477 citations · 2.9k across the 68 of their papers we have counts for
66 papers · 1 filter
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…
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…
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…
Leveraging Quantum Annealer to identify an Event-topology at High Energy Colliders
Minho Kim, Pyungwon Ko, Jae-hyeon Park +1
With increasing energy and luminosity available at the Large Hadron collider (LHC), we get a chance to take a pure bottom-up approach solely based on data. This will extend the sco…
Deep-Learned Event Variables for Collider Phenomenology
Doojin Kim, Kyoungchul Kong, Konstantin T. Matchev +2
The choice of optimal event variables is crucial for achieving the maximal sensitivity of experimental analyses. Over time, physicists have derived suitable kinematic variables for…
Neutrino mass and with dark symmetry
Arnab Dasgupta, Sin Kyu Kang, Myeonghun Park
We propose an extension of the Standard Model (SM) for radiative neutrino mass by introducing a dark gauge symmetry. The kinetic mixing between the SM gauges and the dark…