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Author |
Aiola, S.; Amhis, Y.; Billoir, P.; Jashal, B.K.; Henry, L.; Oyanguren, A.; Marin Benito, C.; Polci, F.; Quagliani, R.; Schiller, M.; Wang, M. |
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Title |
Hybrid seeding: A standalone track reconstruction algorithm for scintillating fibre tracker at LHCb |
Type |
Journal Article |
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Year |
2021 |
Publication |
Computer Physics Communications |
Abbreviated Journal |
Comput. Phys. Commun. |
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Volume  |
260 |
Issue |
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Pages |
107713 - 5pp |
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Keywords |
Track reconstruction; Pattern Recognition; LHCb |
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Abstract |
We describe the Hybrid seeding, a stand-alone pattern recognition algorithm aiming at finding charged particle trajectories for the LHCb upgrade. A significant improvement to the charged particle reconstruction efficiency is accomplished by exploiting the knowledge of the LHCb magnetic field and the position of energy deposits in the scintillating fibre tracker detector. Moreover, we achieve a low fake rate and a small contribution to the overall timing budget of the LHCb real-time data processing. |
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Address |
[Billoir, P.; Polci, F.; Quagliani, R.] Sorbonne Univ, Paris Diderot Sorbonne Paris Cite, LPNHE, CNRS IN2P3, Paris, France, Email: louis.henry@cern.ch; |
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Elsevier |
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English |
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ISSN |
0010-4655 |
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Notes |
WOS:000608243400007 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4685 |
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Author |
ANTARES Collaboration (Albert, A. et al); Barrios-Marti, J.; Hernandez-Rey, J.J.; Illuminati, G.; Lotze, M.; Tönnis, C.; Zornoza, J.D.; Zuñiga, J. |
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Title |
Model-independent search for neutrino sources with the ANTARES neutrino telescope |
Type |
Journal Article |
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Year |
2020 |
Publication |
Astroparticle Physics |
Abbreviated Journal |
Astropart Phys. |
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Volume |
114 |
Issue |
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Pages |
35-47 |
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Keywords |
Neutrino astronomy; Astroparticle physics; Pattern recognition; Anisotropy |
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Abstract |
A novel method to analyse the spatial distribution of neutrino candidates recorded with the ANTARES neutrino telescope is introduced, searching for an excess of neutrinos in a region of arbitrary size and shape from any direction in the sky. Techniques originating from the domains of machine learning, pattern recognition and image processing are used to purify the sample of neutrino candidates and for the analysis of the obtained skymap. In contrast to a dedicated search for a specific neutrino emission model, this approach is sensitive to a wide range of possible morphologies of potential sources of high-energy neutrino emission. The application of these methods to ANTARES data yields a large-scale excess with a post-trial significance of 2.5 sigma. Applied to public data from IceCube in its IC40 configuration, an excess consistent with the results from ANTARES is observed with a post-trial significance of 2.1 sigma. |
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Address |
[Albert, A.; Drouhin, D.; Racca, C.; Saldana, M.] Univ Haute Alsace, Inst Univ Technol Colmar, GRPHE, 34 Rue Grillenbreit,BP Colmar 50568, F-68008 Mulhouse, France, Email: stefan.geisselsoeder@fau.de; |
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Publisher |
Elsevier |
Place of Publication |
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English |
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ISSN |
0927-6505 |
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Notes |
WOS:000489353300005 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
4167 |
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Author |
Bouhova-Thacker, E.; Kostyukhin, V.; Koffas, T.; Liebig, W.; Limper, M.; Piacquadio, G.N.; Prokofiev, K.; Weiser, C.; Wildauer, A. |
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Title |
Expected Performance of Vertex Reconstruction in the ATLAS Experiment at the LHC |
Type |
Journal Article |
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Year |
2010 |
Publication |
IEEE Transactions on Nuclear Science |
Abbreviated Journal |
IEEE Trans. Nucl. Sci. |
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Volume |
57 |
Issue |
2 |
Pages |
760-767 |
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Keywords |
Data analysis; data reconstruction; high energy physics; pattern recognition; reconstruction algorithms; tracking; vertex detectors |
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Abstract |
