Measurements of partial branching fractions of inclusive 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>B</mml:mi><mml:mo stretchy="false">→</mml:mo><mml:msub><mml:mi>X</mml:mi><mml:mi>u</mml:mi></mml:msub><mml:msup><mml:mo>ℓ</mml:mo><mml:mo>+</mml:mo></mml:msup><mml:msub><mml:mi>ν</mml:mi><mml:mo>ℓ</mml:mo></mml:msub></mml:math>
 decays with hadronic tagging

Cao, L.; Sutcliffe, W.; Tonder, R. Van; Bernlochner, F. U.; Adachi, I.; Aihara, H.; Said, S. Al; Asner, D. M.; Atmacan, H.; Aushev, T.; Ayad, R.; Babu, V.; Bauer, M.; Behera, P. K.; Belous, K.; Bennett, J. V.; Bessner, M.; Bhardwaj, V.; Bilka, T.; Biswal, Jyoti Prakash; Bonvicini, G.; Bożek, A.; Bračko, M.; Browder, T. E.; Campajola, M.; Červenkov, D.; Chang, M.-C.; Chang, P.; Chekelian, V.; Chen, A.; Cheon, B. G.; Chilikin, K.; Cho, H. E.; Cho, K.; Cho, S.-J.; Choi, S. K.; Choi, Y.; Choudhury, S.; Cinabro, D.; Cunliffe, S.; Das, Santosh K.; Dash, N.; Nardo, G. De; Capua, F. Di; Dingfelder, J.; Doležal, Z.; Dong, T. V.; Dubey, S.; Eidelman, S.; Epifanov, D.; Ferber, T.; Ferlewicz, D.; Frey, A.; Fulsom, B. G.; Garg, Rocky Bala; Gaur, V.; Garmash, A.; Giri, A.; Goldenzweig, P.; Guan, Y. H.; Hadjivasiliou, C.; Hara, T.; Hartbrich, O.; Hayasaka, K.; Hayashii, H.; Hedges, M. T.; Villanueva, M. Hernández; Hou, W.-S.; Hsu, C.-L.; Iijima, T.; Inami, K.; Ishikawa, A.; Itoh, R.; Iwasaki, M.; Iwasaki, Y.; Jacobs, W. W.; Jang, E.-J.; Jeon, H. B.; Jia, S.; Jin, Y.; Joo, C.; Joo, K. K.; Kang, K. H.; Karyan, G.; Kawasaki, T.; Kichimi, H.; Kiesling, C.; Kim, B. H.; Kim, C. H.; Kim, D. Y.; Kim, H. J.; Kim, K.-H.; Kim, S. H.; Kim, Y.-K.; Kinoshita, K.; Kodyš, P.; Konno, T.; Korobov, A.; Korpar, S.; Kotchetkov, D.; Kovalenko, E.; Križan, P.; Kroeger, R.; Krokovny, P.; Kuhr, T.; Kumar, M.; Kumar, Ramanuj; Kumara, K.; Kuzmin, A.; Kwon, Y. J.; Lalwani, K.; Lange, J. S.; Lee, I. S.; Lee, S. C.; Lewis, P.; Li, C. H.; Li, J.; Li, L. K.; Li, Y. B.; Gioi, L. Li; Libby, J.; Lieret, K.; Liptak, Z.; Liventsev, D.; MacNaughton, J.; MacQueen, C.; Masuda, M.; Matsuda, T.; Matvienko, D.; Merola, M.; Metzner, F.; Miyabayashi, K.; Mizuk, R.; Mohanty, G. B.; Moon, T. J.; Mori, Takashi; Mrvar, M.; Mussa, R.; Nakao, M.; Natkaniec, Z.; Natochii, A.; Nayak, L.; Nayak, M.; Niiyama, M.; Nisar, N. K.; Nishida, S.; Nishimura, K.; Ogawa, S.; Ono, Hiroyuki; Onuki, Y.; Oskin, P.; Pakhlov, P.; Pakhlova, G.; Pang, T.; Pardi, S.; Park, C. W.; Park, H.; Park, S.-H.; Patra, S.; Paul, S.; Pedlar, T. K.; Pestotnik, R.; Piilonen, L. E.; Podobnik, T.; Popov, V.; Prencipe, E.; Prim, M. T.; Ritter, M.; Röhrken, M.; Rostomyan, A.; Rout, N.; Różańska, M.; Russo, G.; Sahoo, Deepak Kumar; Sakai, Y.; Sandilya, S.; Sangal, A.; Šantelj, L.; Sanuki, T.; Savinov, V.; Schnell, G.; Schueler, J.; Schwanda, C.; Schwartz, A. J.; Seino, Y.; Senyo, K.; Sevior, M. E.; Shapkin, M.; Sharma, C.; Shen, C. P.; Shiu, J. G.; Simon, F.; Sokolov, A.; Solovieva, E.; Stanič, S.; Starič, M.; Stottler, Z. S.; Strube, J.; Sumiyoshi, T.; Takizawa, M.; Tamponi, U.; Tanida, K.; Tenchini, F.; Trabelsi, K.; Uchida, M.; Uglov, T.; Unno, Y.; Uno, S.; Urquijo, P.; Usov, Y.; Vahsen, S. E.; Varner, G.; Varvell, K. E.; Vinokurova, A.; Vorobyev, V.; Vossen, A.; Waheed, E.; Wang, C. H.; Wang, E.; Wang, M.-Z.; Wang, P.; Watanabe, M.; Watanuki, S.; Wehle, S.; Wiechczynski, J.; Won, E.; Xu, Xiaolong; Yabsley, B. D.; Yan, W. B.; Yang, S. B.; Ye, H.; Yin, J. H.; Yuan, C. Z.; Yusa, Y.; Zhang, Z. P.; Zhilich, V.; Zhukova, V.; Zhulanov, V.

