ALICE Statistical Wish-list

A few statistical problems faced by the event reconstruction in ALICE experiment at CERN are discussed in this paper. We outline several ad-hoc extensions of traditional Kalman- lter track nding which seem to increase the quality of tracks reconstructed in high multiplicity events anticipated for Pb...

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Detalles Bibliográficos
Autor principal: Belikov, Iouri
Lenguaje:eng
Publicado: CERN 2008
Materias:
Mathematical Physics and Mathematics
Detectors and Experimental Techniques
Computing and Computers
Acceso en línea:https://dx.doi.org/10.5170/CERN-2008-001.83
http://cds.cern.ch/record/1099974
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author Belikov, Iouri
author_facet Belikov, Iouri
author_sort Belikov, Iouri
collection CERN
description A few statistical problems faced by the event reconstruction in ALICE experiment at CERN are discussed in this paper. We outline several ad-hoc extensions of traditional Kalman- lter track nding which seem to increase the quality of tracks reconstructed in high multiplicity events anticipated for Pb– Pb collisions at LHC. These extensions, however, need a stricter formulation and justi cation from the theoretical side. The particle identi cation in ALICE is done by combining the information from different detecting systems using a Bayesian method. Having many clear advantages, this approach introduces into the analysis additional complications which are also discussed here.
id cern-1099974
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2008
publisher CERN
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spelling cern-10999742019-09-30T06:29:59Zdoi:10.5170/CERN-2008-001.83http://cds.cern.ch/record/1099974engBelikov, IouriALICE Statistical Wish-listMathematical Physics and MathematicsDetectors and Experimental TechniquesComputing and ComputersA few statistical problems faced by the event reconstruction in ALICE experiment at CERN are discussed in this paper. We outline several ad-hoc extensions of traditional Kalman- lter track nding which seem to increase the quality of tracks reconstructed in high multiplicity events anticipated for Pb– Pb collisions at LHC. These extensions, however, need a stricter formulation and justi cation from the theoretical side. The particle identi cation in ALICE is done by combining the information from different detecting systems using a Bayesian method. Having many clear advantages, this approach introduces into the analysis additional complications which are also discussed here.CERNoai:cds.cern.ch:10999742008
spellingShingle Mathematical Physics and Mathematics
Detectors and Experimental Techniques
Computing and Computers
Belikov, Iouri
ALICE Statistical Wish-list
title ALICE Statistical Wish-list
title_full ALICE Statistical Wish-list
title_fullStr ALICE Statistical Wish-list
title_full_unstemmed ALICE Statistical Wish-list
title_short ALICE Statistical Wish-list
title_sort alice statistical wish-list
topic Mathematical Physics and Mathematics
Detectors and Experimental Techniques
Computing and Computers
url https://dx.doi.org/10.5170/CERN-2008-001.83
http://cds.cern.ch/record/1099974
work_keys_str_mv AT belikoviouri alicestatisticalwishlist

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