In the harsh environment of the Large Hadron Collider at CERN (design luminosity of 10(34) cm(-2) s(-1)) efficient reconstruction of vertices is crucial for many physics analyses. Described in this paper is the expected performance of the vertex reconstruction used in the ATLAS experiment. The algorithms for the reconstruction of primary and secondary vertices as well as for finding photon conversions and vertex reconstruction in jets are described. The implementation of vertex algorithms which follows a very modular design based on object-oriented C++ is presented. A user-friendly concept allows event reconstruction and physics analyses to compare and optimize their choice among different vertex reconstruction strategies. The performance of implemented algorithms has been studied on a variety of Monte Carlo samples and results are presented. |
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Address |
[Bouhova-Thacker, Eva] Univ Lancaster, Lancaster LA1 4YB, England, Email: bouhova@mail.cern.ch |
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Ieee-Inst Electrical Electronics Engineers Inc |
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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 Volume |
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Edition |
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ISSN |
0018-9499 |
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Notes |
ISI:000276679200006 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
260 |
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Author |
ATLAS Collaboration (Aad, G. et al); Aikot, A.; Amos, K.R.; Aparisi Pozo, J.A.; Bailey, A.J.; Bouchhar, N.; Cabrera Urban, S.; Cantero, J.; Cardillo, F.; Castillo Gimenez, V.; Chitishvili, M.; Costa, M.J.; Didenko, M.; Escobar, C.; Fiorini, L.; Fullana Torregrosa, E.; Fuster, J.; Garcia, C.; Garcia Navarro, J.E.; Gomez Delegido, A.J.; Gonzalez de la Hoz, S.; Gonzalvo Rodriguez, G.R.; Guerrero Rojas, J.G.R.; Lacasta, C.; Marti-Garcia, S.; Martinez Agullo, P.; Miralles Lopez, M.; Mitsou, V.A.; Monsonis Romero, L.; Moreno Llacer, M.; Munoz Perez, D.; Navarro-Gonzalez, J.; Poveda, J.; Prades Ibañez, A.; Rubio Jimenez, A.; Ruiz-Martinez, A.; Sabatini, P.; Saibel, A.; Salt, J.; Sanchez Sebastian, V.; Sayago Galvan, I.; Senthilkumar, V.; Soldevila, U.; Sanchez, J.; Torro Pastor, E.; Valero, A.; Valiente Moreno, E.; Valls Ferrer, J.A.; Varriale, L.; Villaplana Perez, M.; Vos, M. |
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Title |
Electron and photon energy calibration with the ATLAS detector using LHC Run 2 data |
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 |
P02009 - 58pp |
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Keywords |
Calorimeter methods; Pattern recognition; cluster finding; calibration and fitting methods; Performance of High Energy Physics Detectors |
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Abstract |
This paper presents the electron and photon energy calibration obtained with the ATLAS detector using 140 fb-1 of LHC proton -proton collision data recorded at -Js = 13 TeV between 2015 and 2018. Methods for the measurement of electron and photon energies are outlined, along with the current knowledge of the passive material in front of the ATLAS electromagnetic calorimeter. The energy calibration steps are discussed in detail, with emphasis on the improvements introduced in this paper. The absolute energy scale is set using a large sample of Z -boson decays into electron -positron pairs, and its residual dependence on the electron energy is used for the first time to further constrain systematic uncertainties. The achieved calibration uncertainties are typically 0.05% for electrons from resonant Z -boson decays, 0.4% at ET – 10 GeV, and 0.3% at ET – 1 TeV; for photons at ET <^>' 60 GeV, they are 0.2% on average. This is more than twice as precise as the previous calibration. The new energy calibration is validated using .11tfr -, ee and radiative Z -boson decays. |
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Address |
[Filmer, E. K.; Grant, C. M.; Jackson, P.; Kong, A. X. Y.; Pandya, H. D.; Potti, H.; Ruggeri, T. A.; Ting, E. X. L.; White, M. J.] Univ Adelaide, Dept Phys, Adelaide, SA, Australia |
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Corporate Author |
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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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Series Editor |
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Abbreviated Series Title |
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ISSN |
1748-0221 |
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Conference |
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Notes |
WOS:001185791500005 |
Approved |
no |
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Is ISI |
yes |
International Collaboration |
yes |
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Call Number |
IFIC @ pastor @ |
Serial |
6069 |
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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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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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