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-search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202406152000+TO+202406212000]&start=0&max_results=5000
-<h1>New astro-ph.* submissions cross listed on cs.AI, cs.LG, physics.data-an, stat.* staritng 202406152000 and ending 202406212000</h1>Feed last updated: 2024-06-21T00:00:00-04:00<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/2406.12730v1"><h2>Predicting the energetic proton flux with a machine learning regression
-  algorithm</h2></a>Authors:  Mirko Stumpo, Monica Laurenza, Simone Benella, Maria Federica Marcucci</br>Comments: No comment found</br>Primary Category: astro-ph.SR</br>All Categories: astro-ph.SR, astro-ph.IM, cs.LG, physics.space-ph</br><p>The need of real-time of monitoring and alerting systems for Space Weather
-hazards has grown significantly in the last two decades. One of the most
-important challenge for space mission operations and planning is the prediction
-of solar proton events (SPEs). In this context, artificial intelligence and
-machine learning techniques have opened a new frontier, providing a new
-paradigm for statistical forecasting algorithms. The great majority of these
-models aim to predict the occurrence of a SPE, i.e., they are based on the
-classification approach. In this work we present a simple and efficient machine
-learning regression algorithm which is able to forecast the energetic proton
-flux up to 1 hour ahead by exploiting features derived from the electron flux
-only. This approach could be helpful to improve monitoring systems of the
-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/2406.14297v1"><h2>AI in Space for Scientific Missions: Strategies for Minimizing
+search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202406182000+TO+202406242000]&start=0&max_results=5000
+<h1>New astro-ph.* submissions cross listed on cs.LG, cs.AI, stat.*, physics.data-an staritng 202406182000 and ending 202406242000</h1>Feed last updated: 2024-06-24T00:00:00-04:00<a href="http://arxiv.org/pdf/2406.14297v1"><h2>AI in Space for Scientific Missions: Strategies for Minimizing
   Neural-Network Model Upload</h2></a>Authors:  Jonah Ekelund, Ricardo Vinuesa, Yuri Khotyaintsev, Pierre Henri, Gian Luca Delzanno, Stefano Markidis</br>Comments: No comment found</br>Primary Category: cs.AI</br>All Categories: cs.AI, astro-ph.IM</br><p>Artificial Intelligence (AI) has the potential to revolutionize space
 exploration by delegating several spacecraft decisions to an onboard AI instead
 of relying on ground control and predefined procedures. It is likely that there