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Dong, Zhongtian

Publications and source records attributed to Dong, Zhongtian.

Probing the CP structure of the top quark Yukawa at the future muon collider

We study the top-Higgs coupling with a CP violating phase ξ at a future multi-TeV muon collider. We focus on processes that are directly sensitive to the top quark Yukawa coupling: $t$$\overline{t}$$h$, $tbhμν$, and $t$$\overline{t}$$h$$v$$\overline{v}$ with $h$ → $b$$\overline{b}$ and semileptonic top decays. At different energies, different processes dominate the cross section, providing complementary information. At and above an energy of $\mathcal{O}$(10) TeV, vector boson fusion processes dominate. As we show, in the Standard Model there is destructive interference in the vector boson fusion processes $t$$\overline{t}$$h$$v$$\overline{v}$ and $tbhμν$ between the top quark Yukawa and Higgs-gauge boson couplings. A CP-violating phase changes this interference, and the cross section measurement is very sensitive to the size of the CP-violating angle. Although we find that the cross sections are measured to $\mathcal{O}$(50%) statistical uncertainty at 1σ, a 10 and 30 TeV muon collider can bound the CP-violating angle |ξ| ≲ 9.0° and |ξ| ≲ 5.4°, respectively. However, cross section measurements are insensitive to the sign of the CP-violating angle. To determine that the coupling is truly CP violating, observables sensitive to CP-violation must be measured. We find in the $t$$\overline{t}$$h$ process the azimuthal angle between the $t$ + $\overline{t}$ plane and the initial state muon+Higgs plane shows good discrimination for ξ = ±0.1π.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