Physics
AI and AutoML in High Energy Physics: Collider Event Classification with mljar-supervised
- AI in high energy physics
- AutoML collider analysis
- machine learning particle physics
- mljar-supervised research
- event classification deep learning
- ROC AUC physics models
- ensemble learning in physics
- automated machine learning experiments
- tabular data physics AI
MLJAR tools were used in the following publication.
Application of Deep Learning Technique to an Analysis of Hard Scattering Processes at Colliders
Lev Dudko, Petr Volkov, Georgii Vorotnikov, Andrei Zaborenko
Skobeltsyn Institute of Nuclear Physics, M.V. Lomonosov Moscow State University, Moscow, Russian Federation
This research explores the application of artificial intelligence and Automated Machine Learning (AutoML) to classification tasks in high energy collider physics. Using the mljar-supervised framework, the authors evaluated automated ensemble models against tuned deep neural networks for top-quark event identification and QCD background suppression. AutoML models achieved competitive ROC AUC performance and in some cases outperformed manually tuned DNNs on test datasets, demonstrating the viability of automated model selection in particle physics analysis. The study highlights how machine learning, ensemble methods, and AutoML pipelines can accelerate scientific discovery in large-scale physics experiments.
DLCP’21 – Deep Learning in Computational Physics Workshop • September 14, 2021
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