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CERN Accelerating science

Thesis
Report number CERN-THESIS-2023-237
Title Neural Networks for Mass Regression for HH $\rightarrow$ bb$\tau\tau$
Author(s) Lin, Storm Feng (Stony Brook U.)
Publication 40.
Thesis note Master : Stony Brook U. : 2023
Thesis supervisor(s) Piacquadio, Giacinto ; Buat, Quentin
Note Presented 08 May 2023
Subject category Detectors and Experimental Techniques
Accelerator/Facility, Experiment CERN LHC ; ATLAS
Abstract The value of the Higgs boson self-coupling is predicted by the Standard Model, but it is still largely unconstrained experimentally and thus being actively studied at the ATLAS experiment at the LHC. The HH $\rightarrow$ bb$\tau\tau$ decay channel is one of the most sensitive probes for studying this property. Regressing the di-Higgs invariant mass ($m_{HH}$) for events in this channel and understanding its distribution are important to improving the determination of the Higgs self-coupling. This thesis discusses recurrent, dense, and mixture density neural network models for predicting $m_{HH}$ from other event variables. These new models demonstrate significant improvement in both accuracy of mHH predictions and ability to separate $m_{HH}$ distributions for different values of the self-coupling constant when compared to the currently adopted Missing Mass Calculator method for $m_{HH}$ estimation in this channel.

Email contact: giacinto.piacquadio@cern.ch

 Record created 2023-11-10, last modified 2023-12-08


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