المؤلفون: Kees Benkendorfer, Benjamin Philip Nachman, Luc Le Pottier

المصدر: Physical Review
Physical Review D, vol 104, iss 3

مصطلحات موضوعية: FOS: Computer and information sciences, FOS: Physical sciences, Machine Learning (stat.ML), Atomic, High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex), High Energy Physics - Phenomenology (hep-ph), Particle and Plasma Physics, Robustness (computer science), Statistics - Machine Learning, Classifier (linguistics), Invariant mass, Nuclear, Decorrelation, Physics, Quantum Physics, Learning classifier system, Large Hadron Collider, business.industry, Molecular, Pattern recognition, Nuclear & Particles Physics, High Energy Physics - Phenomenology, Physics - Data Analysis, Statistics and Probability, Unsupervised learning, Anomaly detection, Artificial intelligence, business, Data Analysis, Statistics and Probability (physics.data-an), Astronomical and Space Sciences

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المؤلفون: David Shih, Benjamin Philip Nachman

المصدر: Physical Review
Physical Review D, vol 101, iss 7

مصطلحات موضوعية: FOS: Computer and information sciences, Signal region, FOS: Physical sciences, Machine Learning (stat.ML), 01 natural sciences, Atomic, High Energy Physics - Experiment, High Energy Physics - Experiment (hep-ex), High Energy Physics - Phenomenology (hep-ph), Particle and Plasma Physics, Statistics - Machine Learning, 0103 physical sciences, Leverage (statistics), Nuclear, 010306 general physics, Physics, Quantum Physics, Large Hadron Collider, 010308 nuclear & particles physics, Molecular, Density estimation, Nuclear & Particles Physics, Anode, High Energy Physics - Phenomenology, Physics - Data Analysis, Statistics and Probability, Conditional probability density, Anomaly detection, Algorithm, Data Analysis, Statistics and Probability (physics.data-an), Astronomical and Space Sciences

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المؤلفون: Benjamin Philip Nachman, Kiel Howe, Jack H. Collins

المصدر: Physical Review

مصطلحات موضوعية: Physics, Large Hadron Collider, Exploit, 010308 nuclear & particles physics, business.industry, Physics beyond the Standard Model, Resonance, Machine learning, computer.software_genre, 01 natural sciences, Uncorrelated, Test case, 0103 physical sciences, Invariant mass, Artificial intelligence, 010306 general physics, business, Classifier (UML), computer

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المؤلفون: Benjamin Philip Nachman, Vladimir Gligorov, Simon Knapen, Dean J. Robinson, Michele Papucci

المساهمون: Laboratoire de Physique Nucléaire et de Hautes Énergies (LPNHE (UMR_7585)), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Paris Diderot - Paris 7 (UPD7)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

المصدر: Physical Review
Physical Review D
Physical Review D, American Physical Society, 2019, 99 (1), pp.015023. ⟨10.1103/PhysRevD.99.015023⟩
Phys.Rev.D
Phys.Rev.D, 2019, 99 (1), pp.015023. ⟨10.1103/PhysRevD.99.015023⟩

مصطلحات موضوعية: Particle physics, Higgs particle: decay, Physics beyond the Standard Model, 01 natural sciences, Particle identification, ALICE, 0103 physical sciences, CERN LHC Coll: upgrade, [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex], Sensitivity (control systems), 010306 general physics, numerical calculations, Event reconstruction, Physics, Luminosity (scattering theory), Time projection chamber, Large Hadron Collider, Interaction point, 010308 nuclear & particles physics, new physics, background, pi: decay, B: decay, sensitivity, LHC-B, time projection chamber, Beyond the standard model, eta: decay, [PHYS.HPHE]Physics [physics]/High Energy Physics - Phenomenology [hep-ph], vertex: primary, luminosity: high, particle: long-lived, particle identification

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