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008			210826s2022 si ob 001 0 eng d
010			_a 2021033763
020			_a9789811234033 _q(ebook)
020			_a9811234035 _q(ebook)
020			_z9789811234026 _q(hbk.)
020			_z9811234027 _q(hbk.)
040			_aWSPC _beng _cWSPC
050		4	_aQC793.2 _b.A78 2022
072		7	_aSCI _x051000 _2bisacsh
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082	0	0	_a539.7/6028563 _223
049			_aMAIN
245	0	0	_aArtificial intelligence for high energy physics _h[electronic resource] / _ceditors, Paolo Calafiura, David Rousseau, Kazuhiro Terao.
260			_aSingapore : _bWorld Scientific, _c2022.
300			_a1 online resource (828 p.)
504			_aIncludes bibliographical references and index.
505	0		_aIntroduction -- 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"-- _cPublisher's website.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat Reader.
650		0	_aParticles (Nuclear physics) _910887
650		0	_aArtificial intelligence. _93407
655		0	_aElectronic books. _93294
700	1		_aCalafiura, Paolo. _9178480
700	1		_aRousseau, David _c(Physics) _9178481
700	1		_aTerao, Kazuhiro. _9178482
856	4	0	_uhttps://www.worldscientific.com/worldscibooks/10.1142/12200#t=toc _zAccess to full text is restricted to subscribers.
942			_cEBK
999			_c97794 _d97794