Measurement of the branching fraction of the doubly Cabibbo-suppressed decay and search for
M. Ablikim1, M. N. Achasov10,b, P. Adlarson68, M. Albrecht4, R. Aliberti28, A. Amoroso67A,67C, M. R. An32, Q. An64,50, X. H. Bai58, Y. Bai49, O. Bakina29, R. Baldini Ferroli23A, I. Balossino24A, Y. Ban39,g, V. Batozskaya1,37, D. Becker28, K. Begzsuren26, N. Berger28, M. Bertani23A, D. Bettoni24A, F. Bianchi67A,67C, J. Bloms61, A. Bortone67A,67C, I. Boyko29, R. A. Briere5, A. Brueggemann61, H. Cai69, X. Cai1,50, A. Calcaterra23A, G. F. Cao1,55, N. Cao1,55, S. A. Cetin54A, J. F. Chang1,50, W. L. Chang1,55, G. Chelkov29,a, C. Chen36, G. Chen1, H. S. Chen1,55, M. L. Chen1,50, S. J. Chen35, T. Chen1, X. R. Chen25,55, X. T. Chen1, Y. B. Chen1,50, Z. J. Chen20,h, W. S. Cheng67C, G. Cibinetto24A, F. Cossio67C, J. J. Cui42, H. L. Dai1,50, J. P. Dai71, A. Dbeyssi14, R. E. de Boer4, D. Dedovich29, Z. Y. Deng1, A. Denig28, I. Denysenko29, M. Destefanis67A,67C, F. De Mori67A,67C, Y. Ding33, J. Dong1,50, L. Y. Dong1,55, M. Y. Dong1,50,55, X. Dong69, S. X. Du73, P. Egorov29,a, Y. L. Fan69, J. Fang1,50, S. S. Fang1,55, Y. Fang1, R. Farinelli24A, L. Fava67B,67C, F. Feldbauer4, G. Felici23A, C. Q. Feng64,50, J. H. Feng51, K Fischer62, M. Fritsch4, C. D. Fu1, H. Gao55, Y. N. Gao39,g, Yang Gao64,50, S. Garbolino67C, I. Garzia24A,24B, P. T. Ge69, Z. W. Ge35, C. Geng51, E. M. Gersabeck59, A Gilman62, K. Goetzen11, L. Gong33, W. X. Gong1,50, W. Gradl28, M. Greco67A,67C, L. M. Gu35, M. H. Gu1,50, C. Y Guan1,55, A. Q. Guo25,55, L. B. Guo34, R. P. Guo41, Y. P. Guo9,f, A. Guskov29,a, T. T. Han42, W. Y. Han32, X. Q. Hao15, F. A. Harris57, K. K. He47, K. L. He1,55, F. H. Heinsius4, C. H. Heinz28, Y. K. Heng1,50,55, C. Herold52, M. Himmelreich11,d, T. Holtmann4, G. Y. Hou1,55, Y. R. Hou55, Z. L. Hou1, H. M. Hu1,55, J. F. Hu48,i, T. Hu1,50,55, Y. Hu1, G. S. Huang64,50, K. X. Huang51, L. Q. Huang65, L. Q. Huang25,55, X. T. Huang42, Y. P. Huang1, Z. Huang39,g, T. Hussain66, N Hüsken22,28, W. Imoehl22, M. Irshad64,50, J. Jackson22, S. Jaeger4, S. Janchiv26, Q. Ji1, Q. P. Ji15, X. B. Ji1,55, X. L. Ji1,50, Y. Y. Ji42, Z. K. Jia64,50, H. B. Jiang42, S. S. Jiang32, X. S. Jiang1,50,55, Y. Jiang55, J. B. Jiao42, Z. Jiao18, S. Jin35, Y. Jin58, M. Q. Jing1,55, T. Johansson68, N. Kalantar-Nayestanaki56, X. S. Kang33, R. Kappert56, M. Kavatsyuk56, B. C. Ke73, I. K. Keshk4, A. Khoukaz61, P. Kiese28, R. Kiuchi1, R. Kliemt11, L. Koch30, O. B. Kolcu54A, B. Kopf4, M. Kuemmel4, M. Kuessner4, A. Kupsc37,68, W. Kühn30, J. J. Lane59, J. S. Lange30, P. Larin14, A. Lavania21, L. Lavezzi67A,67C, Z. H. Lei64,50, H. Leithoff28, M. Lellmann28, T. Lenz28, C. Li40, C. Li36, C. H. Li32, Cheng Li64,50, D. M. Li73, F. Li1,50, G. Li1, H. Li44, H. Li64,50, H. B. Li1,55, H. J. Li15, H. N. Li48,i, J. Q. Li4, J. S. Li51, J. W. Li42, Ke Li1, L. J Li1, L. K. Li1, Lei Li3, M. H. Li36, P. R. Li31,j,k, S. X. Li9, S. Y. Li53, T. Li42, W. D. Li1,55, W. G. Li1, X. H. Li64,50, X. L. Li42, Xiaoyu Li1,55, H. Liang1,55, H. Liang64,50, H. Liang27, Y. F. Liang46, Y. T. Liang25,55, G. R. Liao12, L. Z. Liao42, J. Libby21, A. Limphirat52, C. X. Lin51, D. X. Lin25,55, T. Lin1, B. J. Liu1, C. X. Liu1, D. Liu14,64, F. H. Liu45, Fang Liu1, Feng Liu6, G. M. Liu48,i, H. M. Liu1,55, Huanhuan Liu1, Huihui Liu16, J. B. Liu64,50, J. L. Liu65, J. Y. Liu1,55, K. Liu1, K. Y. Liu33, Ke