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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>

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