-
The ratio
$ \cal{R} $ of the$ {{{H_{bc}^{0}}}} $ baryon production cross-section multiplied by the branching fraction of the$ {{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varLambda}^+_c}}}{{{{\pi}^-}}}}}} ({{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varXi}^+_c}}}{{{{\pi}^-}}}}}})$ decay relative to that of the$ {{{{\varLambda}^0_b}}} $ ($ {{{{\varXi}_{b}^{0}}}} $ ) baryon can be written as$ \begin{aligned}[b] {\cal{R}}({{{{\varLambda}^+_c}}}{{{{\pi}^-}}})\equiv &\frac{\sigma(pp {{\rightarrow }} {{{H_{bc}^{0}}}} X)\ {{{{\mathcal{B}}}}}\left( {{{{{{H_{bc}^{0}}}}{{\rightarrow }}{{{{\varLambda}^+_c}}}({{\rightarrow }}\ p {{{K^-}}} {{{{\pi}^+}}}){{{{\pi}^-}}}}}} \right)}{\sigma(pp {{\rightarrow }} {{{{\varLambda}^0_b}}} X)\ {{{{\mathcal{B}}}}}\left( {{{{{{{\varLambda}^0_b}}}{{\rightarrow }}{{{{\varLambda}^+_c}}}({{\rightarrow }}\ p {{{K^-}}} {{{{\pi}^+}}}){{{{\pi}^-}}}}}} \right)}\\ =& \frac{N({{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varLambda}^+_c}}}{{{{\pi}^-}}}}}})}{N({{{{{{{\varLambda}^0_b}}}{{\rightarrow }}{{{{\varLambda}^+_c}}}{{{{\pi}^-}}}}}})}\cdot \frac{\varepsilon({{{{\varLambda}^0_b}}})}{\varepsilon({{{H_{bc}^{0}}}})}, \end{aligned} $
(1) $ \begin{aligned}[b] {\cal{R}}({{{{\varXi}^+_c}}}{{{{\pi}^-}}})\equiv &\frac{\sigma(pp {{\rightarrow }} {{{H_{bc}^{0}}}} X)\ {{{{\mathcal{B}}}}}\left( {{{{{{H_{bc}^{0}}}}{{\rightarrow }}{{{{\varXi}^+_c}}}({{\rightarrow }}\ p {{{K^-}}} {{{{\pi}^+}}}){{{{\pi}^-}}}}}} \right)}{\sigma(pp {{\rightarrow }} {{{{\varXi}_{b}^{0}}}} X)\ {{{{\mathcal{B}}}}}\left( {{{{{{{\varXi}_{b}^{0}}}}{{\rightarrow }}{{{{\varXi}^+_c}}}({{\rightarrow }}\ p {{{K^-}}} {{{{\pi}^+}}}){{{{\pi}^-}}}}}} \right)} \\ = & \frac{N({{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varXi}^+_c}}}{{{{\pi}^-}}}}}})}{N({{{{{{{\varXi}_{b}^{0}}}}{{\rightarrow }}{{{{\varXi}^+_c}}}{{{{\pi}^-}}}}}})}\cdot \frac{\varepsilon({{{{\varXi}_{b}^{0}}}})}{\varepsilon({{{H_{bc}^{0}}}})}, \end{aligned} $
(2) where N and
$ \varepsilon $ are the signal yield and the efficiency for the corresponding decay modes, respectively. The efficiency accounts for the geometrical acceptance, trigger, reconstruction, and event selection. The$ \cal{R} $ is determined in the fiducial region$ 2<y<4.5 $ and$ 2<{{p_{\rm{T}}}}<20 \;{\rm{MeV}}/{c} $ .Efficiencies are determined from the simulated samples. The
$ {{p_{\rm{T}}}} $ distributions of$ {{{{\varLambda}^0_b}}} $ and$ {{{{\varXi}_{b}^{0}}}} $ baryons are not well modeled in simulation. To improve the description, a gradient boosted weighting method [57] is used to apply a kinematic correction on the$ {{p_{\rm{T}}}} $ distributions of the$ {{{{\varLambda}^0_b}}} $ and$ {{{{\varXi}_{b}^{0}}}} $ decay products of the simulated control samples. With this correction, a good agreement on the$ {{{{\varLambda}^0_b}}} $ and$ {{{{\varXi}_{b}^{0}}}} $ $ {{p_{\rm{T}}}} $ distribution is seen between the data and simulation. The track detection and particle identification efficiencies are calibrated with the data [58-60]. The imperfect modeling of input variables used in the BDT training can bias the efficiency estimation. To suppress such effects, ratios between the BDT response distribution of the background-subtracted data sample and that of the simulated sample are calculated using the control channel. The background subtraction is performed using the$ {sPlot} $ method [61] with$ m({{{{\varLambda}^+_c}}}{{{{\pi}^-}}}) $ and$ m({{{{\varXi}^+_c}}}{{{{\pi}^-}}}) $ as discriminating variables. These ratios are applied as correction weights to the simulated samples for all reconstructed decay modes.The total efficiency ratio