Liu17, L. Liu64,50, M. H. Liu9,f, P. L. Liu1, Q. Liu55, S. B. Liu64,50, T. Liu9,f, W. K. Liu36, W. M. Liu64,50, X. Liu31,j,k, Y. Liu31,j,k, Y. B. Liu36, Z. A. Liu1,50,55, Z. Q. Liu42, X. C. Lou1,50,55, F. X. Lu51, H. J. Lu18, J. G. Lu1,50, X. L. Lu1, Y. Lu1, Y. P. Lu1,50, Z. H. Lu1, C. L. Luo34, M. X. Luo72, T. Luo9,f, X. L. Luo1,50, X. R. Lyu55, Y. F. Lyu36, F. C. Ma33, H. L. Ma1, L. L. Ma42, M. M. Ma1,55, Q. M. Ma1, R. Q. Ma1,55, R. T. Ma55, X. Y. Ma1,50, Y. Ma39,g, F. E. Maas14, M. Maggiora67A,67C, S. Maldaner4, S. Malde62, Q. A. Malik66, A. Mangoni23B, Y. J. Mao39,g, Z. P. Mao1, S. Marcello67A,67C, Z. X. Meng58, J. G. Messchendorp56,11, G. Mezzadri24A, H. Miao1, T. J. Min35, R. E. Mitchell22, X. H. Mo1,50,55, N. Yu. Muchnoi10,b, H. Muramatsu60, S. Nakhoul11,d, Y. Nefedov29, F. Nerling11,d, I. B. Nikolaev10,b, Z. Ning1,50, S. Nisar8,l, Y. Niu 42, S. L. Olsen55, Q. Ouyang1,50,55, S. Pacetti23B,23C, X. Pan9,f, Y. Pan59, A. Pathak1, A. Pathak27, M. Pelizaeus4, H. P. Peng64,50, K. Peters11,d, J. Pettersson68, J. L. Ping34, R. G. Ping1,55, S. Plura28, S. Pogodin29, R. Poling60, V. Prasad64,50, H. Qi64,50, H. R. Qi53, M. Qi35, T. Y. Qi9,f, S. Qian1,50, W. B. Qian55, Z. Qian51, C. F. Qiao55, J. J. Qin65, L. Q. Qin12, X. P. Qin9,f, X. S. Qin42, Z. H. Qin1,50, J. F. Qiu1, S. Q. Qu36, S. Q. Qu53, K. H. Rashid66, K. Ravindran21, C. F. Redmer28, K. J. Ren32, A. Rivetti67C, V. Rodin56, M. Rolo67C, G. Rong1,55, Ch. Rosner14, M. Rump61, H. S. Sang64, A. Sarantsev29,c, Y. Schelhaas28, C. Schnier4, K. Schoenning68, M. Scodeggio24A,24B, K. Y. Shan9,f, W. Shan19, X. Y. Shan64,50, J. F. Shangguan47, L. G. Shao1,55, M. Shao64,50, C. P. Shen9,f, H. F. Shen1,55, X. Y. Shen1,55, B.-A. Shi55, H. C. Shi64,50, R. S. Shi1,55, X. Shi1,50, X. D Shi64,50, J. J. Song15, W. M. Song27,1, Y. X. Song39,g, S. Sosio67A,67C, S. Spataro67A,67C, F. Stieler28, K. X. Su69, P. P. Su47, Y.-J. Su55, G. X. Sun1, H. Sun55, H. K. Sun1, J. F. Sun15, L. Sun69, S. S. Sun1,55, T. Sun1,55, W. Y. Sun27, X Sun20,h, Y. J. Sun64,50, Y. Z. Sun1, Z. T. Sun42, Y. H. Tan69, Y. X. Tan64,50, C. J. Tang46, G. Y. Tang1, J. Tang51, L. Y Tao65, Q. T. Tao20,h, J. X. Teng64,50, V. Thoren68, W. H. Tian44, Y. Tian25,55, I. Uman54B, B. Wang1, B. L. Wang55, C. W. Wang35, D. Y. Wang39,g, F. Wang65, H. J. Wang31,j,k, H. P. Wang1,55, K. Wang1,50, L. L. Wang1, M. Wang42, M. Z. Wang39,g, Meng Wang1,55, S. Wang9,f, T. Wang9,f, T. J. Wang36, W. Wang51, W. H. Wang69, W. P. Wang64,50, X. Wang39,g, X. F. Wang31,j,k, X. L. Wang9,f, Y. D. Wang38, Y. F. Wang1,50,55, Y. H. Wang40, Y. Q. Wang1, Z. Wang1,50, Z. Y. Wang1,55, Ziyi Wang55, D. H. Wei12, F. Weidner61, S. P. Wen1, D. J. White59, U. Wiedner4, G. Wilkinson62, M. Wolke68, L. Wollenberg4, J. F. Wu1,55, L. H. Wu1, L. J. Wu1,55, X. Wu9,f, X. H. Wu27, Y. Wu64, Z. Wu1,50, L. Xia64,50, T. Xiang39,g, G. Y. Xiao35, H. Xiao9,f, S. Y. Xiao1, Y. L. Xiao9,f, Z. J. Xiao34, C. Xie35, X. H. Xie39,g, Y. Xie42, Y. G. Xie1,50, Y. H. Xie6, Z. P. Xie64,50, T. Y. Xing1,55, C. F. Xu1, C. J. Xu51, G. F. Xu1, H. Y. Xu58, Q. J. Xu13, S. Y. Xu63, X. P. Xu47, Y. C. Xu55, Z. P. Xu35, F. Yan9,f, L. Yan9,f, W. B. Yan64,50, W. C. Yan73, H. J. Yang43,e, H. L. Yang27, H. X. Yang1, L. Yang44, S. L. Yang55, Y. X. Yang1,55, Yifan Yang1,55, M. Ye1,50, M. H. Ye7, J. H. Yin1, Z. Y. You51, B. X. Yu1,50,55, C. X. Yu36, G. Yu1,55, J. S. Yu20,h, T. Yu65, C. Z. Yuan1,55, L. Yuan2, S. C. Yuan1, X. Q. Yuan1, Y. Yuan1,55, Z. Y. Yuan51, C. X. Yue32, A. A. Zafar66, F. R. Zeng42, X. Zeng Zeng6, Y. Zeng20,h, Y. H. Zhan51, A. Q. Zhang1, B. L. Zhang1, B. X. Zhang1, G. Y. Zhang15, H. Zhang64, H. H. Zhang51, H. H. Zhang27, H. Y. Zhang1,50, J. L. Zhang70, J. Q. Zhang34, J. W. Zhang1,50,55, J. Y. Zhang1, J. Z. Zhang1,55, Jianyu Zhang1,55, Jiawei Zhang1,55, L. M. Zhang53, L. Q. Zhang51, Lei Zhang35, P. Zhang1, Q. Y. Zhang32,73, Shulei Zhang20,h, X. D. Zhang38, X. M. Zhang1, X. Y. Zhang42, X. Y. Zhang47, Y. Zhang62, Y. T. Zhang73, Y. H. Zhang1,50, Yan Zhang64,50, Yao Zhang1, Z. H. Zhang1, Z. Y. Zhang69, Z. Y. Zhang36, G. Zhao1, J. Zhao32, J. Y. Zhao1,55, J. Z. Zhao1,50, Lei Zhao64,50, Ling Zhao1, M. G. Zhao36, Q. Zhao1, S. J. Zhao73, Y. B. Zhao1,50, Y. X. Zhao25,55, Z. G. Zhao64,50, A. Zhemchugov29,a, B. Zheng65, J. P. Zheng1,50, Y. H. Zheng55, B. Zhong34, C. Zhong65, X. Zhong51, H. Zhou42, L. P. Zhou1,55, X. Zhou69, X. K. Zhou55, X. R. Zhou64,50, X. Y. Zhou32, Y. Z. Zhou9,f, J. Zhu36, K. Zhu1, K. J. Zhu1,50,55, L. X. Zhu55, S. H. Zhu63, S. Q. Zhu35, T. J. Zhu70, W. J. Zhu9,f, Y. C. Zhu64,50, Z. A. Zhu1,55, B. S. Zou1, J. H. Zou1
