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Author Binosi, D.; Chang, L.; Ding, M.H.; Gao, F.; Papavassiliou, J.; Roberts, C.D.
Title Distribution amplitudes of heavy-light mesons Type Journal Article
Year 2019 Publication Physics Letters B Abbreviated Journal Phys. Lett. B
Volume 790 Issue Pages 257-262
Keywords B-meson decays; Heavy-light mesons; Nonperturbative continuum methods in quantum field theory; Parton distribution amplitudes; Quantum chromodynamics
Abstract (down) A symmetry-preserving approach to the continuum bound-state problem in quantum field theory is used to calculate the masses, leptonic decay constants and light-front distribution amplitudes of empirically accessible heavy-light mesons. The inverse moment of the B-meson distribution is particularly important in treatments of exclusive B-decays using effective field theory and the factorisation formalism; and its value is therefore computed: lambda(B) = (zeta = 2GeV) = 0.54(3) GeV. As an example and in anticipation of precision measurements at new-generation B-factories, the branching fraction for the rare B -> gamma (E-gamma)l nu(l) radiative decay is also calculated, retaining 1/m(B)(2), and 1/E-gamma(2) corrections to the differential decay width, with the result Gamma(B -> gamma l nu l) /Gamma(B) = 0.47 (15) on E-gamma > 1.5 GeV.
Address [Binosi, Daniele; Ding, Minghui] European Ctr Theoret Studies Nucl Phys & Related, Str Tabarelle 286, I-38123 Villazzano, TN, Italy, Email: binosi@ectstar.eu;
Corporate Author Thesis
Publisher Elsevier Science Bv Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0370-2693 ISBN Medium
Area Expedition Conference
Notes WOS:000460118200030 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 3934
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Author Kasieczka, G. et al; Sanz, V.
Title The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics Type Journal Article
Year 2021 Publication Reports on Progress in Physics Abbreviated Journal Rep. Prog. Phys.
Volume 84 Issue 12 Pages 124201 - 64pp
Keywords anomaly detection; machine learning; unsupervised learning; weakly supervised learning; semisupervised learning; beyond the standard model; model-agnostic methods
Abstract (down) A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.
Address [Kasieczka, Gregor] Univ Hamburg, Inst Expt Phys, Hamburg, Germany, Email: gregor.kasieczka@uni-hamburg.de;
Corporate Author Thesis
Publisher IOP Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0034-4885 ISBN Medium
Area Expedition Conference
Notes WOS:000727698500001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 5039
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Author ATLAS Collaboration (Abat, E. et al); Bernabeu Verdu, J.; Castillo Gimenez, V.; Costa, M.J.; Escobar, C.; Ferrer, A.; Garcia, C.; Gonzalez-Sevilla, S.; Higon-Rodriguez, E.; Lacasta, C.; Marti-Garcia, S.; Mitsou, V.A.; Ruiz, A.; Solans, C.; Valero, A.; Valls Ferrer, J.A.
Title A layer correlation technique for pion energy calibration at the 2004 ATLAS Combined Beam Test Type Journal Article
Year 2011 Publication Journal of Instrumentation Abbreviated Journal J. Instrum.
Volume 6 Issue Pages P06001 - 35pp
Keywords Calorimeter methods; Pattern recognition, cluster finding, calibration and fitting methods; Calorimeters; Detector modelling and simulations I (interaction of radiation with matter, interaction of photons with matter, interaction of hadrons with matter, etc)
Abstract (down) A new method for calibrating the hadron response of a segmented calorimeter is developed and successfully applied to beam test data. It is based on a principal component analysis of energy deposits in the calorimeter layers, exploiting longitudinal shower development information to improve the measured energy resolution. Corrections for invisible hadronic energy and energy lost in dead material in front of and between the calorimeters of the ATLAS experiment were calculated with simulated Geant4 Monte Carlo events and used to reconstruct the energy of pions impinging on the calorimeters during the 2004 Barrel Combined Beam Test at the CERN H8 area. For pion beams with energies between 20 GeV and 180 GeV, the particle energy is reconstructed within 3% and the energy resolution is improved by between 11% and 25% compared to the resolution at the electromagnetic scale.
