Search for rare decay $J/ \psi \to \phi e^+ e^-$
BESIII Collaboration: M. Ablikim, M. N. Achasov, S. Ahmed, M., Albrecht, M. Alekseev, A. Amoroso, F. F. An, Q. An, Y. Bai, O. Bakina, R., Baldini Ferroli, Y. Ban, K. Begzsuren, D. W. Bennett, J. V. Bennett, N., Berger, M. Bertani, D. Bettoni, F. Bianchi, E. Boger, I. Boyko

TL;DR
This study searches for the rare decay $J/\psi \to \phi e^+ e^-$ using a large dataset, finds no signal, and sets an upper limit on its branching fraction, which is still above Standard Model expectations.
Contribution
First search for the decay $J/\psi \to \phi e^+ e^-$ with a large dataset, establishing an upper limit on its branching fraction.
Findings
No signal observed for the decay.
Upper limit on branching fraction set at $1.2 \times 10^{-7}$.
Limit is about ten times higher than Standard Model prediction.
Abstract
Using a data sample of events collected at 3.686 GeV with the BESIII detector at the BEPCII, we search for the rare decay via . No signal events are observed and the upper limit on the branching fraction is set to be at the 90\% confidence level, which is still about one order of magnitude higher than the Standard Model prediction.
| Sources | Uncertainty |
|---|---|
| 0.7 | |
| Tracking | 6.0 |
| PID | 2.3 |
| Kinematic fit | 3.3 |
| Signal region | 1.8 |
| Background estimation | 1.5 |
| MC statistics | 0.4 |
| MC modeling | 5.4 |
| 0.9 | |
| 1.0 | |
| Total | 9.5 |
| Item | Value |
|---|---|
| (90% CL) | |
| % | |
| % | |
| % | |
| 9.5% | |
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Search for rare decay
M. Ablikim1, M. N. Achasov10,d, S. Ahmed15, M. Albrecht4, M. Alekseev55A,55C, A. Amoroso55A,55C, F. F. An1, Q. An52,42, Y. Bai41, O. Bakina27, R. Baldini Ferroli23A, Y. Ban35, K. Begzsuren25, D. W. Bennett22, J. V. Bennett5, N. Berger26, M. Bertani23A, D. Bettoni24A, F. Bianchi55A,55C, E. Boger27,b, I. Boyko27, R. A. Briere5, H. Cai57, X. Cai1,42, A. Calcaterra23A, G. F. Cao1,46, S. A. Cetin45B, J. Chai55C, J. F. Chang1,42, W. L. Chang1,46, G. Chelkov27,b,c, G. Chen1, H. S. Chen1,46, J. C. Chen1, M. L. Chen1,42, P. L. Chen53, S. J. Chen33, X. R. Chen30, Y. B. Chen1,42, W. Cheng55C, X. K. Chu35, G. Cibinetto24A, F. Cossio55C, H. L. Dai1,42, J. P. Dai37,h, A. Dbeyssi15, D. Dedovich27, Z. Y. Deng1, A. Denig26, I. Denysenko27, M. Destefanis55A,55C, F. De Mori55A,55C, Y. Ding31, C. Dong34, J. Dong1,42, L. Y. Dong1,46, M. Y. Dong1,42,46, Z. L. Dou33, S. X. Du60, P. F. Duan1, J. Fang1,42, S. S. Fang1,46, Y. Fang1, R. Farinelli24A,24B, L. Fava55B,55C, S. Fegan26, F. Feldbauer4, G. Felici23A, C. Q. Feng52,42, E. Fioravanti24A, M. Fritsch4, C. D. Fu1, Q. Gao1, X. L. Gao52,42, Y. Gao44, Y. G. Gao6, Z. Gao52,42, B. Garillon26, I. Garzia24A, A. Gilman49, K. Goetzen11, L. Gong34, W. X. Gong1,42, W. Gradl26, M. Greco55A,55C, L. M. Gu33, M. H. Gu1,42, Y. T. Gu13, A. Q. Guo1, L. B. Guo32, R. P. Guo1,46, Y. P. Guo26, A. Guskov27, Z. Haddadi29, S. Han57, X. Q. Hao16, F. A. Harris47, K. L. He1,46, X. Q. He51, F. H. Heinsius4, T. Held4, Y. K. Heng1,42,46, Z. L. Hou1, H. M. Hu1,46, J. F. Hu37,h, T. Hu1,42,46, Y. Hu1, G. S. Huang52,42, J. S. Huang16, X. T. Huang36, X. Z. Huang33, Z. L. Huang31, T. Hussain54, W. Ikegami