(BESIII Collaboration)
1 Institute of High Energy Physics, Beijing 100049, People’s Republic of China
2 Beihang University, Beijing 100191, People’s Republic of China
3 Beijing Institute of Petrochemical Technology, Beijing 102617, People’s Republic of China
4 Bochum Ruhr-University, D-44780 Bochum, Germany
5 Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
6 Central China Normal University, Wuhan 430079, People’s Republic of China
7 China Center of Advanced Science and Technology, Beijing 100190, People’s Republic of China
8 COMSATS University Islamabad, Lahore Campus, Defence Road, Off Raiwind Road, 54000 Lahore, Pakistan
9 Fudan University, Shanghai 200433, People’s Republic of China
10 G.I. Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
11 GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
12 Guangxi Normal University, Guilin 541004, People’s Republic of China
13 Hangzhou Normal University, Hangzhou 310036, People’s Republic of China
14 Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
15 Henan Normal University, Xinxiang 453007, People’s Republic of China
16 Henan University of Science and Technology, Luoyang 471003, People’s Republic of China
17 Henan University of Technology, Zhengzhou 450001, People’s Republic of China
18 Huangshan College, Huangshan 245000, People’s Republic of China
19 Hunan Normal University, Changsha 410081, People’s Republic of China
20 Hunan University, Changsha 410082, People’s Republic of China
21 Indian Institute of Technology Madras, Chennai 600036, India
22 Indiana University, Bloomington, Indiana 47405, USA
23 INFN Laboratori Nazionali di Frascati , (A)INFN Laboratori Nazionali di Frascati, I-00044, Frascati, Italy; (B)INFN Sezione di Perugia, I-06100, Perugia, Italy; (C)University of Perugia, I-06100, Perugia, Italy
24 INFN Sezione di Ferrara, (A)INFN Sezione di Ferrara, I-44122, Ferrara, Italy; (B)University of Ferrara, I-44122, Ferrara, Italy
25 Institute of Modern Physics, Lanzhou 730000, People’s Republic of China
26 Institute of Physics and Technology, Peace Ave. 54B, Ulaanbaatar 13330, Mongolia
27 Jilin University, Changchun 130012, People’s Republic of China
28 Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
29 Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
30 Justus-Liebig-Universitaet Giessen, II. Physikalisches Institut, Heinrich-Buff-Ring 16, D-35392 Giessen, Germany
31 Lanzhou University, Lanzhou 730000, People’s Republic of China
32 Liaoning Normal University, Dalian 116029, People’s Republic of China
33 Liaoning University, Shenyang 110036, People’s Republic of China
34 Nanjing Normal University, Nanjing 210023, People’s Republic of China
35 Nanjing University, Nanjing 210093, People’s Republic of China
36 Nankai University, Tianjin 300071, People’s Republic of China
37 National Centre for Nuclear Research, Warsaw 02-093, Poland
38 North China Electric Power University, Beijing 102206, People’s Republic of China
39 Peking University, Beijing 100871, People’s Republic of China
40 Qufu Normal University, Qufu 273165, People’s Republic of China
41 Shandong Normal University, Jinan 250014, People’s Republic of China
42 Shandong University, Jinan 250100, People’s Republic of China
43 Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
44 Shanxi Normal University, Linfen 041004, People’s Republic of China
45 Shanxi University, Taiyuan 030006, People’s Republic of China
