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Data Unfolding Methods in High Energy Physics · Around

2016

Data Unfolding Methods in High Energy Physics

Schmitt, Stefan

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A selection of unfolding methods commonly used in High Energy Physics is compared.

The methods discussed here are: bin-by-bin correction factors, matrix inversion, template fit, Tikhonov regularisation and two examples of iterative methods.
Two procedures to choose the strength of the regularisation are tested, namely the L-curve scan and a scan of global correlation coefficients.
The advantages and disadvantages of the unfolding methods and choices of the regularisation strength are discussed using a toy example.

Built on

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Similar

P. C. Hansen, The L-curve and Its Use in the Numerical Treatment of Inverse Problems
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P. C. Hansen, Discrete Inverse Problems – Insight and Algorithms
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V. Blobel, “Data unfolding”, talk given at the Terascale statistics School, Hamburg (2010) http://www.desy.de/~blobel/
2010
Cited alongside, same era.

Then

PHYSTAT 2011 Workshop proceedings, Eds. L. Lyons and H. Prosper, CERN-2011-006
2011
Later among the works it cites.
S. Schmitt, JINST 7
Original
2012
Later among the works it cites.
B. Malaescu, arXiv:0907.3791; C++ code provided by the author, August 2016
Original
2016
Closest in time.
B. Malaescu, private communication, August 2016
2016
Closest in time.

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