Andersson56, M, Irshad52,42, Q. Ji1, Q. P. Ji16, X. B. Ji1,46, X. L. Ji1,42, H. L. Jiang36, X. S. Jiang1,42,46, X. Y. Jiang34, J. B. Jiao36, Z. Jiao18, D. P. Jin1,42,46, S. Jin33, Y. Jin48, T. Johansson56, A. Julin49, N. Kalantar-Nayestanaki29, X. S. Kang34, M. Kavatsyuk29, B. C. Ke1, I. K. Keshk4, T. Khan52,42, A. Khoukaz50, P. Kiese26, R. Kiuchi1, R. Kliemt11, L. Koch28, O. B. Kolcu45B,f, B. Kopf4, M. Kornicer47, M. Kuemmel4, M. Kuessner4, A. Kupsc56, M. Kurth1, W. Kühn28, J. S. Lange28, P. Larin15, L. Lavezzi55C, S. Leiber4, H. Leithoff26, C. Li56, Cheng Li52,42, D. M. Li60, F. Li1,42, F. Y. Li35, G. Li1, H. B. Li1,46, H. J. Li1,46, J. C. Li1, J. W. Li40, K. J. Li43, Kang Li14, Ke Li1, Lei Li3, P. L. Li52,42, P. R. Li46,7, Q. Y. Li36, T. Li36, W. D. Li1,46, W. G. Li1, X. L. Li36, X. N. Li1,42, X. Q. Li34, Z. B. Li43, H. Liang52,42, Y. F. Liang39, Y. T. Liang28, G. R. Liao12, L. Z. Liao1,46, J. Libby21, C. X. Lin43, D. X. Lin15, B. Liu37,h, B. J. Liu1, C. X. Liu1, D. Liu52,42, D. Y. Liu37,h, F. H. Liu38, Fang Liu1, Feng Liu6, H. B. Liu13, H. L Liu41, H. M. Liu1,46, Huanhuan Liu1, Huihui Liu17, J. B. Liu52,42, J. Y. Liu1,46, K. Y. Liu31, Ke Liu6, L. D. Liu35, Q. Liu46, S. B. Liu52,42, X. Liu30, Y. B. Liu34, Z. A. Liu1,42,46, Zhiqing Liu26, Y. F. Long35, X. C. Lou1,42,46, H. J. Lu18, J. G. Lu1,42, Y. Lu1, Y. P. Lu1,42, C. L. Luo32, M. X. Luo59, T. Luo9,j, X. L. Luo1,42, S. Lusso55C, X. R. Lyu46, F. C. Ma31, H. L. Ma1, L. L. Ma36, M. M. Ma1,46, Q. M. Ma1, X. N. Ma34, X. Y. Ma1,42, Y. M. Ma36, F. E. Maas15, M. Maggiora55A,55C, S. Maldaner26, Q. A. Malik54, A. Mangoni23B, Y. J. Mao35, Z. P. Mao1, S. Marcello55A,55C, Z. X. Meng48, J. G. Messchendorp29, G. Mezzadri24A, J. Min1,42, T. J. Min33, R. E. Mitchell22, X. H. Mo1,42,46, Y. J. Mo6, C. Morales Morales15, N. Yu. Muchnoi10,d, H. Muramatsu49, A. Mustafa4, S. Nakhoul11,g, Y. Nefedov27, F. Nerling11,g, I. B. Nikolaev10,d, Z. Ning1,42, S. Nisar8, S. L. Niu1,42, X. Y. Niu1,46, S. L. Olsen46, Q. Ouyang1,42,46, S. Pacetti23B, Y. Pan52,42, M. Papenbrock56, P. Patteri23A, M. Pelizaeus4, J. Pellegrino55A,55C, H. P. Peng52,42, Z. Y. Peng13, K. Peters11,g, J. Pettersson56, J. L. Ping32, R. G. Ping1,46, A. Pitka4, R. Poling49, V. Prasad52,42, H. R. Qi2, M. Qi33, T. Y. Qi2, S. Qian1,42, C. F. Qiao46, N. Qin57, X. S. Qin4, Z. H. Qin1,42, J. F. Qiu1, S. Q. Qu34, K. H. Rashid54,i, C. F. Redmer26, M. Richter4, M. Ripka26, A. Rivetti55C, M. Rolo55C, G. Rong1,46, Ch. Rosner15, A. Sarantsev27,e, M. Savrié24B, K. Schoenning56, W. Shan19, X. Y. Shan52,42, M. Shao52,42, C. P. Shen2, P. X. Shen34, X. Y. Shen1,46, H. Y. Sheng1, X. Shi1,42, J. J. Song36, W. M. Song36, X. Y. Song1, S. Sosio55A,55C, C. Sowa4, S. Spataro55A,55C, F. F. Sui36, G. X. Sun1, J. F. Sun16, L. Sun57, S. S. Sun1,46, X. H. Sun1, Y. J. Sun52,42, Y. K