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Artificial intelligence for high energy physics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Artificial intelligence for high energy physicseditors, Paolo Calafiura, David Rousseau, Kazuhiro Terao.
other author:	Calafiura, Paolo.
Published:	Singapore :World Scientific,c2022.
Description:	1 online resource (828 p.)
Subject:	Particles (Nuclear physics)
Online resource:	https://www.worldscientific.com/worldscibooks/10.1142/12200#t=toc
ISBN:	9789811234033$q(ebook)

Artificial intelligence for high energy physics
Artificial intelligence for high energy physics[electronic resource] /editors, Paolo Calafiura, David Rousseau, Kazuhiro Terao. - Singapore :World Scientific,c2022. - 1 online resource (828 p.)

Includes bibliographical references and index.

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.

"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"--

Mode of access: World Wide Web.

ISBN: 9789811234033$q(ebook)

LCCN: 2021033763Subjects--Topical Terms:

204127
Particles (Nuclear physics)

LC Class. No.: QC793.2 / .A78 2022

Dewey Class. No.: 539.7/6028563

Artificial intelligence for high energy physics
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245 0 0 $a Artificial intelligence for high energy physics $h [electronic resource] / $c editors, Paolo Calafiura, David Rousseau, Kazuhiro Terao.
260 $a Singapore : $b World Scientific, $c c2022.
300 $a 1 online resource (828 p.)
504 $a Includes bibliographical references and index.
505 0 $a 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 $a "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"-- $c Publisher's website.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Description based on print version record.
650 0 $a Particles (Nuclear physics) $3 204127
650 0 $a Artificial intelligence. $3 194058
700 1 $a Calafiura, Paolo. $3 960424
700 1 $a Rousseau, David $c (Physics) $3 960425
700 1 $a Terao, Kazuhiro. $3 960426
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/12200#t=toc