46 Sichuan University, Chengdu 610064, People’s Republic of China
47 Soochow University, Suzhou 215006, People’s Republic of China
48 South China Normal University, Guangzhou 510006, People’s Republic of China
49 Southeast University, Nanjing 211100, People’s Republic of China
50 State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People’s Republic of China
51 Sun Yat-Sen University, Guangzhou 510275, People’s Republic of China
52 Suranaree University of Technology, University Avenue 111, Nakhon Ratchasima 30000, Thailand
53 Tsinghua University, Beijing 100084, People’s Republic of China
54 Turkish Accelerator Center Particle Factory Group, (A)Istinye University, 34010, Istanbul, Turkey; (B)Near East University, Nicosia, North Cyprus, Mersin 10, Turkey
55 University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
56 University of Groningen, NL-9747 AA Groningen, The Netherlands
57 University of Hawaii, Honolulu, Hawaii 96822, USA
58 University of Jinan, Jinan 250022, People’s Republic of China
59 University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
60 University of Minnesota, Minneapolis, Minnesota 55455, USA
61 University of Muenster, Wilhelm-Klemm-Str. 9, 48149 Muenster, Germany
62 University of Oxford, Keble Rd, Oxford, UK OX13RH
63 University of Science and Technology Liaoning, Anshan 114051, People’s Republic of China
64 University of Science and Technology of China, Hefei 230026, People’s Republic of China
65 University of South China, Hengyang 421001, People’s Republic of China
66 University of the Punjab, Lahore-54590, Pakistan
67 University of Turin and INFN, (A)University of Turin, I-10125, Turin, Italy; (B)University of Eastern Piedmont, I-15121, Alessandria, Italy; (C)INFN, I-10125, Turin, Italy
68 Uppsala University, Box 516, SE-75120 Uppsala, Sweden
69 Wuhan University, Wuhan 430072, People’s Republic of China
70 Xinyang Normal University, Xinyang 464000, People’s Republic of China
71 Yunnan University, Kunming 650500, People’s Republic of China
72 Zhejiang University, Hangzhou 310027, People’s Republic of China
73 Zhengzhou University, Zhengzhou 450001, People’s Republic of China
a Also at the Moscow Institute of Physics and Technology, Moscow 141700, Russia
b Also at the Novosibirsk State University, Novosibirsk, 630090, Russia
c Also at the NRC ”Kurchatov Institute”, PNPI, 188300, Gatchina, Russia
d Also at Goethe University Frankfurt, 60323 Frankfurt am Main, Germany
e Also at Key Laboratory for Particle Physics, Astrophysics and Cosmology, Ministry of Education; Shanghai Key Laboratory for Particle Physics and Cosmology; Institute of Nuclear and Particle Physics, Shanghai 200240, People’s Republic of China
f Also at Key Laboratory of Nuclear Physics and Ion-beam Application (MOE) and Institute of Modern Physics, Fudan University, Shanghai 200443, People’s Republic of China
g Also at State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, People’s Republic of China
h Also at School of Physics and Electronics, Hunan University, Changsha 410082, China
i Also at Guangdong Provincial Key Laboratory of Nuclear Science, Institute of Quantum Matter, South China Normal University, Guangzhou 510006, China
j Also at Frontiers Science Center for Rare Isotopes, Lanzhou University, Lanzhou 730000, People’s Republic of China
k Also at Lanzhou Center for Theoretical Physics, Lanzhou University, Lanzhou 730000, People’s Republic of China
l Also at the Department of Mathematical Sciences, IBA, Karachi , Pakistan
Abstract
Using of collision data collected at a center-of-mass energy of 3.773 GeV with the BESIII detector, we present a measurement of the branching fraction of the doubly Cabibbo-suppressed (DCS) decay and a search for the DCS decay .