$ \varepsilon({{{{\varLambda}^0_b}}})/\varepsilon({{{H_{bc}^{0}}}}) $ is determined to be$ 3.18\pm0.05 $ , and$ \varepsilon({{{{\varXi}_{b}^{0}}}})/\varepsilon({{{H_{bc}^{0}}}}) $ is calculated to be$ 3.00\pm0.02 $ for$ m({{{H_{bc}^{0}}}}) = 6900 \;{\rm{MeV}}/{c^2} $ and$ \tau({{{H_{bc}^{0}}}}) = 0.4\;{\rm{ps}} $ . The efficiency is larger for the control mode, mainly due to the longer lifetime of the$ {{{{\varLambda}^0_b}}} $ and$ {{{{\varXi}_{b}^{0}}}} $ baryons. The efficiency depends on the mass and lifetime hypotheses of the$ {{{H_{bc}^{0}}}} $ state, and is evaluated from simulation. The kinematic properties of the fully simulated samples are weighted to match those of the generator-level sample to calculate the efficiency for different$ {{{H_{bc}^{0}}}} $ mass and lifetime assumptions. -
Various sources of systematic uncertainties on
$ {\cal{R}} $ are estimated and combined in quadrature. The effect of imperfect description of the mass distributions on the yield estimates is studied using alternative signal and background models. For the signal model, the Hypatia [62] function is used instead of the nominal double-sided Crystal Ball function. For the background model of the control modes, the nominal double-exponential function is replaced by a first-order polynomial function. As the background model for the$ {{{H_{bc}^{0}}}} $ decay modes is interpolated from the sidebands, its uncertainty is evaluated by both replacing the nominal function with an exponential function and varying the sideband regions. The largest deviation with respect to the nominal result is taken as the corresponding uncertainty. In total, the associated systematic uncertainty is estimated to be 0.1% and 0.9% for$ {{{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varLambda}^+_c}}}{{{{\pi}^-}}}}}}} $ and$ {{{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varXi}^+_c}}}{{{{\pi}^-}}}}}}} $ decays, respectively.In the
$ {\cal{R}} $ ratios, systematic uncertainties arising from the track detection efficiency largely cancel, and the uncertainty due to limited size of simulation samples is determined to be 1.6% (0.7%) on$ {\cal{R}} ({{{{\varLambda}^+_c}}}{{{{\pi}^-}}}) $ ($ {\cal{R}} ({{{{\varXi}^+_c}}}{{{{\pi}^-}}}) $ ). The particle identification efficiency is determined in bins of particle momentum, pseudorapidity, and track multiplicity using control channels in the data [60]. As the particle identification variables have large dependencies on the momentum of the final-state particles, there are sizeable differences in these efficiencies between the control and signal channels, which do not cancel in the ratio measurement. Systematic effects arising from the choice of binning scheme are evaluated by varying the bin sizes and reevaluating the efficiency. The largest deviations from the nominal result, 1.7% and 2.1%, are assigned as the systematic uncertainty for the$ {{{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varLambda}^+_c}}}{{{{\pi}^-}}}}}}} $ and$ {{{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varXi}^+_c}}}{{{{\pi}^-}}}}}}} $ decays, respectively.The
