guest ::
| Home > Publications database > Tau identification algorithms and study of the CP structure of the Yukawa coupling between the Higgs boson and tau leptons in CMS |
| Home > Publications database > Tau identification algorithms and study of the CP structure of the Yukawa coupling between the Higgs boson and tau leptons in CMS |
| Book/Dissertation / PhD Thesis | PUBDB-2023-01292 |
; ;
2023
Verlag Deutsches Elektronen-Synchrotron DESY
Hamburg
This record in other databases:
Please use a persistent id in citations: doi:10.3204/PUBDB-2023-01292
Report No.: DESY-THESIS-2023-005
Abstract: The measurement of the CP properties of the Yukawa coupling of the Higgs boson to τleptons is presented. The data set used for the analysis is collected by the CMS experiment atthe LHC during the Run 2 data-taking period in proton-proton collisions at √s = 13 TeV andcorresponds to an integrated luminosity of 137 fb$^{−1}$. The Yukawa coupling between the Higgsboson and $τ$ leptons is parametrised in terms of the effective mixing angle α$^{Hττ}$, where the valueα$^{Hττ}$ = 0◦(90◦) corresponds to the SM scenario of the pure CP-even (CP-odd) H$ττ$ coupling.The angle between the decay planes of the $τ$ leptons is used as the observable encoding the CP nature of the Higgs boson. The measurement is performed in the $τ$$_e$$τ$$_h$ channel where oneτ lepton decays into an electron and the other hadronically. The results are combined with themeasurement in the $τ$$_q$$τ$$_h$ and $τ$$_h$$τ$$_h$ channels. The observed (expected) value of the effectivemixing angle for the combination is measured to be: α$^{Hττ}$ = −1 ± 19◦(0 ± 21◦) @68.3% CL.The results are compatible with the SM expectation and the pure CP-odd hypothesis isrejected at the observed (expected) significance level of 3.0 (2.6) standard deviations.The improvements to the $τ$ lepton identification in CMS in the context of the Run 3 preparationare described. Retraining and optimisation of the DeepTau algorithm with the addition ofthe adversarial fine-tuning procedure are performed. The resulting model improves upon theprevious DeepTau model in terms of the background rejection by 10-50% and has a betterdescription of data with simulation in the H → $ττ$ selection region.A new algorithm called Tau Transformer (TaT) is proposed to overcome the limitations of theDeepTau architecture. The TaT core is based on self-attention layers and features the embeddingmodule allowing for the multimodality treatment of the input representation. Comparison of theTaT model with the retrained DeepTau model and a comparable ParticleNet-based architectureshows consistently improved performance by up to 50% in the misidentification rate across the $p\tiny{T}$, $η$, and decay mode ranges of interest.
|
The record appears in these collections: |
PDF
PDF (PDFA)