The branching fraction of is determined to be
. No signal is observed for and an upper limit of is set on the branching fraction at the 90% C.L.
We combine these results with the world-average branching fractions of their counterpart Cabibbo-favored decays
to determine the ratios of the doubly Cabibbo-suppressed over the Cabibbo-favored branching fractions, and at the 90% C.L., which
correspond to and , respectively, where is the Cabibbo angle.
pacs:
13.20.Fc, 14.40.Lb
I Introduction
Studies of doubly Cabibbo-suppressed (DCS) decays of charmed mesons provide important information on charmed-hadron dynamics. The ratio of the branching fraction of a given DCS decay relative to its Cabibbo-favored (CF) counterpart is naively expected to be about ( is the Cabibbo mixing angle) Lipkin ; theory_1 .
Recently, BESIII reported the observation of the DCS decay bes3_DCS_Kpipipi0 ; bes3-DCS-Dp-K3pi-v2 (charge conjugate processes are implied throughout this paper).
The branching fraction of this decay averaged over the two measurements reported in Refs. bes3_DCS_Kpipipi0 ; bes3-DCS-Dp-K3pi-v2 is ,
which gives a DCS/CF branching fraction ratio of .
Comprehensive measurements of the DCS decays of other charmed mesons, especially for isospin symmetrical decays of ,
may shed light on the origin of this anomalously large DCS/CF branching fraction ratio.
So far, only a few DCS decays, namely , and , have been observed, with decay branching fractions extracted from the ratio of DCS/CF decay branching fractions from the experiments determining - mixing parameters or coherence parameters pdg2020 .
In this paper, we present the first direct measurements of the branching fractions of and
by analyzing 2.93 fb-1 of collision data lum_bes3 taken at a center-of-mass energy of 3.773 GeV with the BESIII detector.
Because the traditional hadronic tag method suffers from complex quantum-correlation effects zzxing ,
this analysis is performed with the semileptonic tag method adopted in our previous work bes3-DCS-Dp-K3pi-v2 .
Our direct measurements would benefit the constraint of the charm mixing parameters when combining with individual CF decay branching fraction.
II Data and Monte Carlo
The BESIII detector is a magnetic
spectrometer BESIII located at the Beijing Electron
Positron Collider (BEPCII) Yu:IPAC2016-TUYA01 . The
cylindrical core of the BESIII detector consists of a helium-based
multilayer drift chamber (), a plastic scintillator time-of-flight
system (), and a CsI(Tl) electromagnetic calorimeter (),
which are all enclosed in a superconducting solenoidal magnet
providing a 1.0 T magnetic field. The solenoid is supported by an
octagonal flux-return yoke with resistive plate counter muon-identifier modules interleaved with steel. The acceptance of
charged particles and photons is 93% over the solid angle. The
charged-particle momentum resolution at is
, and the resolution of specific ionization energy loss (d/d) is for electrons
from Bhabha scattering. The EMC measures photon energies with a
resolution of () at GeV in the barrel (end-cap)
region. The time resolution of the TOF barrel part is 68 ps, while
that of the end-cap part is 110 ps.
Details about the design and performance of the BESIII detector are given in Ref. BESIII .
Simulated samples produced with the geant4-based geant4 Monte Carlo (MC) package, which
includes the geometric description of the BESIII detector and the
detector response, are used to determine the detection efficiency
and to estimate the backgrounds. The simulation includes the beam-energy spread and initial-state radiation in the
annihilations modeled with the generator kkmckkmc .
The inclusive MC samples consist of the production of pairs,
the non- decays of the , the initial-state radiation
production of the and states, and the
continuum processes.
The known decay modes are modelled with evtgenevtgen using the branching fractions taken from the
Particle Data Group (PDG) pdg2020 , and the remaining unknown decays of the charmonium states are
modeled by lundcharmlundcharm . Final-state radiation is incorporated using the photos package photos .
