424 citations · 912 across the 15 of their papers we have counts for
11 papers · 1 filter
Les Houches 2023: Physics at TeV Colliders: Standard Model Working Group Report
J. Andersen, B. Assi, K. Asteriadis +81
This report presents a short summary of the activities of the "Standard Model" working group for the "Physics at TeV Colliders" workshop (Les Houches, France, 12-30 June, 2023).
Accelerating Resonance Searches via Signature-Oriented Pre-training
Congqiao Li, Antonios Agapitos, Jovin Drews +9
The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging signifi…
Optimal transport for a novel event description at hadron colliders
Loukas Gouskos, Fabio Iemmi, Sascha Liechti +3
We propose a novel strategy for disentangling proton collisions at hadron colliders such as the LHC that considerably improves over the current state of the art. Employing a metric…
Does Lorentz-symmetric design boost network performance in jet physics?
Congqiao Li, Huilin Qu, Sitian Qian +5
In the deep learning era, improving the neural network performance in jet physics is a rewarding task as it directly contributes to more accurate physics measurements at the LHC. R…
Particle Transformer for Jet Tagging
Huilin Qu, Congqiao Li, Sitian Qian
Jet tagging is a critical yet challenging classification task in particle physics. While deep learning has transformed jet tagging and significantly improved performance, the lack…
An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
Shiqi Gong, Qi Meng, Jue Zhang +6
Deep learning methods have been increasingly adopted to study jets in particle physics. Since symmetry-preserving behavior has been shown to be an important factor for improving th…
Jet tagging in the Lund plane with graph networks
Frédéric A. Dreyer, Huilin Qu
The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider. In this a…
The Machine Learning Landscape of Top Taggers
G. Kasieczka, T. Plehn, A. Butter +24
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established metho…
ParticleNet: Jet Tagging via Particle Clouds
Huilin Qu, Loukas Gouskos
How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set…
Probing Triple-W Production and Anomalous WWWW Coupling at the CERN LHC and future 100TeV proton-proton collider
Yiwen Wen, Huilin Qu, Daneng Yang +3
Triple gauge boson production at the LHC can be used to test the robustness of the Standard Model and provide useful information for VBF di-boson scattering measurement. Especially…
New mixing pattern for neutrinos
Huilin Qu, Bo-Qiang Ma
We propose a new mixing pattern for neutrinos with a nonzero mixing angle . Under a simple form, it agrees well with current neutrino oscillation data and displays a number…