Sun52,42, Y. Z. Sun1, Z. J. Sun1,42, Z. T. Sun1, Y. T Tan52,42, C. J. Tang39, G. Y. Tang1, X. Tang1, M. Tiemens29, B. Tsednee25, I. Uman45D, B. Wang1, B. L. Wang46, C. W. Wang33, D. Wang35, D. Y. Wang35, Dan Wang46, H. H. Wang36, K. Wang1,42, L. L. Wang1, L. S. Wang1, M. Wang36, Meng Wang1,46, P. Wang1, P. L. Wang1, W. P. Wang52,42, X. F. Wang1, Y. Wang52,42, Y. F. Wang1,42,46, Z. Wang1,42, Z. G. Wang1,42, Z. Y. Wang1, Zongyuan Wang1,46, T. Weber4, D. H. Wei12, P. Weidenkaff26, S. P. Wen1, U. Wiedner4, M. Wolke56, L. H. Wu1, L. J. Wu1,46, Z. Wu1,42, L. Xia52,42, X. Xia36, Y. Xia20, D. Xiao1, Y. J. Xiao1,46, Z. J. Xiao32, Y. G. Xie1,42, Y. H. Xie6, X. A. Xiong1,46, Q. L. Xiu1,42, G. F. Xu1, J. J. Xu1,46, L. Xu1, Q. J. Xu14, X. P. Xu40, F. Yan53, L. Yan55A,55C, W. B. Yan52,42, W. C. Yan2, Y. H. Yan20, H. J. Yang37,h, H. X. Yang1, L. Yang57, R. X. Yang52,42, S. L. Yang1,46, Y. H. Yang33, Y. X. Yang12, Yifan Yang1,46, Z. Q. Yang20, M. Ye1,42, M. H. Ye7, J. H. Yin1, Z. Y. You43, B. X. Yu1,42,46, C. X. Yu34, J. S. Yu30, J. S. Yu20, C. Z. Yuan1,46, Y. Yuan1, A. Yuncu45B,a, A. A. Zafar54, Y. Zeng20, B. X. Zhang1, B. Y. Zhang1,42, C. C. Zhang1, D. H. Zhang1, H. H. Zhang43, H. Y. Zhang1,42, J. Zhang1,46, J. L. Zhang58, J. Q. Zhang4, J. W. Zhang1,42,46, J. Y. Zhang1, J. Z. Zhang1,46, K. Zhang1,46, L. Zhang44, S. F. Zhang33, T. J. Zhang37,h, X. Y. Zhang36, Y. Zhang52,42, Y. H. Zhang1,42, Y. T. Zhang52,42, Yang Zhang1, Yao Zhang1, Yu Zhang46, Z. H. Zhang6, Z. P. Zhang52, Z. Y. Zhang57, G. Zhao1, J. W. Zhao1,42, J. Y. Zhao1,46, J. Z. Zhao1,42, Lei Zhao52,42, Ling Zhao1, M. G. Zhao34, Q. Zhao1, S. J. Zhao60, T. C. Zhao1, Y. B. Zhao1,42, Z. G. Zhao52,42, A. Zhemchugov27,b, B. Zheng53, J. P. Zheng1,42, W. J. Zheng36, Y. H. Zheng46, B. Zhong32, L. Zhou1,42, Q. Zhou1,46, X. Zhou57, X. K. Zhou52,42, X. R. Zhou52,42, X. Y. Zhou1, Xiaoyu Zhou20, Xu Zhou20, A. N. Zhu1,46, J. Zhu34, J. Zhu43, K. Zhu1, K. J. Zhu1,42,46, S. Zhu1, S. H. Zhu51, X. L. Zhu44, Y. C. Zhu52,42, Y. S. Zhu1,46, Z. A. Zhu1,46, J. Zhuang1,42, 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 Institute of Information Technology, Lahore, Defence Road, Off Raiwind Road, 54000 Lahore, Pakistan
9 Fudan University, Shanghai 200443, 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 Guangxi University, Nanning 530004, People’s Republic of China
14 Hangzhou Normal University, Hangzhou 310036, People’s Republic of China
15 Helmholtz Institute Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
16 Henan Normal University, Xinxiang 453007, People’s Republic of China
17 Henan University of Science and Technology, Luoyang 471003, 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 (A)INFN Laboratori Nazionali di Frascati, I-00044, Frascati, Italy; (B)INFN and University of Perugia, I-06100, Perugia, Italy
