An attempt to improve the constraint limits on aTGC’s from the highly boosted Z boson pair production at the LHC using machine learning

Aleesha, KT (2022) An attempt to improve the constraint limits on aTGC’s from the highly boosted Z boson pair production at the LHC using machine learning. Masters thesis, Indian Institute of Science Education and Research Kolkata.

Text (MS dissertation of Aleesha KT (17MS206)) 17MS206_Thesis_file.pdf - Submitted Version Restricted to Repository staff only Download (42MB)

Abstract

For diboson production of Z, tri-linear interactions between neutral gauge bosons like ZZZ and ZZɣ are forbidden at the tree level in the Standard Model. However, in the BSM scenarios the Lagrangian can contain terms corresponding to nTGC’s known as anomalous triple gauge couplings in the EFT framework. For the signal process, p p → Z Z → l⁺₁ l⁻₁ l⁺₂ l⁻₂, where l₁, l₂ stands for electrons(e) and muons(μ) anomalous events can be observed in the region M4l > 1 TeV . Owing to the large Lorentz boost factor of the Z boson , the decay products are highly collimated, resulting in small angular separation between the same-flavor oppositely signed di-leptons(OSD). Simulated e⁺e⁻ → ZZ → e⁺e⁻e⁺e⁻ events are used in this study by passing generator level output through GEANT4 implementation of a simplified CMS detector geometry. The prospect of resolving the closely produced electrons by exclusively utilizing the infromation from ECAL in the CMS is examined as the events coming from aTGC contributions has a clean signature when ΔR(l⁺l⁻) < 0.2 . The energy deposits in the calorimeter are encoded in a 2D image in the Φ - η plane. The 2D images generated via OpenCMSG4 is validated by comparing the images obtained from the CMS Open Data. A peak detection algorithm is developed to resolve the nearly collinear di-leptons. The energy of the leptons is reconstructed by employing the expectationmaximization algorithm.

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