紀錄類型: |
書目-電子資源
: Monograph/item
|
正題名/作者: |
Artificial intelligence for high energy physics/ editors, Paolo Calafiura, David Rousseau, Kazuhiro Terao. |
其他作者: |
Calafiura, Paolo. |
出版者: |
Singapore :World Scientific, : c2022., |
面頁冊數: |
1 online resource (828 p.) |
內容註: |
Introduction -- Part I: Discriminative models for signal/background boosting -- Boosted decision trees -- Deep learning from four vectors -- Anomaly detection for physics analysis and less than supervised learning -- Part II: Data quality monitoring -- Data quality monitoring anomaly detection -- Part III: Generative models -- Generative models for fast simulation -- Generative networks for LHC events -- Part IV: Machine learning platforms -- Distributed training and optimization of neural networks -- Machine learning for triggering and data acquisition -- Part V: Detector data reconstruction -- End-to-end analyses using image classification -- Clustering -- Graph neural networks for particle tracking and reconstruction -- Part VI: Jet classification and particle identification from low level -- Image-based jet analysis -- Particle identification in neutrino detectors -- Sequence-based learning -- Part VII: Physics inference -- Simulation-based inference methods for particle physics -- Dealing with nuisance parameters -- Bayesian neural networks -- Parton distribution functions -- Part VIII: Scientific competitions and open datasets -- Machine learning scientific competitions and datasets. |
標題: |
Particles (Nuclear physics) - |
電子資源: |
https://www.worldscientific.com/worldscibooks/10.1142/12200#t=toc |
ISBN: |
9789811234033 |