477 citations · 2.9k across the 68 of their papers we have counts for
68 papers
Machine Learning on Heterogeneous, Edge, and Quantum Hardware for Particle Physics (ML-HEQUPP)
Julia Gonski, Jenni Ott, Shiva Abbaszadeh +117
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environmen…
Primordial cosmic complexity and effects of reheating
Pankaj Saha, Myeonghun Park
We study the effects of the reheating phase on the evolution of complexities for the primordial curvature perturbation using the squeezed formalism. We examine the evolution of the…
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…