Abstract
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.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Belle collaboration, Measurement of the decay B0 → π-ℓ+v and determination of |Vub|, Phys. Rev. D 83 (2011) 071101 [arXiv:1012.0090] [INSPIRE].
BaBar collaboration, Study of B → π-ℓv and B → ρℓv Decays and Determination of |Vub|, Phys. Rev. D 83 (2011) 032007 [arXiv:1005.3288] [INSPIRE].
BaBar collaboration, Branching fraction and form-factor shape measurements of exclusive charmless semileptonic B decays, and determination of |Vub|, Phys. Rev. D 86 (2012) 092004 [arXiv:1208.1253] [INSPIRE].
Belle collaboration, Study of Exclusive B → Xuℓv Decays and Extraction of ||Vub|| using Full Reconstruction Tagging at the Belle Experiment, Phys. Rev. D 88 (2013) 032005 [arXiv:1306.2781] [INSPIRE].
Belle collaboration, Measurement Of |Vub| From Inclusive Charmless Semileptonic B Decays, Phys. Rev. Lett. 104 (2010) 021801 [arXiv:0907.0379] [INSPIRE].
BaBar collaboration, Study of \( \overline{B} \) → Xuℓ\( \overline{v} \) decays in B\( \overline{B} \) events tagged by a fully reconstructed B-meson decay and determination of |Vub|, Phys. Rev. D 86 (2012) 032004 [arXiv:1112.0702] [INSPIRE].
Belle collaboration, Measurements of partial branching fractions of inclusive B → Xuℓ+vℓ decays with hadronic tagging, Phys. Rev. D 104 (2021) 012008 [arXiv:2102.00020] [INSPIRE].
LHCb collaboration, Determination of the quark coupling strength |Vub| using baryonic decays, Nature Phys. 11 (2015) 743 [arXiv:1504.01568] [INSPIRE].
Particle Data Group collaboration, Review of Particle Physics, Phys. Rev. D 98 (2018) 030001 [INSPIRE].
J. Chay, H. Georgi and B. Grinstein, Lepton energy distributions in heav y meson decays from QCD, Phys. Lett. B 247 (1990) 399 [INSPIRE].
I.I.Y. Bigi, N.G. Uraltsev and A.I. Vainshtein, Nonperturbative corrections to inclusive beauty and charm decays: QCD versus phenomenological models, Phys. Lett. B 293 (1992) 430 [Erratum ibid. 297 (1992) 477] [hep-ph/9207214] [INSPIRE].
B. Blok, L. Koyrakh, M.A. Shifman and A.I. Vainshtein, Differential distributions in semileptonic decays of the heavy flavors in QCD, Phys. Rev. D 49 (1994) 3356 [Erratum ibid. 50 (1994) 3572] [hep-ph/9307247] [INSPIRE].
A.V. Manohar and M.B. Wise, Inclusive semileptonic B and polarized Λb decays from QCD, Phys. Rev. D 49 (1994) 1310 [hep-ph/9308246] [INSPIRE].
T. van Ritbergen, The Second order QCD contribution to the semileptonic b → u decay rate, Phys. Lett. B 454 (1999) 353 [hep-ph/9903226] [INSPIRE].
M. Brucherseifer, F. Caola and K. Melnikov, On the O(\( {\alpha}_s^2 \)) corrections to b → Xue\( \overline{\nu} \) inclusive decays, Lett. B 721 (2013) 107 [arXiv:1302.0444] [INSPIRE].
B. Capdevila, P. Gambino and S. Nandi, Perturbative corrections to power suppressed effects in \( \overline{B} \) → Xuℓν, JHEP 04 (2021) 137 [arXiv:2102.03343] [INSPIRE].
M. Neubert, QCD based interpretation of the lepton spectrum in inclusive \( \overline{B} \) → Xuℓ\( \overline{\nu} \) decays, Phys. Rev. D 49 (1994) 3392 [hep-ph/9311325] [INSPIRE].
