424 citations · 912 across the 15 of their papers we have counts for
17 papers
LDMX -- The Light Dark Matter eXperiment
Torsten Akesson, Layan Alsaraya, Stephen Appert +116
The Light Dark Matter eXperiment (LDMX) is an electron fixed-target experiment optimized to search for sub-GeV dark matter production through the missing momentum signature. LDMX i…
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…
Timing Performance of the CMS High Granularity Calorimeter Prototype
CMS HGCAL collaboration
This paper describes the experience with the calibration, reconstruction and evaluation of the timing capabilities of the CMS HGCAL prototype in the beam tests in 2018. The calibra…
Jet-Origin Identification and Its Application at an Electron-Positron Higgs Factory
Hao Liang, Yongfeng Zhu, Yuexin Wang +4
To enhance the scientific discovery power of high-energy collider experiments, we propose and realize the concept of jet origin identification that categorizes jets into 5 quark sp…
ParticleNet and its application on CEPC Jet Flavor Tagging
Yongfeng Zhu, Hao Liang, Yuexin Wang +3
Identification of quark flavor is essential for collider experiments in high-energy physics, relying on the flavor tagging algorithm. In this study, using a full simulation of the…
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…
Construction and commissioning of CMS CE prototype silicon modules
B. Acar, G. Adamov, C. Adloff +327
As part of its HL-LHC upgrade program, the CMS Collaboration is developing a High Granularity Calorimeter (CE) to replace the existing endcap calorimeters. The CE is a sampling cal…
The DAQ system of the 12,000 Channel CMS High Granularity Calorimeter Prototype
B. Acar, G. Adamov, C. Adloff +327
The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant wit…
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…