24 (A)INFN Sezione di Ferrara, I-44122, Ferrara, Italy; (B)University of Ferrara, I-44122, Ferrara, Italy
25 Institute of Physics and Technology, Peace Ave. 54B, Ulaanbaatar 13330, Mongolia
26 Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
27 Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
28 Justus-Liebig-Universitaet Giessen, II. Physikalisches Institut, Heinrich-Buff-Ring 16, D-35392 Giessen, Germany
29 KVI-CART, University of Groningen, NL-9747 AA Groningen, The Netherlands
30 Lanzhou University, Lanzhou 730000, People’s Republic of China
31 Liaoning University, Shenyang 110036, People’s Republic of China
32 Nanjing Normal University, Nanjing 210023, People’s Republic of China
33 Nanjing University, Nanjing 210093, People’s Republic of China
34 Nankai University, Tianjin 300071, People’s Republic of China
35 Peking University, Beijing 100871, People’s Republic of China
36 Shandong University, Jinan 250100, People’s Republic of China
37 Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
38 Shanxi University, Taiyuan 030006, People’s Republic of China
39 Sichuan University, Chengdu 610064, People’s Republic of China
40 Soochow University, Suzhou 215006, People’s Republic of China
41 Southeast University, Nanjing 211100, People’s Republic of China
42 State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People’s Republic of China
43 Sun Yat-Sen University, Guangzhou 510275, People’s Republic of China
44 Tsinghua University, Beijing 100084, People’s Republic of China
45 (A)Ankara University, 06100 Tandogan, Ankara, Turkey; (B)Istanbul Bilgi University, 34060 Eyup, Istanbul, Turkey; (C)Uludag University, 16059 Bursa, Turkey; (D)Near East University, Nicosia, North Cyprus, Mersin 10, Turkey
46 University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
47 University of Hawaii, Honolulu, Hawaii 96822, USA
48 University of Jinan, Jinan 250022, People’s Republic of China
49 University of Minnesota, Minneapolis, Minnesota 55455, USA
50 University of Muenster, Wilhelm-Klemm-Str. 9, 48149 Muenster, Germany
51 University of Science and Technology Liaoning, Anshan 114051, People’s Republic of China
52 University of Science and Technology of China, Hefei 230026, People’s Republic of China
53 University of South China, Hengyang 421001, People’s Republic of China
54 University of the Punjab, Lahore-54590, Pakistan
55 (A)University of Turin, I-10125, Turin, Italy; (B)University of Eastern Piedmont, I-15121, Alessandria, Italy; (C)INFN, I-10125, Turin, Italy
56 Uppsala University, Box 516, SE-75120 Uppsala, Sweden
57 Wuhan University, Wuhan 430072, People’s Republic of China
58 Xinyang Normal University, Xinyang 464000, People’s Republic of China
59 Zhejiang University, Hangzhou 310027, People’s Republic of China
60 Zhengzhou University, Zhengzhou 450001, People’s Republic of China
a Also at Bogazici University, 34342 Istanbul, Turkey
