Advances in Multi-Variate Analysis Methods for New Physics Searches at the Large Hadron Collider
arXiv:2105.07530 · doi:10.1016/j.revip.2021.100063
Abstract
Between the years 2015 and 2019, members of the Horizon 2020-funded Innovative Training Network named "AMVA4NewPhysics" studied the customization and application of advanced multivariate analysis methods and statistical learning tools to high-energy physics problems, as well as developed entirely new ones. Many of those methods were successfully used to improve the sensitivity of data analyses performed by the ATLAS and CMS experiments at the CERN Large Hadron Collider; several others, still in the testing phase, promise to further improve the precision of measurements of fundamental physics parameters and the reach of searches for new phenomena. In this paper, the most relevant new tools, among those studied and developed, are presented along with the evaluation of their performances.
arXiv comment101 pages, 21 figures, submitted to Elsevier. [v2]: Updated to published version (in 'Reviews in Physics')
References in corpus (43)
- 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
- The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
- The anti-k_t jet clustering algorithm
- An Introduction to PYTHIA 8.2
- Asymptotic formulae for likelihood-based tests of new physics
- DELPHES 3, A modular framework for fast simulation of a generic collider experiment
- R-parity violating supersymmetry
- Particle-flow reconstruction and global event description with the CMS detector
- Observation of Long-Range Near-Side Angular Correlations in Proton-Proton Collisions at the LHC
- Pileup Per Particle Identification
- A Precision Measurement of the Mass of the Top Quark
- Deep learning in color: towards automated quark/gluon jet discrimination
- Jet Flavour Classification Using DeepJet
- Heavy Dark Matter Through the Higgs Portal
- A Monte Carlo global analysis of the Standard Model Effective Field Theory: the top quark sector
- Higgs boson pair production at NNLO with top quark mass effects
- Search for the Standard Model Higgs boson produced in association with top quarks and decaying into in pp collisions at = 8 TeV with the ATLAS detector
- A measurement of the Higgs boson mass in the diphoton decay channel
- QCD-Aware Recursive Neural Networks for Jet Physics
- Automation of the matrix element reweighting method
- Search for a standard model Higgs boson produced in association with a top-quark pair and decaying to bottom quarks using a matrix element method
- Search for heavy resonances decaying into a or boson and a Higgs boson in final states with leptons and -jets in 36 fb of TeV collisions with the ATLAS detector
- Measurements of production cross sections of WZ and same-sign WW boson pairs in association with two jets in proton-proton collisions at 13 TeV
- Measurement of the Higgs boson production rate in association with top quarks in final states with electrons, muons, and hadronically decaying tau leptons at 13 TeV
- Searches for a heavy scalar boson H decaying to a pair of 125 GeV Higgs bosons hh or for a heavy pseudoscalar boson A decaying to Zh, in the final states with h to tau tau
- Search for nonresonant Higgs boson pair production in final states with two bottom quarks and two photons in proton-proton collisions at 13 TeV
- Search for H production in the H decay channel with leptonic decays in proton-proton collisions at 13 TeV
- Study of jet quenching with Z+jet correlations in PbPb and pp collisions at sqrt(s[NN]) = 5.02 TeV
- Measurement of Bose-Einstein correlations with first CMS data
- Measurement of top quark pair production in association with a Z boson in proton-proton collisions at 13 TeV
- Unravelling via the matrix element method
- A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment
- INFERNO: Inference-Aware Neural Optimisation
- Plotting the Differences Between Data and Expectation
- Search for H production in the all-jet final state in proton-proton collisions at 13 TeV
- Semi-Supervised Anomaly Detection - Towards Model-Independent Searches of New Physics
- Accelerated Matrix Element Method with Parallel Computing
- Reducing the dependence of the neural network function to systematic uncertainties in the input space
- On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case
- MoMEMta, a modular toolkit for the Matrix Element Method at the LHC
- Using Machine Learning to Speed Up and Improve Calorimeter R&D
- Measurement of differential cross sections for Z bosons produced in association with charm jets in pp collisions at 13 TeV