The decay is simulated using an MC generator which combines the resonant decays , , , and a three-body phase-space model.
The decay is simulated with a four-body phase-space model.
The decay is simulated with the modified pole model MPM
with the pole mass fixed at the nominal mass pdg2020 and the other parameters quoted from bes3-D0-kev .
III Measurement method
The center-of-mass energy of 3.773 GeV lies above the production threshold but below that of .
At this energy point, the pairs are produced copiously and are not accompanied by additional hadrons.
This allows decays to be studied with the double-tag method.
In this analysis double-tag events refer to those in which the DCS decays or are found on the recoiling side of the semileptonic decay .
The branching fraction of or is determined by
(1)
where
is the total number of pairs in the data sample determined in our previous work bes3-crsDD ,
is the signal yield of the double-tag events obtained from the data sample,
is the effective efficiency of reconstructing the double-tag events,
and
is the branching fraction of the semileptonic decay taken from the PDG pdg2020 .
IV Event selection
The double-tag candidates are required to contain at least two good photons for and four for as well as exactly four charged tracks for both modes.
We use the same selection criteria for , , , and candidates as were used in our previous studies bes3_DCS_Kpipipi0 ; epjc76 ; cpc40 ; bes3-Dp-K1ev ; bes3-D-b1enu .
All charged tracks are required to originate from a region within , 1 cm and 10 cm.
Here, is the polar angle of the charged track with respect to the MDC axis, and are the distances of closest approach of the charged track to the interaction point perpendicular to and along the MDC axis, respectively.
Particle identification (PID) of kaons and pions is performed with the combined d/d and TOF information to calculate their corresponding confidence levels.
Charged tracks with confidence level for kaon (pion) hypothesis greater than that for pion (kaon) hypothesis are assigned as kaon (pion) candidates.
Photon candidates are selected by using the information recorded by the EMC. The shower time is required to be within 700 ns of the event start time. The shower energy is required to be greater than 25 (50) MeV if the crystal with the maximum deposited energy in that cluster is in the barrel (end-cap) region BESIII . The opening angle between the shower direction and the extrapolated position on the EMC of the closest charged track must be greater than .
The candidates are formed by photon pairs with invariant mass within GeV. To improve the resolution, a kinematic fit constraining the
invariant mass to the known mass pdg2020 is imposed on the selected photon pair.
In the selection of the candidates,
the invariant mass of the combination is required to be outside of the interval GeV/ to reject the dominant peaking background from the singly Cabibbo-suppressed decay . This requirement corresponds to about five standard deviations of the experimental mass resolution.
The signal candidates for or are identified with two variables: the energy difference
(2)
and the beam-constrained mass
(3)
Here, is the beam energy, and are the momentum and energy of the candidate in the rest frame, respectively.
If there are multiple candidates for the hadronic side,
only the one with the minimum is kept.
The correctly reconstructed candidates concentrate around zero in the distribution
and around the nominal mass in the distribution.
The events satisfying MeV for and MeV for are kept for further analysis.
After the hadronic mesons are reconstructed, the candidates for are selected from the remaining tracks that have not been used to select the hadronic side.
Then, the number of extra charged tracks () is required to be zero.
The charge of the electron candidate is required to be opposite to that of the kaon from the hadronic decay.
Electron PID uses the combined d/d, TOF, and EMC information, with which the combined confidence levels under the electron, pion, and kaon hypotheses (, , and ) are calculated.
Electron candidates are required to satisfy and .
To reduce the background due to mis-identification between hadrons and electrons, the energy of the electron candidate deposited in the EMC is further required to be greater than 0.8 times its measured momentum.
Then, to partially compensate the effects of final-state radiation and bremsstrahlung (FSR recovery), the four-momenta of photon(s) within of the initial electron direction are added to the electron four-momentum measured by the MDC.
The charged kaons from the semileptonic decay are required to satisfy the same PID criteria as the kaons from the hadronic decays, and to have a charge opposite to that of the electron.
To suppress potential backgrounds from hadronic decays with a misidentified electron,
the invariant mass of the combination, , is required to be less than 1.8 GeV/.
Furthermore, we require that
the maximum energy of extra photons () which have not been used in the tag
selection is less than 0.25 GeV and there is no extra candidate ().
The semileptonic decay is identified using a kinematic quantity defined as
(4)
Here, and are the missing energy and momentum of the double-tag event in the center-of-mass system, in
which and are the energy and momentum of the , and
and are the energy and momentum of the candidate. The
resolution is improved by constraining the energy to the beam energy and , where is the unit vector in the momentum direction of the and is the nominal mass pdg2020 .
Fig. 1: Distributions of versus of the accepted double-tag candidate events for (a) and (b) versus decays in data. The area between dashed red lines show the signal region.
Figure 1 shows the distributions of versus of the double-tag candidate events in data. The clusters around the known mass along the axis and zero along the axis are the signal double-tag candidate events.
