A5105511988- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Quantum Chromodynamics and Particle Interactions
- Particle Detector Development and Performance
- Computational Physics and Python Applications
- Dark Matter and Cosmic Phenomena
- Cosmology and Gravitation Theories
- Neutrino Physics Research
- Nuclear reactor physics and engineering
- Medical Imaging Techniques and Applications
- Astrophysics and Cosmic Phenomena
- Nuclear physics research studies
- Radiation Detection and Scintillator Technologies
- Distributed and Parallel Computing Systems
- Black Holes and Theoretical Physics
- Gamma-ray bursts and supernovae
- International Science and Diplomacy
- Atomic and Subatomic Physics Research
- Topological and Geometric Data Analysis
- Superconducting Materials and Applications
- Optical properties and cooling technologies in crystalline materials
- Scientific Computing and Data Management
- Stochastic processes and financial applications
- Noncommutative and Quantum Gravity Theories
- Muon and positron interactions and applications
European Organization for Nuclear Research
2020-2025
A. Alikhanyan National Laboratory
2022-2024
University of Antwerp
2024
Georgia State University
2009-2024
Southeast University
2024
Institute of High Energy Physics
2017-2024
State Key Laboratory of Millimeter Waves
2024
University of California, Santa Barbara
2017-2023
Instituto de Física de Cantabria
2021-2023
Universidad de Cantabria
2021-2023
How to represent a jet is at the core of machine learning on physics. Inspired by notion point clouds, we propose new approach that considers as an unordered set its constituent particles, effectively ``particle cloud.'' Such particle cloud representation jets efficient in incorporating raw information and also explicitly respects permutation symmetry. Based representation, ParticleNet, customized neural network architecture using Dynamic Graph Convolutional Neural Network for tagging...
A bstract Deep learning methods have been increasingly adopted to study jets in particle physics. Since symmetry-preserving behavior has shown be an important factor for improving the performance of deep many applications, Lorentz group equivariance — a fundamental spacetime symmetry elementary particles recently incorporated into model jet tagging. However, design is computationally costly due analytic construction high-order tensors. In this article, we introduce LorentzNet, new The...
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range modern machine learning approaches. Unlike most methods they rely low-level input, for instance calorimeter output. While their network architectures are vastly different, performance is comparatively similar. In general, find that these new approaches extremely powerful and great fun.
A bstract The identification of boosted heavy particles such as top quarks or vector bosons is one the key problems arising in experimental studies at Large Hadron Collider. In this article, we introduce LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description radiation patterns within to optimally disentangle signatures objects from background events. We apply framework number different benchmarks, showing significantly improved performance for...
Jet tagging is a critical yet challenging classification task in particle physics. While deep learning has transformed jet and significantly improved performance, the lack of large-scale public dataset impedes further enhancement. In this work, we present JetClass, new comprehensive for tagging. The JetClass consists 100 M jets, about two orders magnitude larger than existing datasets. A total 10 types jets are simulated, including several unexplored so far. Based on large dataset, propose...
To enhance the scientific discovery power of high-energy collider experiments, we propose and realize concept jet-origin identification that categorizes jets into five quark species <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"><a:mo stretchy="false">(</a:mo><a:mi>b</a:mi><a:mo>,</a:mo><a:mi>c</a:mi><a:mo>,</a:mo><a:mi>s</a:mi><a:mo>,</a:mo><a:mi>u</a:mi><a:mo>,</a:mo><a:mi>d</a:mi><a:mo stretchy="false">)</a:mo></a:math>, antiquarks <e:math...
Abstract Quarks (except top quarks) and gluons produced in collider experiments hadronize fragment into sprays of stable particles, called jets. Identification quark flavor is desired for high-energy physics, relying on tagging algorithms. In this study, using a full simulation the Circular Electron Positron Collider (CEPC), we investigate performance two different algorithms: ParticleNet, based Graph Neural Network, LCFIPlus, Gradient Booted Decision Tree. Compared to ParticleNet...
In the deep learning era, improving neural network performance in jet physics is a rewarding task, as it directly contributes to more accurate measurements at LHC. Recent research has proposed various designs consideration of full Lorentz symmetry, but its benefit still not systematically asserted, given that there remain many successful networks without taking into account. We conduct detailed study on Lorentz-symmetric design. propose two generalized approaches for modifying network—these...
The diffusion model has demonstrated promising results in image generation, recently becoming mainstream and representing a notable advancement for many generative modeling tasks. Prior applications of the both fast event detector simulation high energy physics have shown exceptional performance, providing viable solution to generate sufficient statistics within constrained computational budget preparation High Luminosity LHC. However, these suffer from slow generation with large sampling...
