MLAnalysis: An open-source program for high energy physics analyses
Published: 24 October 2023| Version 1 | DOI: 10.17632/xnrgv2z76h.1
Contributors:
Yu-Chen Guo, Fan Feng, An Di, Shi-Qi Lu, Ji-Chong YangDescription
We present a python-based program for phenomenological investigations in particle physics using machine learning algorithms, called MLAnalysis. The program is able to convert LHE and LHCO files generated by MadGraph5_aMC@NLO into data sets for machine learning algorithms, which can analyze the information of the events. At present, it contains three machine learning (ML) algorithms: isolation forest (IF) algorithm, nested isolation forest (NIF) algorithm, kmeans anomaly detection (KMAD), and some basic functionality to analyze the kinematic features of a data set. Users can use this program to improve the efficiency of searching for new physics signals.
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Categories
Computational Physics, Machine Learning, Particle Physics Phenomenology