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ML-Based Analysis of Particle Distributions in High-Intensity Laser Experiments: Role of Binning Strategy.
Yury Rodimkov1, Evgeny Efimenko2,1, Valentin Volokitin1,3
1Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603950 Nizhni Novgorod, Russia.
Entropy (Basel, Switzerland)
|December 30, 2020
Summary
Optimizing data binning is crucial for machine learning in experimental physics. Proper binning enhances the performance of methods like Support Vector Machines (SVM) and Gradient Boosting Trees (GBT), especially with limited data.
Area of Science:
- Experimental physics
- Quantum electrodynamics
- Machine learning applications
Background:
- Big data processing and statistical inferences are critical in experimental physics.
- Optimal data preprocessing, balancing detail and noise, is essential for machine learning efficiency.
- In strong-field quantum electrodynamics experiments, data binning is key for observed particle and photon distributions.
Purpose of the Study:
- To analyze the impact of data binning on various machine learning methods.
- To assess how binning affects performance in simulated experimental physics data.
- To provide guidance for experimental planning regarding data binning and hyperparameter optimization.
Main Methods:
- Numerical simulations mimicking experimental physics data.
- Evaluation of Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Fully-Connected Neural Network (FCNN), and Convolutional Neural Network (CNN).
- Analysis of binning scale's effect on machine learning model accuracy.
Main Results:
- Data binning significantly impacts SVM and GBT performance.
- FCNN and CNN show resilience to binning, suggesting an ability to learn optimal binning.
- Optimizing binning scale alongside hyperparameters improves efficiency, particularly with limited training datasets.
Conclusions:
- Data binning is a critical hyperparameter in machine learning for experimental physics.
- The choice of machine learning model influences its sensitivity to data binning.
- Optimized binning strategies can enhance experimental data analysis accuracy and efficiency.
