Artificial intelligence for high energy physics (Record no. 97794)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 03560nam a2200433 a 4500 |
| 001 - CONTROL NUMBER | |
| control field | 00012200 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240731095218.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210826s2022 si ob 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9789811234033 |
| -- | (ebook) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9811234035 |
| -- | (ebook) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | (hbk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | (hbk.) |
| 082 00 - CLASSIFICATION NUMBER | |
| Call Number | 539.7/6028563 |
| 245 00 - TITLE STATEMENT | |
| Title | Artificial intelligence for high energy physics |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Singapore : |
| Publisher | World Scientific, |
| Year of publication | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 online resource (828 p.) |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | "The Higgs boson discovery at the Large Hadron Collider in 2012 relied on boosted decision trees. Since then, high energy physics (HEP) has applied modern machine learning (ML) techniques to all stages of the data analysis pipeline, from raw data processing to statistical analysis. The unique requirements of HEP data analysis, the availability of high-quality simulators, the complexity of the data structures (which rarely are image-like), the control of uncertainties expected from scientific measurements, and the exabyte-scale datasets require the development of HEP-specific ML techniques. While these developments proceed at full speed along many paths, the nineteen reviews in this book offer aself-contained, pedagogical introduction to ML models' real-life applications in HEP, written by some of the foremost experts in their area"-- |
| 700 1# - AUTHOR 2 | |
| Author 2 | Calafiura, Paolo. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Rousseau, David |
| 700 1# - AUTHOR 2 | |
| Author 2 | Terao, Kazuhiro. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://www.worldscientific.com/worldscibooks/10.1142/12200#t=toc |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 520 ## - SUMMARY, ETC. | |
| -- | Publisher's website. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Particles (Nuclear physics) |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
No items available.