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010   $a3-319-98249-4
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035   $a(DE-He213)978-3-319-98249-6
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035   $a(EXLCZ)994100000007335070
100   $a20181229d2018      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMachine Learning at the Belle II Experiment $eThe Full Event Interpretation and Its Validation on Belle Data /$fby Thomas Keck
205   $a1st ed. 2018.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018.
215   $a1 online resource (174 pages)
225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
300   $a"Doctoral thesis accepted by the Karlsruhe Institute of Technology, Karlsruhe, Germany."
311   $a3-319-98248-6 
327   $aIntroduction -- From Belle to Belle II -- Multivariate Analysis Algorithms -- Full Event Interpretation -- B tau mu -- Conclusion.
330   $aThis book explores how machine learning can be used to improve the efficiency of expensive fundamental science experiments. The first part introduces the Belle and Belle II experiments, providing a detailed description of the Belle to Belle II data conversion tool, currently used by many analysts. The second part covers machine learning in high-energy physics, discussing the Belle II machine learning infrastructure and selected algorithms in detail. Furthermore, it examines several machine learning techniques that can be used to control and reduce systematic uncertainties. The third part investigates the important exclusive B tagging technique, unique to physics experiments operating at the ? resonances, and studies in-depth the novel Full Event Interpretation algorithm, which doubles the maximum tag-side efficiency of its predecessor. The fourth part presents a complete measurement of the branching fraction of the rare leptonic B decay ?B?tau nu?, which is used to validate the algorithms discussed in previous parts.
410  0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5053
606   $aParticles (Nuclear physics)
606   $aQuantum field theory
606   $aArtificial intelligence
606   $aSociophysics
606   $aEconophysics
606   $aPhysical measurements
606   $aMeasurement
606   $aElementary Particles, Quantum Field Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/P23029
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aData-driven Science, Modeling and Theory Building$3https://scigraph.springernature.com/ontologies/product-market-codes/P33030
606   $aMeasurement Science and Instrumentation$3https://scigraph.springernature.com/ontologies/product-market-codes/P31040
615  0$aParticles (Nuclear physics)
615  0$aQuantum field theory.
615  0$aArtificial intelligence.
615  0$aSociophysics.
615  0$aEconophysics.
615  0$aPhysical measurements.
615  0$aMeasurement.
615 14$aElementary Particles, Quantum Field Theory.
615 24$aArtificial Intelligence.
615 24$aData-driven Science, Modeling and Theory Building.
615 24$aMeasurement Science and Instrumentation.
676   $a006.3
700   $aKeck$b Thomas$4aut$4http://id.loc.gov/vocabulary/relators/aut$0841360
906   $aBOOK
912   $a9910303438403321
996   $aMachine Learning at the Belle II Experiment$91878320
997   $aUNINA