Comparison of machine learning approach to other commonly used unfolding methods

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F21%3A73607798" target="_blank" >RIV/61989592:15310/21:73607798 - isvavai.cz</a>
Result on the web
<a href="https://www.actaphys.uj.edu.pl/fulltext?series=Reg&vol=52&page=863" target="_blank" >https://www.actaphys.uj.edu.pl/fulltext?series=Reg&vol=52&page=863</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5506/APhysPolB.52.863" target="_blank" >10.5506/APhysPolB.52.863</a>

Result language
angličtina
Original language name
Comparison of machine learning approach to other commonly used unfolding methods
Original language description
Unfolding in high energy physics represents the correction of measured spectra in data for the finite detector efficiency, acceptance, and resolution from the detector to particle level. Recent machine learning approaches provide unfolding on an event-by-event basis allowing to simultaneously unfold a large number of variables and thus to cover a wider region of the features that affect detector response. This study focuses on a simple comparison of commonly used methods in RooUnfold package to the machine learning package OmniFold.
Czech name
—
Czech description
—

Project
<a href="/en/project/GA19-21484S" target="_blank" >GA19-21484S: Novel techniques for boosted top quarks reconstruction for new physics searches at LHC</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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