Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors (Particle Acceleration and Detection)
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Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors (Particle Acceleration and Detection), Jacob Greenstein, 9783030657703
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Dr Rudolf Frhwirth is retired from a senior staff position at the Institute of High Energy Physics of the Austrian Academy of Sciences in Vienna, where he headed the Algorithm and Software Development group until end of 2017. He studied mathematics at the TU Wien, from which he received his Diploma degree in 1976 and his Doctor of Technical Sciences degree in 1986. From 1979 to 1984 he was Research Associate at CERN. Since 1996 he is Dozent (Reader) in Statistical Data Analysis at TU Wien, where he regularly gives lectures on statistics and data analysis to physicists. He has contributed to the reconstruction software of numerous experiments, among them WA6, EHS, UA1, DELPHI and CMS at CERN, as well as Belle II at KEK. His research interests are data reconciliation with nonnormal data, pattern recognition in particle detectors, and statistical methods in track and vertex reconstruction, with the focus on adaptive and robust algorithms. Professor Are Strandlie, currently full professor of physics at NTNU – Norwegian University of Science and Technology, received his Master of Science degree in Theoretical Physics in 1995 and his Doctor of Science degree in Experimental Particle Physics in 2000, both from the University of Oslo. He was a Research Fellow at CERN between 2001 and 2003, where he was working on track reconstruction software development for the CMS Tracker. He has held a position as Adjunct Professor at the Department of Physics, University of Oslo, giving lectures about statistics and data analysis techniques in experimental high-energy physics. He is now involved in the ATLAS experiment at CERN. Strandlie’s research interests are concentrated around various aspects of the analysis of high-energy physics data, including the development and application of adaptive methods for track reconstruction.
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