$ {{{{\varLambda}^+_c}}} $ ($ {{{{\varXi}^+_c}}} $ ) mass resolution shows a difference between data and simulation, which affects the selection efficiency. It results in a 0.2% systematic uncertainty contribution for the$ {{{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varXi}^+_c}}}{{{{\pi}^-}}}}}}} $ decay, while the contribution for the$ {{{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varLambda}^+_c}}}{{{{\pi}^-}}}}}}} $ decay is below 0.1%. This systematic uncertainty is negligible compared to other sources.The imperfect simulation of the signal and control modes are considered by applying corrections to the BDT response and kinematic properties of the simulated control mode samples. To assess the systematic uncertainty in these corrections, the correcting weights are varied within their uncertainties. The largest deviation from the nominal result is taken as the systematic uncertainty. Combining the uncertainties from the BDT response correction and the kinematic modeling of the simulated control samples gives an uncertainty of 1.6% for the
$ {{{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varLambda}^+_c}}}{{{{\pi}^-}}}}}}} $ channel, and 3.0% for the$ {{{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varXi}^+_c}}}{{{{\pi}^-}}}}}}} $ channel. The analysis relies on the$ {{{{\varXi}_{bc}^{0}}}} $ $ {{p_{\rm{T}}}} $ model implemented in simulation. No systematic uncertainty is assigned to this model.The algorithm used to compute the
$ {\chi^2_{\rm{IP}}} $ was updated during data collection, which causes a mismatch between data and simulation and introduces systematic effects in the efficiency estimation. The corresponding uncertainty was found to be 5% in the previous$ {{{{\varXi}_{bc}^{0}}}} $ search [29]. Checks by varying the$ {\chi^2_{\rm{IP}}} $ -related requirements show that the uncertainty well covers the change of result. Therefore, a 5% systematic uncertainty is assigned.The systematic uncertainties are summarized in Table 1. The total systematic uncertainty is 5.7% for
$ {{{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varLambda}^+_c}}}{{{{\pi}^-}}}}}}} $ and 6.3% for$ {{{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varXi}^+_c}}}{{{{\pi}^-}}}}}}} $ , for a$ {{{H_{bc}^{0}}}} $ mass of 6900$ \;{\rm{MeV}}/{c^2} $ and lifetime of 0.4$ \;{\rm{ps}} $ . These values of systematic uncertainties are also used for other assumed lifetime and mass hypotheses.${{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varLambda}^+_c}}}{{{{\pi}^-}}}}}}$ ${{{{{{H_{bc}^{0}}}} {{\rightarrow }}{{{{\varXi}^+_c}}}{{{{\pi}^-}}}}}}$ Fit model 0.1% 0.9% Size of simulated samples 1.6% 0.7% Particle identification efficiency 1.7% 2.1% Mass resolution <0.1% 0.2% Simulation model 1.6% 3.0% $ {\chi^2_{\rm{IP}}} $ simulation5.0% 5.0% Total 5.7% 6.3% Table 1. Sources of systematic uncertainty obtained for an
$ {{{H_{bc}^{0}}}} $ mass of 6900$ \;{\rm{MeV}}/{c^2} $ and lifetime of 0.4$ \;{\rm{ps}} $ . The total is the quadratic sum of the individual systematic uncertainties.
