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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. |