what’s just noise. Traditionally, people use algorithms that are either fully supervised or semi-supervised. But the problem is that we don’t have enough labeled data to make it work. So, we came up with something new called CTSAE (Cross-Temporal Spectrogram Autoencoder). It uses a special kind of AI model that combines different parts together to help us identify and group noise better than before. |
Keywords
» Artificial intelligence » Autoencoder » Classification » Clustering » Cnn » Dimensionality reduction » Semi supervised » Supervised » Unsupervised » Vit