Search for the doubly heavy baryons ${\boldsymbol \varOmega_{\boldsymbol{bc}}^{\bf 0}} $ and $ {\boldsymbol\varXi_{\boldsymbol{bc}}^{\bf 0} }$ decaying to ${ \boldsymbol \varLambda_c^+\pi^- }$ and $ {\boldsymbol\varXi_c^+\pi^- }$
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- (LHCb Collaboration) ,
- 1. Centro Brasileiro de Pesquisas Físicas (CBPF), Rio de Janeiro, Brazil
- 2. Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
- 3. Center for High Energy Physics, Tsinghua University, Beijing, China
- 4. Institute Of High Energy Physics (IHEP), Beijing, China
- 5. School of Physics State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing, China
- 6. University of Chinese Academy of Sciences, Beijing, China
- 7. Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China
- 8. Univ. Savoie Mont Blanc, CNRS, IN2P3-LAPP, Annecy, France
- 9. Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
- 10. Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France
- 11. Université Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
- 12. Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, France
- 13. LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Cité, CNRS/IN2P3, Paris, France
- 14. I. Physikalisches Institut, RWTH Aachen University, Aachen, Germany
- 15. Fakultät Physik, Technische Universität Dortmund, Dortmund, Germany
- 16. Max-Planck-Institut für Kernphysik (MPIK), Heidelberg, Germany
- 17. Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg, Germany
- 18. School of Physics, University College Dublin, Dublin, Ireland
- 19. INFN Sezione di Bari, Bari, Italy
- 20. INFN Sezione di Bologna, Bologna, Italy
- 21. INFN Sezione di Ferrara, Ferrara, Italy
- 22. INFN Sezione di Firenze, Firenze, Italy
- 23. INFN Laboratori Nazionali di Frascati, Frascati, Italy
- 24. INFN Sezione di Genova, Genova, Italy
- 25. INFN Sezione di Milano, Milano, Italy
- 26. INFN Sezione di Milano-Bicocca, Milano, Italy
- 27. INFN Sezione di Cagliari, Monserrato, Italy
- 28. Universita degli Studi di Padova, Universita e INFN, Padova, Padova, Italy
- 29. INFN Sezione di Pisa, Pisa, Italy
- 30. INFN Sezione di Roma La Sapienza, Roma, Italy
- 31. INFN Sezione di Roma Tor Vergata, Roma, Italy
- 32. Nikhef National Institute for Subatomic Physics, Amsterdam, Netherlands
- 33. Nikhef National Institute for Subatomic Physics and VU University Amsterdam, Amsterdam, Netherlands
- 34. AGH - University of Science and Technology, Faculty of Physics and Applied Computer Science, Kraków, Poland
- 35. Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences, Kraków, Poland
- 36. National Center for Nuclear Research (NCBJ), Warsaw, Poland
- 37. Horia Hulubei National Institute of Physics and Nuclear Engineering, Bucharest-Magurele, Romania
- 38. Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI), Gatchina, Russia
- 39. Institute for Nuclear Research of the Russian Academy of Sciences (INR RAS), Moscow, Russia
- 40. Institute of Nuclear Physics, Moscow State University (SINP MSU), Moscow, Russia
- 41. Institute of Theoretical and Experimental Physics NRC Kurchatov Institute (ITEP NRC KI), Moscow, Russia
- 42. Yandex School of Data Analysis, Moscow, Russia
- 43. Budker Institute of Nuclear Physics (SB RAS), Novosibirsk, Russia
- 44. Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI), Protvino, Russia, Protvino, Russia
- 45. ICCUB, Universitat de Barcelona, Barcelona, Spain
- 46. Instituto Galego de Física de Altas Enerxías (IGFAE), Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- 47. Instituto de Fisica Corpuscular, Centro Mixto Universidad de Valencia - CSIC, Valencia, Spain
- 48. European Organization for Nuclear Research (CERN), Geneva, Switzerland
- 49. Institute of Physics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- 50. Physik-Institut, Universität Zürich, Zürich, Switzerland
- 51. NSC Kharkiv Institute of Physics and Technology (NSC KIPT), Kharkiv, Ukraine
- 52. Institute for Nuclear Research of the National Academy of Sciences (KINR), Kyiv, Ukraine
