148 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Seoul National UniversityKR34 papers
- Sungkyunkwan UniversityKR17 papers
- Yonsei UniversityKR16 papers
- Korea UniversityKR15 papers
- Korea Institute for Advanced StudyKR14 papers
- University of PittsburghUS14 papers
- High Energy Accelerator Research OrganizationJP11 papers
- Kyungpook National UniversityKR11 papers
- Institute for Basic ScienceKR10 papers
- New Generation University CollegeET10 papers
- Tohoku UniversityJP10 papers
- Chonnam National UniversityKR9 papers
10 papers · 2 filters
Dark photon and dark mediated meson decays
Alakabha Datta, A. Hammad, Danny Marfatia +2
We study flavor changing neutral current decays of and mesons in the dark model, with the dark photon/dark mass between 10 MeV and 2 GeV. Although the model pr…
Disentangling the high and low cutoff scales via the trilinear Higgs couplings in the type-I two-Higgs-doublet model
Sin Kyu Kang, Jinheung Kim, Soojin Lee +1
The type-I two-Higgs-doublet model in the inverted Higgs scenario can retain the theoretical stability all the way up to the Planck scale. The Planck-cutoff scale, $Λ_{\rm cut}^{\r…
Riemannian Data preprocessing in Machine Learning to focus on QCD color structure
A. Hammad, Myeonghun Park
Identifying the quantum chromodynamics (QCD) color structure of processes provides additional information to enhance the reach for new physics searches at the Large Hadron Collider…
Search for Mono-Higgs Signals in Final States Using Deep Neural Networks
A. Hammad, S. Khalil, S. Moretti
We study mono-Higgs signatures emerging in an illustrative new physics scenario involving Standard Model Higgs boson decays to bottom quark pairs using Hybrid Deep Neural Networks.…
Exploration of Parameter Spaces Assisted by Machine Learning
A. Hammad, Myeonghun Park, Raymundo Ramos +1
We demonstrate two sampling procedures assisted by machine learning models via regression and classification. The main objective is the use of a neural network to suggest points li…
N-jettiness for muon jet pairs in electroweak high-energy processes
Junegone Chay, Taehyun Kwon
We study the -jettiness in the electroweak high-energy process for the final muon jet pairs, . Compared to QCD, the ma…
Kinematic Variables and Feature Engineering for Particle Phenomenology
Roberto Franceschini, Doojin Kim, Kyoungchul Kong +3
Kinematic variables have been playing an important role in collider phenomenology, as they expedite discoveries of new particles by separating signal events from unwanted backgroun…
Confronting the prediction of leptonic Dirac CP-violating phase with experiments
Yang Hwan Ahn, Sin Kyu Kang, Raymundo Ramos +1
We update and improve past efforts to predict the leptonic Dirac CP-violating phase with models that predict perturbatively modified tribimaximal or bimaximal mixing. Simple pertur…
Implications of the new CDF-II -boson mass on two-Higgs-doublet models
Yang Hwan Ahn, Sin Kyu Kang, Raymundo Ramos
We present the implications of the recent measurement of boson at CDF II on the two-Higgs-doublet model (2HDM). In the analysis, we impose theoretical bounds such as vacuum sta…
Detectable Gravitational Waves from preheating probes non-thermal Dark Matter
Anish Ghoshal, Pankaj Saha
We describe the challenges and pathways when probing inflaton as dark matter with the stochastic gravitational waves (GWs) signal generated during the (p)reheating. Such scenarios…