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-search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202406112000+TO+202406172000]&start=0&max_results=5000
-<h1>New astro-ph.* submissions cross listed on cs.AI, physics.data-an, stat.*, cs.LG staritng 202406112000 and ending 202406172000</h1>Feed last updated: 2024-06-17T00:00:00-04:00
+search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202406122000+TO+202406182000]&start=0&max_results=5000
+<h1>New astro-ph.* submissions cross listed on physics.data-an, cs.LG, cs.AI, stat.* staritng 202406122000 and ending 202406182000</h1>Feed last updated: 2024-06-18T00:00:00-04:00<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
+this data in astrophysics is the atmospheric retrievals of exoplanets. In order
+to help bridge the gap between machine learning and astrophysics domain
+experts, the 2023 Ariel Data Challenge was hosted to predict posterior
+distributions of 7 exoplanetary features. The procedure outlined in this paper
+leveraged a combination of two deep learning models to address this challenge:
+a Multivariate Gaussian model that generates the mean and covariance matrix of
+a multivariate Gaussian distribution, and a Uniform Quantile model that
+predicts quantiles for use as the upper and lower bounds of a uniform
+distribution. Training of the Multivariate Gaussian model was found to be
+unstable, while training of the Uniform Quantile model was stable. An ensemble
+of uniform distributions was found to have competitive results during testing
+(posterior score of 696.43), and when combined with a multivariate Gaussian
+distribution achieved a final rank of third in the 2023 Ariel Data Challenge
+(final score of 681.57).</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>