I.I.Y. Bigi, M.A. Shifman, N.G. Uraltsev and A.I. Vainshtein, On the motion of heavy quarks inside hadrons: Universal distributions and inclusive decays, Int. J. Mod. Phys. A 9 (1994) 2467 [hep-ph/9312359] [INSPIRE].
K.S.M. Lee and I.W. Stewart, Factorization for power corrections to B → Xsγ and B → Xuℓ\( \overline{\nu} \), Nucl. Phys. B 721 (2005) 325 [hep-ph/0409045] [INSPIRE].
S.W. Bosch, M. Neubert and G. Paz, Subleading shape functions in inclusive B decays, JHEP 11 (2004) 073 [hep-ph/0409115] [INSPIRE].
M. Beneke, F. Campanario, T. Mannel and B.D. Pecjak, Power corrections to \( \overline{B} \) → Xuℓ\( \overline{\nu} \)(Xsγ) decay spectra in the ‘shape-function’ region, JHEP 06 (2005) 071 [hep-ph/0411395] [INSPIRE].
C. Greub, M. Neubert and B.D. Pecjak, NNLO corrections to \( \overline{B} \) → Xuℓ\( \overline{\nu} \)ℓ and the determination of |Vub|, Eur. Phys. J. C 65 (2010) 501 [arXiv:0909.1609] [INSPIRE].
U. Aglietti, F. Di Lodovico, G. Ferrera and G. Ricciardi, |Vub| extraction using the Analytic Coupling model, Nucl. Phys. B Proc. Suppl. 185 (2008) 33 [arXiv:0809.4860] [INSPIRE].
S.W. Bosch, B.O. Lange, M. Neubert and G. Paz, Factorization and shape function effects in inclusive B meson decays, Nucl. Phys. B 699 (2004) 335 [hep-ph/0402094] [INSPIRE].
B.O. Lange, M. Neubert and G. Paz, Theory of charmless inclusive B decays and the extraction of Vub, Phys. Rev. D 72 (2005) 073006 [hep-ph/0504071] [INSPIRE].
J.R. Andersen and E. Gardi, Inclusive spectra in charmless semileptonic B decays by dressed gluon exponentiation, JHEP 01 (2006) 097 [hep-ph/0509360] [INSPIRE].
P. Gambino, P. Giordano, G. Ossola and N. Uraltsev, Inclusive semileptonic B decays and the determination of |Vub|, JHEP 10 (2007) 058 [arXiv:0707.2493] [INSPIRE].
A. Crivellin and S. Pokorski, Can the differences in the determinations of Vub and Vcb be explained by New Physics?, Phys. Rev. Lett. 114 (2015) 011802 [arXiv:1407.1320] [INSPIRE].
P. Baldi, P. Sadowski and D. Whiteson, Searching for Exotic Particles in High-Energy Physics with Deep Learning, Nature Commun. 5 (2014) 4308 [arXiv:1402.4735] [INSPIRE].
D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban and D. Whiteson, Jet Flavor Classification in High-Energy Physics with Deep Neural Networks, Phys. Rev. D 94 (2016) 112002 [arXiv:1607.08633] [INSPIRE].
D. Guest, K. Cranmer and D. Whiteson, Deep Learning and its Application to LHC Physics, Ann. Rev. Nucl. Part. Sci. 68 (2018) 161 [arXiv:1806.11484] [INSPIRE].
D.J. Lange, The EvtGen particle decay simulation package, Nucl. Instrum. Meth. A 462 (2001) 152 [INSPIRE].
T. Gleisberg et al., Event generation with SHERPA 1.1, JHEP 02 (2009) 007 [arXiv:0811.4622] [INSPIRE].
T. Sjöstrand, S. Mrenna and P.Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852 [arXiv:0710.3820] [INSPIRE].
T. Sjöstrand et al., An introduction to PYTHIA 8.2, Comput. Phys. Commun. 191 (2015) 159 [arXiv:1410.3012] [INSPIRE].
E. Barberio, B. van Eijk and Z. Was, PHOTOS: A Universal Monte Carlo for QED radiative corrections in decays, Comput. Phys. Commun. 66 (1991) 115 [INSPIRE].