b Also at the Moscow Institute of Physics and Technology, Moscow 141700, Russia
c Also at the Functional Electronics Laboratory, Tomsk State University, Tomsk, 634050, Russia
d Also at the Novosibirsk State University, Novosibirsk, 630090, Russia
e Also at the NRC ”Kurchatov Institute”, PNPI, 188300, Gatchina, Russia
f Also at Istanbul Arel University, 34295 Istanbul, Turkey
g Also at Goethe University Frankfurt, 60323 Frankfurt am Main, Germany
h 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
i Also at Government College Women University, Sialkot - 51310. Punjab, Pakistan.
j 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
Abstract
Using a data sample of events collected at 3.686 GeV with the BESIII detector at the BEPCII, we search for the rare decay via . No signal events are observed and the upper limit on the branching fraction is set to be at the 90% confidence level, which is still about one order of magnitude higher than the Standard Model prediction.
pacs:
13.20.Gd, 13.40.Ks
I INTRODUCTION
The BESIII experiment has accumulated events which is the largest data sample produced directly in annihilation in the world currently. By tagging the two soft pions in the decay of , the final states from decay can be well distinguished. This provides an almost background free sample to investigate the rare decay, which may be sensitive to new physics. The rare decay is one particulary interesting example guoxd . This decay channel occurs mainly through the three dynamic processes shown in Figs. 1(a)-(c). These include: (a) the leading-order electromagnetic (EM) process; (b) the EM and strong mixed loop process; and (c) the EM process proceeding through three virtual photons. In diagram (b), the non-perturbative strong loop can be treated as proceeding through intermediate mesons, as discussed in Ref. guoxd . Within the framework of the Standard Model (SM), the partial widths from the leading EM and mixed loop processes are predicted to be at a level of and keV, respectively, corresponding to branching fractions at the order of and guoxd . However, if there is a new particle involved in the intermediate process, such as a dark photon with a mass of several MeV/ or a glueball with certain quantum numbers, contribution from Fig. 1(b) can be enhanced greatly. Thus, any deviations from the predictions guoxd would hint at the existence of new physics. Alternatively, if a positive result were obtained with a branching fraction in the expected range, this decay channel could be used to extract information of some interesting mesons such as or since their form factors are involved in the predictions.
Although BESIII has also available the currently world’s largest data sample of directly produced , this is not used in the present analysis due to badly-controlled background contamination from QED processes. In this work, we report on search for the rare decay of via .
