Results 111 to 120 of about 41,775 (139)

Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC

The European Physical Journal Special Topics
The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable growth and innovation in the past decade.
S. Mondal, L. Mastrolorenzo
semanticscholar   +1 more source

Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics

International Conference on Machine Learning
This study introduces a novel transformer model optimized for large-scale point cloud processing in scientific domains such as high-energy physics (HEP) and astrophysics.
Siqi Miao, Zhiyuan Lu, Mia Liu, Javier Duarte, Pan Li   +4 more
semanticscholar   +1 more source

Xiwu: A Basis Flexible and Learnable LLM for High Energy Physics

arXiv.org
Large Language Models (LLMs) are undergoing a period of rapid updates and changes, with state-of-the-art (SOTA) model frequently being replaced. When applying LLMs to a specific scientific field, it's challenging to acquire unique domain knowledge while ...
Zhengde Zhang, Yiyu Zhang, Haodong Yao, Jianwen Luo, Rui Zhao, Bo Huang, Jiameng Zhao, Yipu Liao, Ke Li, Lina Zhao, Jun Cao, Fazhi Qi, Changzheng Yuan   +12 more
semanticscholar   +1 more source

Image and point-cloud classification for jet analysis in high-energy physics: A survey

Frontiers of Physics
Nowadays, there has been a growing trend in the field of high-energy physics (HEP), in both its experimental and phenomenological studies, to incorporate machine learning (ML) and its specialized branch, deep learning (DL).
Hamza Kheddar, Yassine Himeur, Abbes Amira, R. Soualah   +3 more
semanticscholar   +1 more source

HEP ML LAB: An end-to-end framework for applying machine learning to phenomenology studies

Chinese Physics C, High Energy Physics & Nuclear Physics
Recent years have seen the development and growth of machine learning in high-energy physics. However, additional effort is required to continue exploring the use of machine learning to its full potential.
Jing 靖 Li 李, H. Sun 孙
semanticscholar   +1 more source

CelloAI: Leveraging Large Language Models for HPC Software Development in High Energy Physics

arXiv.org
Next-generation High Energy Physics (HEP) experiments will generate unprecedented data volumes, necessitating High Performance Computing (HPC) integration alongside traditional high-throughput computing.
Mohammad Atif, Kriti Chopra, Ozgur Kilic, Tianle Wang, Zhihua Dong, Charles Leggett, Meifeng Lin, P. Calafiura, Salman Habib   +8 more
semanticscholar   +1 more source

Application of high energy physics detector description transformation for visualization in Unity

Proceedings of 42nd International Conference on High Energy Physics — PoS(ICHEP2024)
Visualization is integral to high-energy physics (HEP) experiments, spanning from detector design to data analysis. Presently, depicting detectors within HEP is an intricate challenge.
Tianzi Song, Yumei Zhang, Zhengyun You
semanticscholar   +1 more source

Proceedings of the XXV DAE-BRNS High Energy Physics (HEP) Symposium 2022, 12–16 December, Mohali, India

Springer Proceedings in Physics

semanticscholar   +1 more source

Treatment of muscle‐invasive and advanced bladder cancer in 2020

Ca-A Cancer Journal for Clinicians, 2020
Vaibhav G Patel, William K Oh, Matthew D Galsky   +2 more
exaly  

Molecular imaging in oncology: Current impact and future directions

Ca-A Cancer Journal for Clinicians, 2022
Steven P Rowe, Martin G Pomper
exaly