Searches for the BSM scenarios at the LHC using decision tree based machine learning algorithms: A comparative study and review of Random Forest, Adaboost, XGboost and LightGBM frameworks

Abstract

Machine learning algorithms are now being extensively used in our daily lives, spanning across diverse industries as well as academia. In the field of high energy physics (HEP), the most common and challenging task is separating a rare signal from a much larger background. The boosted decision tree (BDT) algorithm has been a cornerstone of the high energy physics for analyzing event triggering, particle identification, jet tagging, object reconstruction, event classification, and other related tasks for quite some time. This article presents a comprehensive overview of research conducted by both HEP experimental and phenomenological groups that utilize decision tree algorithms in the context of the Standard Model and Supersymmetry (SUSY). We also summarize the basic concept of machine learning and decision tree algorithm along with the working principle of \texttt{Random Forest}, \texttt{AdaBoost} and two gradient boosting frameworks, such as \texttt{XGBoost}, and \texttt{LightGBM}. Using a case study of electroweakino productions at the high luminosity LHC, we demonstrate how these algorithms lead to improvement in the search sensitivity compared to traditional cut-based methods in both compressed and non-compressed R-parity conserving SUSY scenarios. The effect of different hyperparameters and their optimization, feature importance study using SHapley values are also discussed in detail.

Published in EPJST

References in corpus (26)

• Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
• Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
• PYTHIA 6.4 Physics and Manual
• The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
• Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
• Boosted Decision Trees as an Alternative to Artificial Neural Networks for Particle Identification
• Observation of the diphoton decay of the Higgs boson and measurement of its properties
• Evidence for production of single top quarks and first direct measurement of |Vtb|
• Observation of Single Top Quark Production and Measurement of |Vtb| with CDF
• GUT-inspired SUSY and the muon g-2 anomaly: prospects for LHC 14 TeV
• Studies of Boosted Decision Trees for MiniBooNE Particle Identification
• Anomalous muon magnetic moment, supersymmetry, naturalness, LHC search limits and the landscape
• The Electroweak Sector of the pMSSM in the Light of LHC - 8 TeV and Other Data
• Many faces of low mass neutralino dark matter in the unconstrained MSSM, LHC data and new signals
• A Coverage Study of the CMSSM Based on ATLAS Sensitivity Using Fast Neural Networks Techniques
• Non-universal Gaugino mass models under the lamppost of muon (g-2)
• Electron and muon magnetic moments and implications for dark matter and model characterisation in non-universal $U(1{)}^{\prime }$ supersymmetric models
• Lepton number violating mSUGRA and neutrino masses
• The ABC of RPV: Classification of R-Parity Violating Signatures at the LHC for Small Couplings
• Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model
• Supersymmetric gauged U(1)${}_{L_{\mu }-L_{\tau }}$ model for electron and muon $(g-2)$ anomaly
• Impact of LHC data on muon $g-2$ solutions in vector-like extension of the Constrained MSSM
• Constraints from neutrino masses and muon (g-2) in the bilinear R-parity violating supersymmetric model
• Bilinear R-parity violating supersymmetry under the light of neutrino oscillation, higgs and flavor data
• Improving sensitivity of trilinear RPV SUSY searches using machine learning at the LHC
• Search for top squarks in the four-body decay mode with single lepton final states in proton-proton collisions at $\sqrt{s}$ = 13 TeV

Cited by in corpus (6)

• The ABC of RPV II: Classification of R-parity Violating Signatures from UDD Couplings and their Coverage at the LHC
• Probing sub-TeV Higgsinos aided by a ML-based top tagger in the context of Trilinear RPV SUSY
• Improving smuon searches with Neural Networks
• Projected sensitivity of CTAO to axion-like particles from blazars with a machine learning approach
• The Physics Behind ML-based Quark-Gluon Taggers
• RocketStack: Level-aware Deep Recursive Ensemble Learning Architecture