The signal region is selected around the known mass: those candidates satisfying GeV/ are kept for further analysis.
After the implementation of the above-mentioned requirements, the distributions of the surviving events are shown in Fig. 2.
The detection efficiencies obtained from signal MC samples are and for the double-tag events
of and versus , respectively,
where the efficiencies include the branching fraction of and the uncertainties are statistical only.
The background components and corresponding ratios in the total background are described below.
For versus ,
the peaking backgrounds are mainly from the CF modes
versus due to
the mis-identification between kaons and pions in the hadronic side (36.0%) and
the mis-identification between kaons and electrons in the hadronic side (12.9%);
while the residual backgrounds are
versus (7.9%),
versus (6.5%),
versus (5.8%) and
other decay modes (30.9%).
For versus ,
the peaking backgrounds are mainly from
versus (30.6%);
while the residual backgrounds are
versus (8.2%),
versus (8.2%),
versus (4.1%),
and other decay modes (49.0%).
To measure the signal yields, unbinned maximum-likelihood fits are performed on the distributions.
The non-peaking backgrounds (including a small contribution from wrongly reconstructed semileptonic candidates) are described by the corresponding MC-simulated shapes.
The background shapes are derived from the inclusive MC sample and the
signal shapes from the signal MC samples.
The yield of the peaking background is fixed based on the known branching fractions and the mis-identification rates, and the yields of the signal and
non-peaking backgrounds are free parameters of the fits.
The fit results are shown in Fig. 2.
From these fits, we measure signal events for the decay and signal events for .
These results give the product branching fractions to be
,
and
.
Combining the world average of pdg2020 ,
we obtain
and
The statistical
significance of the signal is calculated by , where
and are the maximal
likelihood of the fits with and without the signal contribution, respectively.
These significances are determined to be and for and , respectively.
Fig. 2: Fits to the distributions of the accepted double-tag candidate events for (a) and (b) versus decays.
The points with error bars are data.
The blue solid curves are the total fit results (Total fit). The red dotted and black dashed curves are the fitted signal (Signal) and background (Total BKG) components, respectively.
The component between the black dashed and pink dot-dashed curves is the peaking background and the pink dot-dashed curve represents the other background contributions (Other BKG).
The upper limit on the branching fraction of the decay is determined to be at 90% confidence level, using the Bayesian approach UPM after incorporating the systematic uncertainty.
The distribution of the likelihood versus the assumed branching fraction is shown in Fig. 3.
Fig. 3: Distributions of normalized likelihood distributions versus the signal yield and branching fraction of .
The results obtained with and without incorporating the systematic uncertainty are shown by the red dashed and blue solid curves, respectively. The black arrow shows the result corresponding to 90% confidence level.
V Systematic uncertainties
The systematic uncertainties originating from tracking (PID) efficiencies
are studied by using a control sample of events.
The efficiency ratios of data and MC simulation for tracking and PID are
and , respectively.
Here, the two dimensional (momentum and ) tracking (PID) efficiencies from the control sample have been re-weighted to match those in the signal decays.
The systematic uncertainties associated with the and tracking (PID) efficiencies are investigated with , , versus , , , as well as versus double-tag hadronic events, using a sample with a missing or .
The ratios of tracking or PID efficiencies for charged kaons and pions between data and MC simulation are listed in Table 1.
Here, the momentum dependent tracking (PID) efficiencies from control samples have been re-weighted to match those in the signal decays.
After correcting the signal MC efficiencies by these factors, the residual uncertainties on the tracking (PID) efficiencies of
, , and are assigned as 0.2% (0.2%), 0.3% (0.2%), and 0.2% (0.2%), respectively.
Table 1: The ratios of efficiencies of tracking, PID, tracking, and PID between data and MC simulation.
Source
(%)
(%)
The systematic uncertainty of reconstruction efficiency is investigated by using the double-tag hadronic decays of
and tagged by either or epjc76 ; cpc40 .
The systematic uncertainty on the reconstruction efficiency is assigned as 0.8% for each .
The systematic uncertainty associated with the fit is estimated by comparing the baseline branching-fraction result with the result obtained
with alternative signal shapes and background shapes.
The systematic uncertainty due to the assumed signal shape is estimated by replacing the nominal description with one convolved with a Gaussian resolution function.
Here, the parameters used in the convolved Gaussian function representing the data-MC simulation difference are
obtained from the CF decay .
The change in the branching fraction due to the assumed signal shape is found to be negligible.
The systematic uncertainty from the simulated background shape is taken into account by varying the
dominant peaking background component by .
The change in the re-measured branching fraction, 1.0%, is assigned as the systematic uncertainty associated with the background shape for decays, while that for decays, is found to be negligible.
In addition, the effects of other background sources, examined by varying their size and shape, are also negligible.
The systematic uncertainties due to the requirements of and for the hadronic side as well
as the requirement of for the semileptonic side are studied
by using control samples of the CF decay versus .
