A Practical Way to Regularize Unfolding of Sharply Varying Spectra with Low Data Statistics
Abstract
Unfolding is a wellestablished tool in particle physics. However, a naive application of the standard regularization techniques to unfold the momentum spectrum of protons ejected in the process of negative muon nuclear capture led to a result exhibiting unphysical artifacts. A finite data sample limited the range in which unfolding can be performed, thus introducing a cutoff. A sharply falling "true" distribution led to low data statistics near the cutoff, which exacerbated the regularization bias and produced an unphysical spike in the resulting spectrum. An improved approach has been developed to address these issues and is illustrated using a toy model. The approach uses full Poisson likelihood of data, and produces a continuous, physically plausible, unfolded distribution. The new technique has a broad applicability since spectra with similar features, such as sharply falling spectra, are common.
 Authors:

 Fermilab
 Publication Date:
 Research Org.:
 Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
 Sponsoring Org.:
 USDOE Office of Science (SC), High Energy Physics (HEP) (SC25)
 OSTI Identifier:
 1573831
 Report Number(s):
 arXiv:1906.07918; FERMILABPUB19262PPD
oai:inspirehep.net:1740520
 DOE Contract Number:
 AC0207CH11359
 Resource Type:
 Journal Article
 Journal Name:
 TBD
 Additional Journal Information:
 Journal Name: TBD
 Country of Publication:
 United States
 Language:
 English
Citation Formats
Gaponenko, Andrei. A Practical Way to Regularize Unfolding of Sharply Varying Spectra with Low Data Statistics. United States: N. p., 2019.
Web.
Gaponenko, Andrei. A Practical Way to Regularize Unfolding of Sharply Varying Spectra with Low Data Statistics. United States.
Gaponenko, Andrei. Wed .
"A Practical Way to Regularize Unfolding of Sharply Varying Spectra with Low Data Statistics". United States. https://www.osti.gov/servlets/purl/1573831.
@article{osti_1573831,
title = {A Practical Way to Regularize Unfolding of Sharply Varying Spectra with Low Data Statistics},
author = {Gaponenko, Andrei},
abstractNote = {Unfolding is a wellestablished tool in particle physics. However, a naive application of the standard regularization techniques to unfold the momentum spectrum of protons ejected in the process of negative muon nuclear capture led to a result exhibiting unphysical artifacts. A finite data sample limited the range in which unfolding can be performed, thus introducing a cutoff. A sharply falling "true" distribution led to low data statistics near the cutoff, which exacerbated the regularization bias and produced an unphysical spike in the resulting spectrum. An improved approach has been developed to address these issues and is illustrated using a toy model. The approach uses full Poisson likelihood of data, and produces a continuous, physically plausible, unfolded distribution. The new technique has a broad applicability since spectra with similar features, such as sharply falling spectra, are common.},
doi = {},
journal = {TBD},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {6}
}