fwXmachina example: Anomaly detection

Published: 11 April 2023| Version 1 | DOI: 10.17632/y698s5kscs.1
Contributors:
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Description

This dataset provides a ROOT file with three trees: - "background" includes events of all SM processes with final state e+ e- mu+ mu-, dominated by ZZ* - "h2e2mu-1" includes events from the BSM decay H-->a1 a2, for Higgs mass of 70 GeV and Higgs-like scalars a1 [25 GeV] and a2 [30 GeV]. These further decay like a1 --> e+ e-, a2 --> mu+ mu- - "h2e2mu-2" includes events from the BSM decay H-->a1 a2, for Higgs mass of 125 GeV and Higgs-like scalars a1 [10 GeV] and a2 [15 GeV]. These further decay like a1 --> e+ e-, a2 --> mu+ mu- Each tree contains the same variables: pT, eta, and phi for both electrons and both muons, and the m_ee, m_mumu, and m_eemumu invariant masses. Only events with exactly two reconstructed muons and two reconstructed electrons were included. All events were produced with MadGraph. BSM decays and hadronization were performed with Pythia8 with the ATLAS AZ 17 tune. Showering, event reconstruction, and detector simulation was performed with Delphes. Pseudorapidity/phi coordinates were used with eta between -4.9 --> 4.9 and phi between -pi --> pi. Minimum bias pileup with <mu> = 50 was applied using the CMS with pileup card in Delphes. All Higgs production is via the gluon-gluon fusion (ggF) channel. In MadGraph, the Higgs effective field theory (HEFT) model is used for all signal and background production.

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Institutions

Westmont College, University of Pittsburgh, Saint Louis University

Categories

Physics, Particle Physics, Machine Learning

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