The corresponding uncertainties are taken to be the differences of the acceptance efficiencies between data and MC simulation. These uncertainties are all found to be 0.1%.
The systematic uncertainty associated with the veto in the distribution is assigned by varying the mass window by MeV/.
The maximum relative change in the measured branching fraction is not significantly larger than the statistical
uncertainty after considering the correlations between the signal yields, hence this uncertainty is ignored ksocut .
The systematic uncertainty due to MC modeling is assigned to be the difference between
the nominal efficiency and the average efficiency based on the signal MC events of the various components.
Besides individual phase-space decays, the resonant decays , , and
have been considered for ;
and the resonant decays , , and have been considered for .
The corresponding systematic uncertainties are assigned as 1.9% and 3.6% for and , respectively.
The uncertainty in the MC modeling of the semileptonic decay of has been estimated in our previous work and is negligible bes3-D0-kev .
The systematic uncertainty due to the , , and requirements
is estimated by using a control sample of the CF decay versus .
The differences in the acceptance efficiencies between data and MC simulation, 0.2% and 0.8%, are taken as the corresponding systematic uncertainties for the and decays, respectively.
The uncertainties due to MC sample sizes are 0.7% and 1.2% for and decays, respectively.
The uncertainty from FSR recovery is estimated as 0.3% as in decays bes3-D0-kev .
The total number of the pairs in the data sample is cited from Ref. bes3-crsDD and is known with a precision that induces a systematic uncertainty of 0.9%.
The branching fraction of contributes a systematic uncertainty of 1.0% pdg2020 .
Adding all these uncertainties in quadrature yields a total systematic uncertainty of
2.9% for and 4.0% for .
The systematic uncertainties discussed above are summarized in Table 2.
Table 2: Systematic uncertainties (in %) in the determination of the branching fractions.
Source
Tracking of , , and
0.7
0.7
PID of , , and
0.5
0.5
reconstruction
0.8
1.6
veto
N/A
Ignored
MC model
1.9
3.6
fit
1.0
Negligible
requirement
0.1
0.1
requirement
0.1
0.1
0.2
0.8
MC statistics
0.7
1.2
FSR recovery
0.3
0.3
0.9
0.9
Quoted branching fraction
1.0
1.0
Total
2.9
4.0
VI Summary
In conclusion, using of collision data accumulated at a center-of-mass energy of 3.773 GeV with the BESIII detector,
we have measured the branching fraction of the DCS decay of and performed a search for the DCS decay .
The branching fraction of is determined to be , which
is consistent with the PDG value pdg2020 .
No significant signal is seen for and an upper limit of is set on the branching fraction at the 90% C.L.
Using the world-average value of pdg2020 ,
we obtain the DCS/CF ratio , corresponding to .
Our result for and the world-average value of pdg2020 leads to the upper limit at the 90% C.L., corresponding to .
In the future, amplitude analyses of these two decays with larger data samples taken by BESIII bes3-white-paper ; Li:2021iwf can be used to measure the decay rates of the intermediate two-body decays, which are important for exploring quark SU(3)-flavor symmetry and its breaking effects,
and thereby benefit the theoretical predictions of violation in hadronic decays Saur:2020rgd .
VII Acknowledgement
The BESIII collaboration thanks the staff of BEPCII and the IHEP computing center for their strong support. This work is supported in part by National Key Research and Development Program of China under Contracts Nos. 2020YFA0406400, 2020YFA0406300; National Natural Science Foundation of China (NSFC) under Contracts Nos. 12105076, 11705230, 11625523, 11635010, 11735014, 11822506, 11835012, 11935015, 11935016, 11935018, 11961141012, 12022510, 12025502, 12035009, 12035013, 12061131003, 12192260, 12192260, 12192261, 12192262, 12192263, 12192264, and 12192265; the Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program; Joint Large-Scale Scientific Facility Funds of the NSFC and CAS under Contracts Nos. U1732263, U1832207; CAS Key Research Program of Frontier Sciences under Contract No. QYZDJ-SSW-SLH040; 100 Talents Program of CAS; INPAC and Shanghai Key Laboratory for Particle Physics and Cosmology; ERC under Contract No. 758462; European Union’s Horizon 2020 research and innovation programme under Marie Sklodowska-Curie grant agreement under Contract No. 894790;; German Research Foundation DFG under Contracts Nos. 443159800, Collaborative Research Center CRC 1044, FOR 2359, GRK 214; Istituto Nazionale di Fisica Nucleare, Italy; Ministry of Development of Turkey under Contract No. DPT2006K-120470; National Science and Technology fund; Olle Engkvist Foundation under Contract No. 200-0605; STFC (United Kingdom); The Knut and Alice Wallenberg Foundation (Sweden) under Contract No. 2016.0157; The Royal Society, UK under Contracts Nos. DH140054, DH160214; The Swedish Research Council; U. S. Department of Energy under Contracts Nos. DE-FG02-05ER41374, DE-SC-0012069.