MLAnalysis: An open-source program for high energy physics analyses

TL;DR

MLAnalysis is an open-source Python tool that converts particle physics simulation data into formats suitable for machine learning, enabling efficient searches for new physics signals using various ML algorithms.

Contribution

It introduces a new Python-based program that facilitates phenomenological analysis in particle physics with integrated machine learning algorithms.

Findings

Supports conversion of LHE and LHCO files for ML analysis

Includes multiple ML algorithms like IF, NIF, and KMAD

Provides basic tools for kinematic feature analysis

Abstract

We present a python-based program for phenomenological investigations in particle physics using machine learning algorithms, called \verb"MLAnalysis". The program is able to convert LHE and LHCO files generated by \verb"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.