doi:10.1103/physrevd.104.012008

Cited by 24 publications

(44 citation statements)

References 74 publications

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“…A possible approach to evading this criticism was presented recently in the reanalysis of the Belle data in ref. [7], where kinematic properties were not included as input features in a BDT classifier. Although the classification power of such a BDT is reduced when viewed in terms of typical machine-learning metrics such as the area under the curve, it can be used to enhance the signal-to-background ratio to a level which permits a binned one-and two-dimensional likelihood analyses of the kinematic features of the signal and background after event selection resulting in a similar significance after the likelihood analysis.…”

Section: Jhep01(2022)143mentioning

confidence: 99%

“…We have chosen this set of high-level features to mimic the feature selection in the BDT analyses performed by Belle in refs. [5,7]. Some differences with respect to the sets used in those papers arise, because we do not have access to all experimental features in our simplified detector simulation, for instance features related to the quality of the signal reconstruction.…”

Section: Input Featuresmentioning

confidence: 99%

“…The determination of the parameters of the CKM matrix is an important test of the Standard Model (SM). Its least known element is |V ub |, which can be determined at B-factories from semileptonic B-decays in the exclusive B → π ν channel [1][2][3][4] as well as from inclusive B → X u ν decays [5][6][7]. Moreover, it can be tested at the LHCb experiment in Λ b → pµν µ decays [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Potential and limitations of machine-learning approaches to inclusive |Vub| determinations

Biekötter

Kwok

Pecjak

2022

J. High Energ. Phys.

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The determination of |Vub| in inclusive semileptonic B → Xuℓν decays will be among the pivotal tasks of Belle II. In this paper we study the potential and limitations of machine-learning approaches that attempt to reduce theory uncertainties by extending the experimentally accessible fiducial region of the B → Xuℓν signal into regions where the B → Xcℓν background is dominant. We find that a deep neural network trained on low-level single particle features offers modest improvement in separating signal from background, compared to BDT set-ups using physicist-engineered high-level features. We further illustrate that while the signal acceptance of such a deep neural network deteriorates in kinematic regions where the signal is small, such as at high hadronic invariant mass, neural networks which exclude kinematic features are flatter in kinematics but less inclusive in the sampling of exclusive hadronic final states at fixed kinematics. The trade-off between these two set-ups is somewhat Monte Carlo dependent, and we study this issue using the multipurpose event generator Sherpa in addition to the widely used B-physics tool EvtGen.

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Section: Jhep01(2022)143mentioning

confidence: 99%

Section: Input Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Potential and limitations of machine-learning approaches to inclusive |Vub| determinations

Biekötter

Kwok

Pecjak

2022

J. High Energ. Phys.

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“…This DFN model contains α s corrections convoluted with the non-perturbative shape function in an ad-hoc exponential model [29]. The two parameters of this shape function in the Kagan-Neubert scheme are taken from a fit to B → X c ν and B → X s γ data [30] (see also [31]). In the figures, the points labelled DFN present the central values of the DFN, while…”

Section: Comparison Between Theory and Monte-carlomentioning

confidence: 99%

“…These variations can be used to estimate the error of the DFN model. This method, using the variation of the DFN models as an error, is used at Belle (see also the recent Belle analysis of theB → X u ν [31]). For both the DFN and the BLNP models, resonant contributions are included using a "hybrid Monte Carlo".…”

Section: Comparison Between Theory and Monte-carlomentioning

confidence: 99%

Impact of background effects on the inclusive Vcb determination

Mannel

Rahimi

Vos

2021

J. High Energ. Phys.

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The determination of the CKM element Vcb from inclusive semileptonic b → cℓ$$ \overline{\nu} $$ ν ¯ decays has reached a high precision thanks to a combination of theoretical and experimental efforts. Aiming towards even higher precision, we discuss two processes that contaminate the inclusive Vcb determination; the b → u background and the contribution of the tauonic mode: b → c(τ → μν$$ \overline{\nu} $$ ν ¯ )$$ \overline{\nu} $$ ν ¯ . Both of these contributions are dealt with at the experimental side, using Monte-Carlo methods and momentum cuts. However, these contributions can be calculated with high precision within the Heavy-Quark Expansion. In this note, we calculate the theoretical predictions for these two processes. We compare our b → u results qualitatively with generator-level Monte-Carlo data used at Belle and Belle II. Finally, we suggest to change the strategy for the extraction of Vcb by comparing the data on B → Xℓ directly with the theoretical expressions, to which our paper facilitates.

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Recent progress in decays of b and c hadrons

Bharucha

2022

Indian J Phys

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Measurements of partial branching fractions of inclusive B→Xuℓ+νℓ decays with hadronic tagging

Cited by 24 publications

References 74 publications

Potential and limitations of machine-learning approaches to inclusive |Vub| determinations

Potential and limitations of machine-learning approaches to inclusive |Vub| determinations

Impact of background effects on the inclusive Vcb determination

Recent progress in decays of b and c hadrons

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