II BESIII AND BEPCII
The BESIII detector bes3 at the BEPCII collider is a major upgrade of the BESII experiment bes2 at the Beijing Electron-Positron Collider (BEPC) and is optimized to study physics in the -charm energy region. The design peak luminosity of BEPCII, cm*-2s-1* at a center-of-mass energy of MeV, was achieved in 2016. The BESIII detector, with a geometrical acceptance of % of the full solid angle, consists of the following five main components. A small-celled multi-layer drift chamber (MDC) with layers is used for charged track reconstruction and measurement of ionization energy loss (). The average single-wire resolution is m, and the momentum resolution for GeV/ charged particles in a T magnetic field is %. The specific resolution is 6% for electrons from Bhabha scattering. A time-of-flight (TOF) system surrounds the MDC. This system is composed of a two-layer barrel, each layer consisting of pieces of cm thick and m long plastic scintillators, as well as two end caps each with fan-shaped, cm thick, plastic scintillators. The time resolution is ps in the barrel and ps in the end caps, providing a separation of more than for momenta up to GeV/. An electromagnetic calorimeter (EMC) is used to measure photon energies and consists of CsI(Tl) crystals in a cylindrically-shaped barrel and two end caps. For GeV photons, the energy resolution is % in the barrel and % in the end caps, and the position resolution is mm in the barrel and mm in the end caps. A superconducting solenoid magnet surrounding the EMC provides a 1 T magnetic field. The muon chamber system is made of resistive plate chambers with layers in the barrel and layers in the end caps and is incorporated into the return iron yoke of the superconducting magnet. The global position resolution is about cm.
Interactions within the BESIII detector are simulated by the GEANT4-based geant4 simulation software boost boost , which includes: geometric and material descriptions of the BESIII detector; detector response and digitization models; and a record of detector running conditions and performances. The production of the resonance is simulated by the Monte Carlo (MC) generator KKMC kkmc , which incorporates the effects of the energy spread of the beam and initial-state radiation. Known decays are generated by EVTGEN evtgen using the branching fractions quoted by the Particle Data Group (PDG) pdg18 , and the remaining unknown decays are generated with the LUNDCHARM model lundcharm .
In this analysis, the process is studied via , and the meson is reconstructed using its decay to . The transition is generated according to the results of an amplitude analysis as described in Ref. bes2jpsipipi . The process is generated according to the amplitude given in Ref. guoxd , in which the leading-order EM process is expected to be the dominant contribution according to the SM prediction. The spin correlation of produced in the previous decay is considered as in Ref. lifengyun . The decay is generated using a distribution, where is the helicity angle of the kaon in the center-of-mass system of the meson.
III Event Selection
Charged tracks are reconstructed with MDC hits within the range , where is the polar angle with respect to the electron beam direction. They are required to originate from the interaction region, defined as cm and cm, where and are the projections of the distances from the closest approach of the tracks to the interaction point in the -plane and in the -direction, respectively.
Particle identification (PID) probabilities for good charged tracks are calculated with the and TOF measurements under the hypothesis that the track originated from a pion, kaon, proton or electron. For kaon candidates, we require that the probability for the kaon hypothesis is larger than the corresponding probability for the pion and proton hypotheses. For electron candidates, the probability for electron hypothesis is required to be larger than the probabilities for the pion and kaon hypotheses. To avoid contamination from pions, electron candidates must satisfy the additional requirement , where and represent the energy deposited in the EMC and the momentum of the electron, respectively.
For the two pions, no PID selection criterion is required. All pairs of opposite charged tracks with momentum less than GeV/ are assumed to be pions, and their recoil masses are calculated and required to be within the range GeV/.
To improve the mass resolution and suppress backgrounds, an energy-momentum constrained kinematic fit (C) to the initial beam four momentum is imposed on the selected charged tracks. The resulting of the kinematic fit is required to be less than . If more than one combination is found in an event, the combination with the least is retained for further analysis.
IV Analysis
The process is studied by examining the two-dimensional distribution of the versus the invariant mass of the pair, . Figure 2(a) shows the distribution for the signal MC sample, where the signal region (shown as a red solid box) is defined as GeV/ and GeV/, where and are the nominal masses of and mesons in taken from PDG pdg18 , respectively. The five boxes with equal area around the signal region are selected as sideband regions, which are categorized into three types. The first type is used to estimate the background without a in the intermediate state; the second one is for the estimation of the background without a in the intermediate state. These first two are shown as the pink dashed boxes and the green dashed double dotted box, respectively. The third type is for the estimation of the background that includes neither a nor a in the intermediate state, and is shown as blue dashed dotted boxes. Figure 2(b) shows the corresponding plot for the data sample. No events are observed in the signal region and two events are observed in the sideband. The non-flat non- background, mainly due to the threshold effect, is estimated by with and . The scale factor is determined to be for the background in the signal region to that in the sideband region. Therefore, the scaled background estimated by the sideband data is events. The projections of Fig. 2(b) on and are shown in Figs. 3(a) and (b), respectively.
