diff --git a/index.html b/index.html index b9faf99..2573043 100644 --- a/index.html +++ b/index.html @@ -1,5 +1,5 @@ -search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202406132000+TO+202406192000]&start=0&max_results=5000 -

New astro-ph.* submissions cross listed on cs.LG, stat.*, cs.AI, physics.data-an staritng 202406132000 and ending 202406192000

Feed last updated: 2024-06-19T00:00:00-04:00

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 +

New astro-ph.* submissions cross listed on cs.LG, physics.data-an, cs.AI, stat.* staritng 202406142000 and ending 202406202000

Feed last updated: 2024-06-20T00:00:00-04:00

Predicting Exoplanetary Features with a Residual Model for Uniform and Gaussian Distributions

Authors: Andrew Sweet
Comments: 19 pages, 7 figures, Conference proceedings for ECML PKDD 2023
Primary Category: astro-ph.EP
All Categories: astro-ph.EP, astro-ph.IM, cs.LG, physics.data-an

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 @@

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.


Insights into Dark Matter Direct Detection Experiments: Decision Trees - versus Deep Learning

Authors: Daniel E. Lopez-Fogliani, Andres D. Perez, Roberto Ruiz de Austri
Comments: 26 pages, 7 figures, 2 tables
Primary Category: astro-ph.IM
All Categories: astro-ph.IM, astro-ph.HE, hep-ex, hep-ph, physics.data-an

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.


+distance.