From 603bc48a87d2445199b5ba9e6350e12ea2bc8f36 Mon Sep 17 00:00:00 2001 From: GitHub Action Date: Wed, 4 Dec 2024 04:12:24 +0000 Subject: [PATCH] new run happened --- index.html | 24 ++---------------------- 1 file changed, 2 insertions(+), 22 deletions(-) diff --git a/index.html b/index.html index 88fbca9..dd7c670 100644 --- a/index.html +++ b/index.html @@ -1,25 +1,5 @@ -search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202411272000+TO+202412032000]&start=0&max_results=5000 -

New astro-ph.* submissions cross listed on cs.LG, physics.data-an, stat.*, cs.AI staritng 202411272000 and ending 202412032000

Feed last updated: 2024-12-02T00:00:00-05:00

Wavelet Scattering Transform for Gravitational Waves Analysis. An - Application to Glitch Characterization

Authors: Alessandro Licciardi, Davide Carbone, Lamberto Rondoni, Alessandro Nagar
Comments: No comment found
Primary Category: gr-qc
All Categories: gr-qc, astro-ph.IM, physics.data-an

Gravitational waves, first predicted by Albert Einstein within the framework -of general relativity, were confirmed in 2015 by the LIGO/Virgo collaboration, -marking a pivotal breakthrough in astrophysics. Despite this achievement, a key -challenge remains in distinguishing true gravitational wave signals from noise -artifacts, or "glitches," which can distort data and affect the quality of -observations. Current state-of-the-art methods, such as the Q-transform, are -widely used for signal processing, but face limitations when addressing certain -types of signals. In this study, we investigate the Wavelet Scattering -Transform (WST), a recent signal analysis method, as a complementary approach. -Theoretical motivation for WST arises from its stability under signal -deformations and its equivariance properties, which make it particularly suited -for the complex nature of gravitational wave data. Our experiments on the LIGO -O1a dataset show that WST simplifies classification tasks and enables the use -of more efficient architectures compared to traditional methods. Furthermore, -we explore the potential benefits of integrating WST with the Q-transform, -demonstrating that ensemble methods exploiting both techniques can capture -complementary features of the signal and improve overall performance. This work -contributes to advancing machine learning applications in gravitational wave -analysis, introducing refined preprocessing techniques that improve signal -detection and classification.


Unsupervised Learning Approach to Anomaly Detection in Gravitational +search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202411282000+TO+202412042000]&start=0&max_results=5000 +

New astro-ph.* submissions cross listed on physics.data-an, cs.LG, stat.*, cs.AI staritng 202411282000 and ending 202412042000

Feed last updated: 2024-12-03T00:00:00-05:00

Unsupervised Learning Approach to Anomaly Detection in Gravitational Wave Data

Authors: Ammar Fayad
Comments: No comment found
Primary Category: gr-qc
All Categories: gr-qc, astro-ph.IM, cs.LG

Gravitational waves (GW), predicted by Einstein's General Theory of Relativity, provide a powerful probe of astrophysical phenomena and fundamental physics. In this work, we propose an unsupervised anomaly detection method