DocumentCode :
1475820
Title :
RAVE—A Detector-Independent Toolkit to Reconstruct Vertices
Author :
Waltenberger, Wolfgang
Author_Institution :
Inst. for High Energy Phys., Austrian Acad. of Sci., Vienna, Austria
Volume :
58
Issue :
2
fYear :
2011
fDate :
4/1/2011 12:00:00 AM
Firstpage :
434
Lastpage :
444
Abstract :
A detector-independent toolkit for vertex reconstruction (RAVE = “Reconstruction (of vertices) in Abstract, Versatile Environments”) is presented that allows geometric and kinematic reconstruction of vertices. Both linear and adaptive estimation techniques are covered. Non-Gaussian input data can be handled via the Gaussian-sum technique. Kinematic constraints are taken into account via the Lagrangian formalism. Finally, the toolkit also contains a simple flavor-tagger. Main design goals are ease of use, flexibility for embedding into existing software frameworks, extensibility, and openness. The implementation is based on modern object-oriented techniques, is coded in C++ with interfaces for Java and Python, and follows an open-source approach.
Keywords :
Kalman filters; adaptive estimation; position sensitive particle detectors; Gaussian-sum technique; Kalman filter; Lagrangian formalism; RAVE; Reconstruction-in-Abstract Versatile Environments; adaptive estimation technique; detector-independent toolkit; flavor-tagger; geometric vertex reconstruction; kinematic vertex reconstruction; linear estimation technique; nonGaussian input data; open-source approach; Covariance matrix; Kalman filters; Kinematics; Software; Software algorithms; User interfaces; Adaptive method; Gaussian sum filter; Kalman filter; event reconstruction; flavor tagging; kinematic fitting;
fLanguage :
English
Journal_Title :
Nuclear Science, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9499
Type :
jour
DOI :
10.1109/TNS.2011.2119492
Filename :
5734880
Link To Document :
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