We propose a novel strategy for disentangling proton collisions at hadron colliders such as the LHC that considerably improves over current state of art. Employing metric inspired by optimal transport problems cost function graph neural network, our algorithm is able to compare two particle collections with different noise levels and learns flag particles originating from main interaction amidst products up 200 simultaneous pileup collisions. thereby sidestep critical task obtaining ground...
Tabular data stands out as one of the most frequently encountered types in high energy physics. Unlike commonly homogeneous such pixelated images, simulating high-dimensional tabular and accurately capturing their correlations are often quite challenging, even with advanced architectures. Based on findings that tree-based models surpass performance deep learning for tasks specific to data, we adopt very recent generative modeling class named conditional flow matching employ different...
Triple gauge boson production at the LHC can be used to test robustness of Standard Model and provide useful information for VBF di-boson scattering measurement. Especially, any derivations from SM prediction will indicate possible new physics. In this paper we present a detailed Monte Carlo study on measuring W ± ∓ in pure leptonic semileptonic decays, probing anomalous quartic WWWW couplings CERN future hadron collider, with parton shower detector simulation effects taken into account....
We propose a new mixing pattern for neutrinos with nonzero angle ${\ensuremath{\theta}}_{13}$. Under simple form, it agrees well current neutrino oscillation data and displays number of intriguing features including the $\ensuremath{\mu}\mathrm{\text{\ensuremath{-}}}\ensuremath{\tau}$ interchange symmetry $|{U}_{\ensuremath{\mu}i}|=|{U}_{\ensuremath{\tau}i}|$, ($i=1$, 2, 3), trimaximal $|{U}_{\mathrm{e}2}|=|{U}_{\ensuremath{\mu}2}|=|{U}_{\ensuremath{\tau}2}|=1/\sqrt{3}$, self-complementarity...
In the deep learning era, improving neural network performance in jet physics is a rewarding task as it directly contributes to more accurate measurements at LHC. Recent research has proposed various designs consideration of full Lorentz symmetry, but its benefit still not systematically asserted, given that there remain many successful networks without taking into account. We conduct detailed study on Lorentz-symmetric design. propose two generalized approaches for modifying - these methods...
We propose one-to-one correspondence reconstruction for electron-positron Higgs factories. For each visible particle, aims to associate relevant detector hits with only one reconstructed particle and accurately identify its species. To achieve this goal, we develop a novel concept featuring 5-dimensional calorimetry that provides spatial, energy, time measurements hit, framework combines state-of-the-art flow artificial intelligence algorithms. In the benchmark process of di-jets, over 90%...
Abstract The demand for wearable monitoring devices in contemporary medicine has significantly increased, especially dynamic environments where traditional bulky equipment is impractical. Conventional flexible or systems suffer from limited air and moisture permeability, lack of stretchability, high power consumption, which restrict their long‐term usage comfort. Herein, a stretchable breathable backscattered system (SBBMS) introduced, integrated with fabric substrate. To address the...
The identification of boosted heavy particles such as top quarks or vector bosons is one the key problems arising in experimental studies at Large Hadron Collider. In this article, we introduce LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description radiation patterns within to optimally disentangle signatures objects from background events. We apply framework number different benchmarks, showing significantly improved performance for compared...
Cardiac troponin I-interacting kinase (TNNI3K) is a cardiac-specific that has been identified as diagnostic marker and therapeutic target in cardiovascular diseases. However, the biological function of TNNI3K cardiac dysfunction remodelling remains elusive. In present study, Tnni3k cardiomyocyte-specific knockout (Tnni3k-cKO) mouse model was established. Echocardiography used to evaluate mice. Heart failure markers were detected using enzyme-linked immunosorbent assay. Haematoxylin eosin...
To solve the expression difficulties of basic principle asynchronous motor, and faults to understand relevant knowledge not fully, motor intuitive express experiment device was designed by analyzing existing mechanical structures motor.The three-dimensional modeling finished for structure device.The experimental is mainly divided into base part design, speed display design rotating demonstration design.Through can verify demonstrate rotation thereby facilitating scholars understand.
Identification of quark flavor is essential for collider experiments in high-energy physics, relying on the tagging algorithm. In this study, using a full simulation Circular Electron Positron Collider (CEPC), we investigated performance two different algorithms: ParticleNet, originally developed at CMS, and LCFIPlus, current algorithm employed CEPC. Compared to ParticleNet significantly enhances performance, resulting significant improvement benchmark measurement accuracy, i.e., 36%...