25 citations · 57 across the 4 of their papers we have counts for
4 papers
Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models
Shikai Qiu, Shuo Han, Xiangyang Ju +2
Parton labeling methods are widely used when reconstructing collider events with top quarks or other massive particles. State-of-the-art techniques are based on machine learning an…
Generative Machine Learning for Detector Response Modeling with a Conditional Normalizing Flow
Allison Xu, Shuo Han, Xiangyang Ju +1
In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the L…
A Holistic Approach to Predicting Top Quark Kinematic Properties with the Covariant Particle Transformer
Shikai Qiu, Shuo Han, Xiangyang Ju +2
Precise reconstruction of top quark properties is a challenging task at the Large Hadron Collider due to combinatorial backgrounds and missing information. We introduce a physics-i…
Higgs-flavon mixing and LHC phenomenology in a simplified model of broken flavor symmetry
Edmond L. Berger, Steven B. Giddings, Haichen Wang +1
The LHC phenomenology of a low-scale gauged flavor symmetry model with inverted hierarchy is studied, through introduction of a simplified model of broken flavor symmetry. A new sc…