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Author |
LHCb Collaboration (Aaij, R. et al); Jaimes Elles, S.J.; Jashal, B.K.; Martinez-Vidal, F.; Oyanguren, A.; Rebollo De Miguel, M.; Sanderswood, I.; Zhuo, J. |
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Title |
Momentum scale calibration of the LHCb spectrometer |
Type |
Journal Article |
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Year |
2024 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
19 |
Issue |
2 |
Pages |
P02008 - 21pp |
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Keywords |
Particle tracking detectors; Analysis and statistical methods |
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Abstract |
For accurate determination of particle masses accurate knowledge of the momentum scale of the detectors is crucial. The procedure used to calibrate the momentum scale of the LHCb spectrometer is described and illustrated using the performance obtained with an integrated luminosity of 1.6 fb-1 collected during 2016 in pp running. The procedure uses large samples of J/qi -> mu+mu- and B+ -> J/qiK+ decays and leads to a relative accuracy of 3 x 10-4 on the momentum scale. |
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Address |
[Egede, U.; Fujii, Y.; Hadavizadeh, T.; Henderson, R. D. L.; Lane, J. J.; Monk, M.; Song, R.; Walton, E. J.; Ward, J. A.] Monash Univ, Sch Phys & Astron, Melbourne, Vic, Australia, Email: seophine.stanislaus@cern.ch |
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Publisher |
IOP Publishing Ltd |
Place of Publication |
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Language |
English |
Summary Language |
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Original Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1748-0221 |
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Expedition |
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Notes |
WOS:001185791500004 |
Approved  |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
6070 |
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Author |
CALICE Collaboration (Lai, S. et al); Irles, A. |
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Title |
Software compensation for highly granular calorimeters using machine learning |
Type |
Journal Article |
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Year |
2024 |
Publication |
Journal of Instrumentation |
Abbreviated Journal |
J. Instrum. |
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Volume |
19 |
Issue |
4 |
Pages |
P04037 - 28pp |
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Keywords |
Large detector-systems performance; Pattern recognition; cluster finding; calibration and fitting methods; Performance of High Energy Physics Detectors |
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Abstract |
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. |
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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 |
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Corporate Author |
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Publisher |
IOP Publishing Ltd |
Place of Publication |
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Editor |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1748-0221 |
ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
WOS:001230094600001 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
6128 |
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