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Author LHCb Collaboration (Aaij, R. et al); Jashal, B.K.; Martinez-Vidal, F.; Oyanguren, A.; Remon Alepuz, C.; Ruiz Vidal, J. url  doi
openurl 
  Title Identification of charm jets at LHCb Type Journal Article
  Year 2022 Publication Journal of Instrumentation Abbreviated Journal J. Instrum.  
  Volume 17 Issue 2 Pages P02028 - 23pp  
  Keywords (down) Analysis and statistical methods; Pattern recognition; cluster finding; calibration and fitting methods; Performance of High Energy Physics Detectors  
  Abstract The identification of charm jets is achieved at LHCb for data collected in 2015-2018 using a method based on the properties of displaced vertices reconstructed and matched with jets. The performance of this method is determined using a dijet calibration dataset recorded by the LHCb detector and selected such that the jets are unbiased in quantities used in the tagging algorithm. The charm-tagging efficiency is reported as a function of the transverse momentum of the jet. The measured efficiencies are compared to those obtained from simulation and found to be in good agreement.  
  Address [Leite, J. Baptista; Bediaga, I; Torres, M. Cruz; De Miranda, J. M.; dos Reis, A. C.; Gomes, A.; Massafferri, A.; Machado, D. Torres] Ctr Brasileiro Pesquisas Fis CBPF, Rio De Janeiro, Brazil, Email: dcraik@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:000770368300015 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 5177  
Permanent link to this record
 

 
Author NEXT Collaboration (Renner, J. et al); Benlloch-Rodriguez, J.; Botas, A.; Ferrario, P.; Gomez-Cadenas, J.J.; Alvarez, V.; Carcel, S.; Carrion, J.V.; Cervera-Villanueva, A.; Diaz, J.; Laing, A.; Liubarsky, I.; Lopez-March, N.; Lorca, D.; Martinez, A.; Monrabal, F.; Muñoz Vidal, J.; Nebot-Guinot, M.; Novella, P.; Palmeiro, B.; Querol, M.; Rodriguez, J.; Serra, L.; Simon, A.; Sorel, M.; Yahlali, N. url  doi
openurl 
  Title Background rejection in NEXT using deep neural networks Type Journal Article
  Year 2017 Publication Journal of Instrumentation Abbreviated Journal J. Instrum.  
  Volume 12 Issue Pages T01004 - 21pp  
  Keywords (down) Analysis and statistical methods; Pattern recognition; cluster finding; calibration and fitting methods; Double-beta decay detectors; Time projection chambers  
  Abstract We investigate the potential of using deep learning techniques to reject background events in searches for neutrinoless double beta decay with high pressure xenon time projection chambers capable of detailed track reconstruction. The differences in the topological signatures of background and signal events can be learned by deep neural networks via training over many thousands of events. These networks can then be used to classify further events as signal or background, providing an additional background rejection factor at an acceptable loss of efficiency. The networks trained in this study performed better than previous methods developed based on the use of the same topological signatures by a factor of 1.2 to 1.6, and there is potential for further improvement.  
  Address [Renner, J.; Munoz Vidal, J.; Benlloch-Rodriguez, J. M.; Botas, A.; Ferrario, P.; Gomez-Cadenas, J. J.; Alvarez, V.; Carcel, S.; Carrion, J. V.; Cervera, A.; Diaz, J.; Laing, A.; Liubarsky, I.; Lopez-March, N.; Lorca, D.; Martinez, A.; Monrabal, F.; Nebot-Guinot, M.; Novella, P.; Palmeiro, B.; Querol, M.; Rodriguez, J.; Serra, L.; Simon, A.; Sorel, M.; Yahlali, N.] CSIC, Inst Fis Corpuscular IFIC, Calle Catedrat Jose Beltran 2, Valencia 46980, Spain, Email: jrenner@ific.uv.es  
  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:000395770200004 Approved no  
  Is ISI yes International Collaboration yes  
  Call Number IFIC @ pastor @ Serial 2995  
Permanent link to this record
 

 
Author LHCb Collaboration (Aaij, R. et al); Martinez-Vidal, F.; Oyanguren, A.; Ruiz Valls, P.; Sanchez Mayordomo, C. url  doi
openurl 
  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 (down) Analysis and statistical methods; Particle identification methods; Pattern recognition; cluster finding; calibration and fitting methods  
  Abstract 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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