Address [Wheeler, S] Univ Alberta, Dept Phys, Ctr Particle Phys, Edmonton, AB T6G 2G7, Canada[Bernabeu, J; Castillo, MV; Costa, MJ; Escobar, C; Ferrer, A; Garcia, C; Gonzalez-Sevilla, S; Higon, E; Lacasta, C; Garcia, SMI; Mitsou, VA; Ruiz, A; Solans, C; Valero, A; Valls, JA] Ctr Mixto UVEG CSIC, Inst Fis Corpuscular IFIC, ES-46071 Valencia, Spain, Email: kjg@particle.kth.se
Corporate Author Thesis
Publisher Iop Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1748-0221 ISBN Medium
Area Expedition Conference
Notes WOS:000294492600001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ elepoucu @ Serial 744
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Author LHCb Collaboration (Aaij, R. et al); Martinez-Vidal, F.; Oyanguren, A.; Ruiz Valls, P.; Sanchez Mayordomo, C.
Title A new algorithm for identifying the flavour of B-s(0) mesons at LHCb Type Journal Article
Year 2016 Publication Journal of Instrumentation Abbreviated Journal J. Instrum.
Volume 11 Issue Pages P05010 - 23pp
Keywords Analysis and statistical methods; Particle identification methods; Pattern recognition; cluster finding; calibration and fitting methods
Abstract (down) A new algorithm for the determination of the initial flavour of B-s(0) mesons is presented. The algorithm is based on two neural networks and exploits the b hadron production mechanism at a hadron collider. The first network is trained to select charged kaons produced in association with the B-s(0) meson. The second network combines the kaon charges to assign the B-s(0) flavour and estimates the probability of a wrong assignment. The algorithm is calibrated using data corresponding to an integrated luminosity of 3 fb(-1) collected by the LHCb experiment in proton-proton collisions at 7 and 8 TeV centre-of-mass energies. The calibration is performed in two ways: by resolving the B-s(0)-B-s(0) flavour oscillations in B-s(0) -> D-s(-)pi(+) decays, and by analysing flavour-specific B-s2*(5840)(0) -> B+K- decays. The tagging power measured in B-s(0) -> D-s(-)pi(+) decays is found to be (1.80 +/- 0.19 ( stat) +/- 0.18 (syst))%, which is an improvement of about 50% compared to a similar algorithm previously used in the LHCb experiment.
Address [Bediaga, I.; De Miranda, J. M.; Rodrigues, F. Ferreira; Gomes, A.; Massafferri, A.; Rodrigues, B. Osorio; dos Reis, A. C.; Rodrigues, A. B.] CBPF, Rio De Janeiro, Brazil, Email: mirco.dorigo@cern.ch
Corporate Author Thesis
Publisher Iop Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1748-0221 ISBN Medium
Area Expedition Conference
Notes WOS:000377851700033 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 2731
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Author CALICE Collaboration (Lai, S. et al); Irles, A.
Title Software compensation for highly granular calorimeters using machine learning Type Journal Article
Year 2024 Publication Journal of Instrumentation Abbreviated Journal J. Instrum.
Volume 19 Issue 4 Pages P04037 - 28pp
Keywords Large detector-systems performance; Pattern recognition; cluster finding; calibration and fitting methods; Performance of High Energy Physics Detectors
Abstract (down) A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied.
Address [Lai, S.; Utehs, J.; Wilhahn, A.] Georg August Univ Gottingen, Phys Inst 2, Friedrich Hund Pl 1, D-37077 Gottingen, Germany, Email: jack.rolph@desy.de
Corporate Author Thesis
Publisher IOP Publishing Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1748-0221 ISBN Medium
Area Expedition Conference
Notes WOS:001230094600001 Approved no
Is ISI yes International Collaboration yes
Call Number IFIC @ pastor @ Serial 6128
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