- 53. University of Birmingham, Birmingham, United Kingdom
- 54. H.H. Wills Physics Laboratory, University of Bristol, Bristol, United Kingdom
- 55. Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom
- 56. Department of Physics, University of Warwick, Coventry, United Kingdom
- 57. STFC Rutherford Appleton Laboratory, Didcot, United Kingdom
- 58. School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom
- 59. School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom
- 60. Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom
- 61. Imperial College London, London, United Kingdom
- 62. Department of Physics and Astronomy, University of Manchester, Manchester, United Kingdom
- 63. Department of Physics, University of Oxford, Oxford, United Kingdom
- 64. Massachusetts Institute of Technology, Cambridge, MA, United States
- 65. University of Cincinnati, Cincinnati, OH, United States
- 66. University of Maryland, College Park, MD, United States
- 67. Los Alamos National Laboratory (LANL), Los Alamos, United States
- 68. Syracuse University, Syracuse, NY, United States
- 69. School of Physics and Astronomy, Monash University, Melbourne, Australia, associated to 56
- 70. Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil, associated to 2
- 71. Physics and Micro Electronic College, Hunan University, Changsha City, China, associated to 7
- 72. Guangdong Provencial Key Laboratory of Nuclear Science, Institute of Quantum Matter, South China Normal University, Guangzhou, China, associated to 3
- 73. School of Physics and Technology, Wuhan University, Wuhan, China, associated to 3
- 74. Departamento de Fisica, Universidad Nacional de Colombia, Bogota, Colombia, associated to 13
- 75. Universität Bonn - Helmholtz-Institut für Strahlen und Kernphysik, Bonn, Germany, associated to 17
- 76. Institut für Physik, Universität Rostock, Rostock, Germany, associated to17
- 77. INFN Sezione di Perugia, Perugia, Italy, associated to 21
- 78. Van Swinderen Institute, University of Groningen, Groningen, Netherlands, associated to 32
- 79. Universiteit Maastricht, Maastricht, Netherlands, associated to 32
- 80. National Research Centre Kurchatov Institute, Moscow, Russia, associated to 41
- 81. National Research University Higher School of Economics, Moscow, Russia, associated to 42
- 82. National University of Science and Technology “MISIS”, Moscow, Russia, associated to 41
- 83. National Research Tomsk Polytechnic University, Tomsk, Russia, associated to 41
- 84. DS4DS, La Salle, Universitat Ramon Llull, Barcelona, Spain, associated to 45
- 85. University of Michigan, Ann Arbor, United States, associated to 68
- a. Universidade Federal do Triângulo Mineiro (UFTM), Uberaba-MG, Brazil
- b. Hangzhou Institute for Advanced Study, UCAS, Hangzhou, China
- c. Università di Bari, Bari, Italy
- d. Università di Bologna, Bologna, Italy
- e. Università di Cagliari, Cagliari, Italy
- f. Università di Ferrara, Ferrara, Italy
- g. Università di Firenze, Firenze, Italy
- h. Università di Genova, Genova, Italy
- i. Università degli Studi di Milano, Milano, Italy
- j. Università di Milano Bicocca, Milano, Italy
- k. Università di Modena e Reggio Emilia, Modena, Italy
- l. Università di Padova, Padova, Italy
- m. Scuola Normale Superiore, Pisa, Italy
- n. Università di Pisa, Pisa, Italy
- o. Università della Basilicata, Potenza, Italy
- p. Università di Roma Tor Vergata, Roma, Italy
- q. Università di Siena, Siena, Italy
- r. Università di Urbino, Urbino, Italy
- s. MSU - Iligan Institute of Technology (MSU-IIT), Iligan, Philippines
- t. AGH - University of Science and Technology, Faculty of Computer Science, Electronics and Telecommunications, Kraków, Poland
- u. P.N. Lebedev Physical Institute, Russian Academy of Science (LPI RAS), Moscow, Russia
- v. Novosibirsk State University, Novosibirsk, Russia
- w. Department of Physics and Astronomy, Uppsala University, Uppsala, Sweden
- x. Hanoi University of Science, Hanoi, Vietnam
- Received Date: 2021-05-27
- Available Online: 2021-09-15
Abstract: The first search for the doubly heavy