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SVD approach to data unfolding

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SVD approach to data unfolding. / Höcker, Andreas ; Kartvelishvili, Vakhtang.
In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 372, No. 3, 01.04.1996, p. 469-481.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Höcker, A & Kartvelishvili, V 1996, 'SVD approach to data unfolding', Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 372, no. 3, pp. 469-481. https://doi.org/10.1016/0168-9002(95)01478-0

APA

Höcker, A., & Kartvelishvili, V. (1996). SVD approach to data unfolding. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 372(3), 469-481. https://doi.org/10.1016/0168-9002(95)01478-0

Vancouver

Höcker A, Kartvelishvili V. SVD approach to data unfolding. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 1996 Apr 1;372(3):469-481. doi: 10.1016/0168-9002(95)01478-0

Author

Höcker, Andreas ; Kartvelishvili, Vakhtang. / SVD approach to data unfolding. In: Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 1996 ; Vol. 372, No. 3. pp. 469-481.

Bibtex

@article{68d264599d7646e7b25886384bb5ae12,
title = "SVD approach to data unfolding",
abstract = "Distributions measured in high energy physics experiments are usually distorted and/or transformed by various detector effects. A regularization method for unfolding these distributions is re-formulated in terms of the Singular Value Decomposition (SVD) of the response matrix. A relatively simple, yet quite efficient unfolding procedure is explained in detail. The concise linear algorithm results in a straightforward implementation with full error propagation, including the complete covariance matrix and its inverse. Several improvements upon widely used procedures are proposed, and recommendations are given how to simplify the task by the proper choice of the matrix. Ways of determining the optimal value of the regularization parameter are suggested and discussed, and several examples illustrating the use of the method are presented.",
author = "Andreas H{\"o}cker and Vakhtang Kartvelishvili",
year = "1996",
month = apr,
day = "1",
doi = "10.1016/0168-9002(95)01478-0",
language = "English",
volume = "372",
pages = "469--481",
journal = "Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment",
issn = "0168-9002",
publisher = "ELSEVIER SCIENCE BV",
number = "3",

}

RIS

TY - JOUR

T1 - SVD approach to data unfolding

AU - Höcker, Andreas

AU - Kartvelishvili, Vakhtang

PY - 1996/4/1

Y1 - 1996/4/1

N2 - Distributions measured in high energy physics experiments are usually distorted and/or transformed by various detector effects. A regularization method for unfolding these distributions is re-formulated in terms of the Singular Value Decomposition (SVD) of the response matrix. A relatively simple, yet quite efficient unfolding procedure is explained in detail. The concise linear algorithm results in a straightforward implementation with full error propagation, including the complete covariance matrix and its inverse. Several improvements upon widely used procedures are proposed, and recommendations are given how to simplify the task by the proper choice of the matrix. Ways of determining the optimal value of the regularization parameter are suggested and discussed, and several examples illustrating the use of the method are presented.

AB - Distributions measured in high energy physics experiments are usually distorted and/or transformed by various detector effects. A regularization method for unfolding these distributions is re-formulated in terms of the Singular Value Decomposition (SVD) of the response matrix. A relatively simple, yet quite efficient unfolding procedure is explained in detail. The concise linear algorithm results in a straightforward implementation with full error propagation, including the complete covariance matrix and its inverse. Several improvements upon widely used procedures are proposed, and recommendations are given how to simplify the task by the proper choice of the matrix. Ways of determining the optimal value of the regularization parameter are suggested and discussed, and several examples illustrating the use of the method are presented.

U2 - 10.1016/0168-9002(95)01478-0

DO - 10.1016/0168-9002(95)01478-0

M3 - Journal article

VL - 372

SP - 469

EP - 481

JO - Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

JF - Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

SN - 0168-9002

IS - 3

ER -