activity
20202026
most citedAn Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging

121 citations · 232 across the 18 of their papers we have counts for

collaborators

19 papers

hep-ex2026

Building an AI-native Research Ecosystem for Experimental Particle Physics: A Community Vision

Thea Klaeboe Aarrestad, Alaa Abdelhamid, Haider Abidi +457

Experimental particle physics seeks to understand the universe by probing its fundamental particles and forces and exploring how they govern the large-scale processes that shape co…

hep-ph2025

Quantum Entanglement between gauge boson pairs at a Muon Collider

Ran Ding, Alim Ruzi, Sitian Qian +3

Quantum entanglement is one of significant physics phenomena that can be examined at a particle collider. A muon collider can provide a stage on which we can study substantial phys…

hep-ph2024

Testing Bell inequalities and probing quantum entanglement at a muon collider

Alim Ruzi, Youpeng Wu, Ran Ding +3

A muon collider represents a promising candidate for the next generation of particle physics experiments after the expected end of LHC operations in the early 2040s. Rare or hard-t…

hep-ph2023★ 2 cited

Searching for Majorana Neutrinos at a Same-Sign Muon Collider

Ruobing Jiang, Tianyi Yang, Sitian Qian +4

Majorana properties of neutrinos have long been a focus in the pursuit of possible new physics beyond the standard model, which has motivated lots of dedicated theoretical and expe…

hep-ph2023★ 7 cited

A Comparative Study of Z mediated Charged Lepton Flavor Violation at future lepton colliders

Jingshu Li, Wanyue Wang, Xunye Cai +5

Charged lepton flavor violation (CLFV) represents a transition between charged leptons of different generations that violates lepton flavor conservation, which is a clear signature…

hep-ph2023

Low Energy Neutrino and Mass Dark Matter Detection Using Freely Falling Atoms

Alim Ruzi, Sitian Qian, Tianyi Yang +1

We propose a new method to detect low-energy neutrinos and low-mass dark matter at or below the MeV scale, through their coherent scatterings from freely falling heavy atoms and th…

hep-ph2023

Muon Beam for Neutrino CP Violation: connecting energy and neutrino frontiers

Alim Ruzi, Tianyi Yang, Dawei Fu +3

We propose here a proposal to connect neutrino and energy frontiers, by exploiting collimated muon beams for neutrino oscillations, which generate symmetric neutrino and antineutri…

hep-ex2022★ 8 cited

Application of Quantum Machine Learning in a Higgs Physics Study at the CEPC

Abdualazem Fadol, Qiyu Sha, Yaquan Fang +5

Machine learning has blossomed in recent decades and has become essential in many fields. It significantly solved some problems in particle physics -- particle reconstruction, even…

hep-ph2022★ 11 cited

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…

hep-ph2022

The physics case for neutrino-neutrino collisions

Sitian Qian, Tianyi Yang, Sen Deng +6

Addressing the mass origin and properties of neutrinos is of strong interest to particle physics, baryogenesis and cosmology. Popular explanations involve physics beyond the standa…

hep-ph2022★ 3 cited

The physics case for a neutrino lepton collider in light of the CDF W mass measurement

Tianyi Yang, Sitian Qian, Sen Deng +6

We propose a neutrino lepton collider where the neutrino beam is generated from TeV scale muon decays. Such a device would allow for a precise measurement of the W mass based on si…

hep-ph2022★ 47 cited

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…

hep-ph2022★ 121 cited

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…

hep-ph2022

Boosted tau lepton as a microscope and macroscope

Sitian Qian, Zhe Guan, Sen Deng +9

Anomalies from the LHCb lepton flavour universality and Fermilab muon anomalous magnetic momentum, show tantalizing hints of possible new physics from the lepton sectors. Due to it…

hep-ph2021★ 22 cited

Searching for heavy leptoquarks at a muon collider

Sitian Qian, Congqiao Li, Qiang Li +5

The LHCb Collaboration recently gave an update on testing lepton flavour universality with , in which a 3.1 standard deviations from the standard…

hep-ph2021

Animating collider processes with Event-time-frame Format

Leyun Gao, Jing Peng, Zilin Dai +4

High Energy Physics processes, such as hard scattering, parton shower, and hadronization, occur at colliders around the world, e.g., the Large Hadron Collider in Europe. The variou…

hep-ph2021★ 5 cited

Longitudinally polarized ZZ scattering at the Muon Collider

Tianyi Yang, Sitian Qian, Zhe Guan +5

Measuring longitudinally polarized vector boson scattering in, e.g., the ZZ channel is a promising way to investigate the unitarization scheme from the Higgs and possible new physi…

hep-ph2020★ 1 cited

The physics case for an electron-muon collider

Meng Lu, Andrew Michael Levin, Congqiao Li +6

An electron-muon collider with an asymmetric collision profile targeting multi-ab integrated luminosity is proposed. This novel collider, operating at collisions energies of…

physics.comp-ph2020★ 5 cited

Schrödinger PCA: On the Duality between Principal Component Analysis and Schrödinger Equation

Ziming Liu, Sitian Qian, Yixuan Wang +2

Principal component analysis (PCA) has achieved great success in unsupervised learning by identifying covariance correlations among features. If the data collection fails to captur…