new run happened

index.html

@@ -1,25 +1,5 @@
-search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202411272000+TO+202412032000]&start=0&max_results=5000
-<h1>New astro-ph.* submissions cross listed on cs.LG, physics.data-an, stat.*, cs.AI staritng 202411272000 and ending 202412032000</h1>Feed last updated: 2024-12-02T00:00:00-05:00<a href="http://arxiv.org/pdf/2411.19122v1"><h2>Wavelet Scattering Transform for Gravitational Waves Analysis. An
-  Application to Glitch Characterization</h2></a>Authors:  Alessandro Licciardi, Davide Carbone, Lamberto Rondoni, Alessandro Nagar</br>Comments: No comment found</br>Primary Category: gr-qc</br>All Categories: gr-qc, astro-ph.IM, physics.data-an</br><p>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.</p></br><a href="http://arxiv.org/pdf/2411.19450v1"><h2>Unsupervised Learning Approach to Anomaly Detection in Gravitational
+search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202411282000+TO+202412042000]&start=0&max_results=5000
+<h1>New astro-ph.* submissions cross listed on physics.data-an, cs.LG, stat.*, cs.AI staritng 202411282000 and ending 202412042000</h1>Feed last updated: 2024-12-03T00:00:00-05:00<a href="http://arxiv.org/pdf/2411.19450v1"><h2>Unsupervised Learning Approach to Anomaly Detection in Gravitational
   Wave Data</h2></a>Authors:  Ammar Fayad</br>Comments: No comment found</br>Primary Category: gr-qc</br>All Categories: gr-qc, astro-ph.IM, cs.LG</br><p>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