Machine Learning applied to high-energy physics fits in new lecture notes

Researchers have developed new lecture notes detailing the application of Machine Learning (ML) surrogates for statistical fits in high-energy physics. These notes outline a comprehensive ML workflow, including the use of Boosted Decision Trees to approximate likelihood functions and active learning with Gaussian processes for efficient data generation. The methodology is demonstrated on the $B^\pm \to K^\pm \nu \bar{\nu}$ anomaly at Belle II, showcasing how ML can explore parameter spaces for Axion-Like Particles (ALPs) under experimental constraints. AI

IMPACT Introduces advanced ML techniques for complex statistical analysis in physics, potentially accelerating discovery.

RANK_REASON The item is a research paper published on arXiv detailing novel applications of ML in high-energy physics. [lever_c_demoted from research: ic=1 ai=1.0]

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• Belle II
• $B^\pm \to K^\pm \nu \bar{\nu}$
• Gaussian processes
• high-energy physics
• Machine Learning

• paper
• other

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