new run happened

index.html

@@ -1,5 +1,5 @@
-search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202406132000+TO+202406192000]&start=0&max_results=5000
-<h1>New astro-ph.* submissions cross listed on cs.LG, stat.*, cs.AI, physics.data-an staritng 202406132000 and ending 202406192000</h1>Feed last updated: 2024-06-19T00:00:00-04:00<a href="http://arxiv.org/pdf/2406.10771v1"><h2>Predicting Exoplanetary Features with a Residual Model for Uniform and
+search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202406142000+TO+202406202000]&start=0&max_results=5000
+<h1>New astro-ph.* submissions cross listed on cs.LG, physics.data-an, cs.AI, stat.* staritng 202406142000 and ending 202406202000</h1>Feed last updated: 2024-06-20T00:00:00-04:00<a href="http://arxiv.org/pdf/2406.10771v1"><h2>Predicting Exoplanetary Features with a Residual Model for Uniform and
   Gaussian Distributions</h2></a>Authors:  Andrew Sweet</br>Comments: 19 pages, 7 figures, Conference proceedings for ECML PKDD 2023</br>Primary Category: astro-ph.EP</br>All Categories: astro-ph.EP, astro-ph.IM, cs.LG, physics.data-an</br><p>The advancement of technology has led to rampant growth in data collection
 across almost every field, including astrophysics, with researchers turning to
 machine learning to process and analyze this data. One prominent example of
@@ -31,19 +31,4 @@ <h1>New astro-ph.* submissions cross listed on cs.LG, stat.*, cs.AI, physics.dat
 radiation risk in both deep space and near-Earth environments. The model is
 very relevant for mission operations and planning, especially when flare
 characteristics and source location are not available in real time, as at Mars
-distance.</p></br><a href="http://arxiv.org/pdf/2406.10372v1"><h2>Insights into Dark Matter Direct Detection Experiments: Decision Trees
-  versus Deep Learning</h2></a>Authors:  Daniel E. Lopez-Fogliani, Andres D. Perez, Roberto Ruiz de Austri</br>Comments: 26 pages, 7 figures, 2 tables</br>Primary Category: astro-ph.IM</br>All Categories: astro-ph.IM, astro-ph.HE, hep-ex, hep-ph, physics.data-an</br><p>The detection of Dark Matter (DM) remains a significant challenge in particle
-physics. This study exploits advanced machine learning models to improve
-detection capabilities of liquid xenon time projection chamber experiments,
-utilizing state-of-the-art transformers alongside traditional methods like
-Multilayer Perceptrons and Convolutional Neural Networks. We evaluate various
-data representations and find that simplified feature representations,
-particularly corrected S1 and S2 signals, retain critical information for
-classification. Our results show that while transformers offer promising
-performance, simpler models like XGBoost can achieve comparable results with
-optimal data representations. We also derive exclusion limits in the
-cross-section versus DM mass parameter space, showing minimal differences
-between XGBoost and the best performing deep learning models. The comparative
-analysis of different machine learning approaches provides a valuable reference
-for future experiments by guiding the choice of models and data representations
-to maximize detection capabilities.</p></br>
+distance.</p></br>