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pro vyhledávání: '"Rudolf Frühwirth"'
Autor:
Rudolf Frühwirth
Publikováno v:
Instruments, Vol 4, Iss 3, p 25 (2020)
This note describes the application of Gaussian mixture regression to track fitting with a Gaussian mixture model of the position errors. The mixture model is assumed to have two components with identical component means. Under the premise that the a
Externí odkaz:
https://doaj.org/article/1298999a435c4a12811169d281ee930e
Publikováno v:
Austrian Journal of Statistics, Vol 37, Iss 3&4 (2016)
A new type of redescending M-estimators is constructed, based on data augmentation with an unspecified outlier model. Necessary and sufficient conditions for the convergence of the resulting estimators to the Hubertype skipped mean are derived. By in
Externí odkaz:
https://doaj.org/article/4d65e2ec9fa449cdaa3fe68e695dfd36
Publikováno v:
Austrian Journal of Statistics, Vol 41, Iss 1 (2016)
The multinomial logit model (MNL) possesses a latent variable representation in terms of random variables following a multivariate logistic distribution. Based on multivariate finite mixture approximations of the multivariate logistic distribution, v
Externí odkaz:
https://doaj.org/article/9be0c1c612d044e1b82d4ba69b87af3f
Autor:
Rudolf Frühwirth, Are Strandlie
This open access book is a comprehensive review of the methods and algorithms that are used in the reconstruction of events recorded by past, running and planned experiments at particle accelerators such as the LHC, SuperKEKB and FAIR. The main topic
Autor:
Thomas Hauth, Tobias Schlüter, Nils Braun, Nicolas Gosling, Laura Zani, Eugenio Paoloni, Giulio Dujany, Benjamin Schwenker, Cyrille Praz, Valerio Bertacchi, T. Kuhr, Navid Rad, Giulia Casarosa, Armine Rostomyan, Felix Metzner, C. Wessel, B. Scavino, Oliver Frost, Thomas Lueck, Alberto Martini, Michael Eliachevitch, Rudolf Frühwirth, Henrikas Svidras, Yuma Uematsu, Patrick Ecker, Aiqiang Guo, Petar Rados, C. Pulvermacher, M. T. Prim, Martin Ritter, S. Spataro, S. Cunliffe, Claus Kleinwort, Jakob Lettenbichler, M. Heck, Dmitrii Neverov, T. Bilka, Luigi Corona, Uwe Gebauer, F. Tenchini, Sebastian Racs, Sourav Kanti Patra, Filippo Dattola, Giacomo de Pietro, G. Rizzo, James Webb, S. Kurz, Thanh van Dong, Carsten Niebuhr, Michael de Nuccio, Sasha Glazov, Leo Piilonen, Gaetano de Marino, B. Spruck, Jakub Kandra, Mateusz Kaleta, Peter Kvasnicka, Tristan Fillinger
Publikováno v:
Computer physics communications 259, 107610 (2021). doi:10.1016/j.cpc.2020.107610
Comput.Phys.Commun.
Comput.Phys.Commun., 2021, 259, pp.107610. ⟨10.1016/j.cpc.2020.107610⟩
Comput.Phys.Commun.
Comput.Phys.Commun., 2021, 259, pp.107610. ⟨10.1016/j.cpc.2020.107610⟩
Computer physics communications 259, 107610 (2021). doi:10.1016/j.cpc.2020.107610
This paper describes the track-finding algorithm that is used for event reconstruction in the Belle II experiment operating at the SuperKEKB B-factory in Tsukuba,
This paper describes the track-finding algorithm that is used for event reconstruction in the Belle II experiment operating at the SuperKEKB B-factory in Tsukuba,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::06cb28d6041f01ce6ead346f480bbf17
https://bib-pubdb1.desy.de/record/453456
https://bib-pubdb1.desy.de/record/453456
Autor:
Rudolf Frühwirth, Are Strandlie
Publikováno v:
Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors ISBN: 9783030657703
The chapter gives an outline of the event reconstruction chain of a typical large experiment, from the trigger to the physics object reconstruction. The concept of the trigger is illustrated by two examples, CMS and LHCb, followed by a discussion of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::45ea4359e0f40316fadef4638fd1fe4c
https://doi.org/10.1007/978-3-030-65771-0_2
https://doi.org/10.1007/978-3-030-65771-0_2
Autor:
Rudolf Frühwirth, Are Strandlie
Publikováno v:
Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors ISBN: 9783030657703
Vertex finding is the search for clusters of tracks that originate at the same point in space. The chapter discusses a variety of methods for finding primary vertices, first in one and then in three dimensions. Details are given on model-based cluste
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8901337200cf4f31c54f6c6a75ca8cec
https://doi.org/10.1007/978-3-030-65771-0_7
https://doi.org/10.1007/978-3-030-65771-0_7
Autor:
Rudolf Frühwirth, Are Strandlie
Publikováno v:
Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors ISBN: 9783030657703
The chapter shows how the equations of motion for charged particles in a homogeneous or inhomogeneous magnetic field are solved. Various types of parametrizations are presented, and formulas for track propagation and error propagation are derived. As
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c608aefd883fd41abbce6c359c70d9bb
https://doi.org/10.1007/978-3-030-65771-0_4
https://doi.org/10.1007/978-3-030-65771-0_4
Autor:
Rudolf Frühwirth, Are Strandlie
Publikováno v:
Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors ISBN: 9783030657703
Track fitting is an application of established statistical estimation procedures with well-known properties. For a long time, estimators based on the least-squares principle were—with some notable exceptions—the principal methods for track fittin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a8f393073a1dbadaf1e2683a2d25c2d9
https://doi.org/10.1007/978-3-030-65771-0_6
https://doi.org/10.1007/978-3-030-65771-0_6
Autor:
Rudolf Frühwirth, Are Strandlie
Publikováno v:
Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors ISBN: 9783030657703
The chapter gives an overview of particle detectors, with the emphasis on tracking detectors. The working principles and the calibration of gaseous, semiconductor, and fiber detectors are explained, followed by a brief review of detector alignment. A
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9606e8a34282c7c48a98ce75f7f6b359
https://doi.org/10.1007/978-3-030-65771-0_1
https://doi.org/10.1007/978-3-030-65771-0_1