Improved extrapolation methods of data-driven background estimations in high energy physics

Choi, S.; Oh, H.

doi:10.1140/epjc/s10052-021-09404-1

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…For completeness, we briefly explain how the OT-kNN and OT-FvT estimators can also be used to estimate the unnormalized background intensity function β s 4 . We employ the widely-used ABCD method (Alison, 2015;Aad et al, 2015;Choi and Oh, 2021;Kasieczka et al, 2021), which requires the following assumption.…”

Section: 23mentioning

confidence: 99%

“…We refer to the book of Behnke et al (2013) for a pedagogical introduction to statistical aspects of the subject; see also Appendix 1 of Lyons (1989). Finally, we mention some recent advances on the widely-used sPlot (Barlow, 1987;Pivk and Le Diberder, 2005;Borisyak and Kazeev, 2019;Dembinski et al, 2022) and ABCD (Alison, 2015;Aad et al, 2015;Choi and Oh, 2021;Kasieczka et al, 2021) techniques for background estimation, the latter of which can be viewed as a precursor to the methods developed in this paper.…”

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confidence: 99%

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Background Modeling for Double Higgs Boson Production: Density Ratios and Optimal Transport

Manole¹,

Alison²,

Kuusela³

et al. 2022

Preprint

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We study the problem of data-driven background estimation, arising in the search of physics signals predicted by the Standard Model at the Large Hadron Collider. Our work is motivated by the search for the production of pairs of Higgs bosons decaying into four bottom quarks. A number of other physical processes, known as background, also share the same final state. The data arising in this problem is therefore a mixture of unlabeled background and signal events, and the primary aim of the analysis is to determine whether the proportion of unlabeled signal events is nonzero. A challenging but necessary first step is to estimate the distribution of background events. Past work in this area has determined regions of the space of collider events where signal is unlikely to appear, and where the background distribution is therefore identifiable. The background distribution can be estimated in these regions, and extrapolated into the region of primary interest using transfer learning of a multivariate classifier. We build upon this existing approach in two ways. On the one hand, we revisit this method by developing a powerful new classifier architecture tailored to collider data. On the other hand, we develop a new method for background estimation, based on the optimal transport problem, which relies on distinct modeling assumptions. These two methods can serve as powerful cross-checks for each other in particle physics analyses, due to the complementarity of their underlying assumptions. We compare their performance on simulated collider data.

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Section: 23mentioning

confidence: 99%

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confidence: 99%

Background Modeling for Double Higgs Boson Production: Density Ratios and Optimal Transport

Manole¹,

Alison²,

Kuusela³

et al. 2022

Preprint

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“…The data-driven estimation of the MJ background is based on the so-called ABCD method [83,84] -a sketch of this method is shown in Figure 7.2. In the ABCD method three control regions (CR) are defined (B, C, and D) by inverting or sequentially relaxing data event selection requirements.…”

Section: General Proceduresmentioning

confidence: 99%

Precision Measurements of Properties of W and Z Bosons with the ATLAS Experiment

Davis-Purcell¹

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The ATLAS Experiment measures the properties of particles created in the highenergy proton-proton collisions delivered by the Large Hadron Collider at CERN. The Standard Model of particle physics is our best description of the subatomic world -this well-studied theory provides accurate and precise descriptions of the fundamental particle properties and their interactions. Multiple areas of the Standard Model make predictions that are more precise than the corresponding experimental measurements -in some cases, increased experimental precision could suggest significant discrepancies between the Standard Model prediction and experimental measurement, potentially hinting at new physics to elucidate. One such area of ten- Contents

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Evidence for four-top quark production in proton-proton collisions at s=13TeV

Tumasyan¹,

Adam²,

Andrejkovic³

et al. 2023

Physics Letters B

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Improved extrapolation methods of data-driven background estimations in high energy physics

Cited by 8 publications

References 15 publications

Background Modeling for Double Higgs Boson Production: Density Ratios and Optimal Transport

Background Modeling for Double Higgs Boson Production: Density Ratios and Optimal Transport

Precision Measurements of Properties of W and Z Bosons with the ATLAS Experiment

Evidence for four-top quark production in proton-proton collisions at s=13TeV

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