E. Barberio and Z. Was, PHOTOS: A Universal Monte Carlo for QED radiative corrections. Version 2.0, Comput. Phys. Commun. 79 (1994) 291 [INSPIRE].
F. De Fazio and M. Neubert, B → Xuℓ\( \overline{\nu} \)ℓ decay distributions to order αs, JHEP 06 (1999) 017 [hep-ph/9905351] [INSPIRE].
S. Bollweg, M. Haußmann, G. Kasieczka, M. Luchmann, T. Plehn and J. Thompson, Deep-Learning Jets with Uncertainties and More, SciPost Phys. 8 (2020) 006 [arXiv:1904.10004] [INSPIRE].
J. Gallicchio, J. Huth, M. Kagan, M.D. Schwartz, K. Black and B. Tweedie, Multivariate discrimination and the Higgs + W/Z search, JHEP 04 (2011) 069 [arXiv:1010.3698] [INSPIRE].
F. James and M. Roos, Errors on Ratios of Small Numbers of Events, Nucl. Phys. B 172 (1980) 475 [INSPIRE].
TASSO collaboration, A Detailed Study of Strange Particle Production in e+e− Annihilation at High-energy, Z. Phys. C 27 (1985) 27 [INSPIRE].
JADE collaboration, Charged Particle and Neutral Kaon Production in e+e− Annihilation at PETRA, Z. Phys. C 20 (1983) 187 [INSPIRE].
P. Skands, S. Carrazza and J. Rojo, Tuning PYTHIA 8.1: the Monash 2013 Tune, Eur. Phys. J. C 74 (2014) 3024 [arXiv:1404.5630] [INSPIRE].
BaBar collaboration, The BABAR Detector: Upgrades, Operation and Performance, Nucl. Instrum. Meth. A 729 (2013) 615 [arXiv:1305.3560] [INSPIRE].
N. Gagliardi, Measurements of Partial Branching Fractions for Charmless Semileptonic B Decays with the BaBar Experiment and Determination of Vub, Ph.D. Thesis, Università di Padova (2009).
BaBar collaboration, The First year of the BaBar experiment at PEP-II, in 30th International Conference on High-Energy Physics, (2000) [hep-ex/0012042] [INSPIRE].
M. Abadi et al., TensorFlow: Large-scale machine learning on heterogeneous systems, https://www.tensorflow.org/ (2015).
J.V. Dillon et al., Tensorflow distributions, CoRR abs/1711.10604 (2017) [arXiv:1711.10604].
F. Chollet et al., Keras, https://keras.io (2015).
Y. Wen, P. Vicol, J. Ba, D. Tran and R.B. Grosse, Flipout: Efficient pseudo-independent weight perturbations on mini-batches, CoRR abs/1803.04386 (2018) [arXiv:1803.04386].
A. Graves, Practical variational inference for neural networks, in Advances in Neural Information Processing Systems, J. Shawe-Taylor et al. eds., vol. 24, Curran Associates, Inc. (2011).
D.P. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, CoRR abs/1412.6980 (2015) [arXiv:1412.6980] [INSPIRE].
T. Chen and C. Guestrin, XGBoost: A Scalable Tree Boosting System, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, New York, NY, U.S.A., pp. 785–794, ACM (2016) [DOI] [arXiv:1603.02754] [INSPIRE].
J. Bergstra, D. Yamins and D.D. Cox, Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms. Comput. Sci. Discov. 8 (2015) 014008.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
ArXiv ePrint: 2105.09271
Rights and permissions
Open Access . This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.
About this article
Cite this article
Biekötter, A., Kwok, K.W. & Pecjak, B.D. Potential and limitations of machine-learning approaches to inclusive |Vub| determinations. J. High Energ. Phys. 2022, 143 (2022). https://doi.org/10.1007/JHEP01(2022)143
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/JHEP01(2022)143