The backgrounds from decays are also studied with an inclusive MC sample of 506 million events. No events survive in the signal region, and only one event is found in the second type of sideband region. This event is from , which will not form a peak in signal region. In addition, the potential peaking backgrounds from with and are studied through exclusive MC events generated with a size corresponding to more than 100 times of that of data. The contribution from these channels is negligible.
Possible background sources from continuum processes are estimated with pb*-1* of data collected at a center-of-mass energy GeV condata , which is about one fifteenth of the integrated luminosity of the data. There are no events satisfying the above selection criteria, therefore we neglect the continuum background.
V Systematic Uncertainties
The systematic uncertainties originate mainly from the number of events, the tracking efficiency, the PID efficiency, the kinematic fit, the selection of the and signal regions, background estimation, MC statistics, and the branching fractions of intermediate decays. These are discussed in detail in the following, and are summarized in Table 1.
The uncertainty from the total number of events is estimated to be % npsip .
The tracking efficiencies for mesons have been studied with the process , . The difference in the efficiencies between data and MC simulation is % per pion ktrkpid . The tracking efficiencies for mesons as functions of transverse momentum have been studied with the process , . The difference in the efficiencies between data and MC simulation is % per kaon ktrkpid . The tracking efficiencies for are obtained with a control sample of radiative Bhabha scattering (including ) at the resonance in Ref. etrk . The difference in tracking efficiencies between data and MC simulation is calculated bin-by-bin over the distribution of transverse momentum versus the polar angle of the lepton tracks. The uncertainty is determined to be % per electron/positron. The systematic uncertainties arising from the different charged tracks are summed linearly to be %.
High purity control samples of and have been selected to study the electron/positron and kaon PID uncertainty. The difference of PID efficiency between data and MC simulation is calculated in bins of momentum and cos. Averaged systematic uncertainties for electron/positron and kaon identification are obtained by weighting the difference with the events in each bin of momentum and cos from the signal MC sample, and determined to be % per electron/positron and % per kaon. Adding these values linearly, the PID systematic uncertainty is determined to be %.
The systematic uncertainty of the C kinematic fit is studied using a control sample of with , . The efficiency difference between data and MC simulation with the requirement is %, which is assigned as the systematic uncertainty.
The uncertainty from the signal regions of and , due to their resolution difference between data and MC simulation, is studied by means of the control sample , , . The efficiency differences in the and signal regions between data and MC simulation are % and %, respectively. Adding them in quadrature yields %, which is taken as the systematic uncertainty.
The uncertainty on the background estimation is studied by an alternative estimation of the scale factor (), estimated using the inclusive MC sample instead of data. The difference between the resulting upper limits is taken as the systematic uncertainty, which is %.
The uncertainty of the detection efficiency attributed to the limited size of the MC sample, %, is taken as the systematic uncertainty from MC statistics.
To estimate the uncertainty from the model used to simulate the decay, we generated a MC sample based on the phase-space assumption. The difference between the efficiencies determined from the MC sample described in Sec. II and the phase space MC sample is %. We take half of the difference (%) as the systematic uncertainty from MC modeling.
In the determination of the upper limit on the branching fraction of the process of interest, we have accounted for the branching fractions of and by taking the values given by the PDG pdg18 . The uncertainties of these cited values are taken as a source of systematic uncertainty, which are % and %, respectively.
The total systematic uncertainty, , is calculated by adding the uncertainties from all sources in quadrature.
VI Result
Since no candidate events are observed in the signal region and background events are estimated, the upper limit on the number of events with is set to be at the % confidence level (CL) using the Feldman-Cousins feldman method with the assumption of a Poisson process.
After taking into account the systematic uncertainty cousins , the upper limit of the branching fraction of is calculated with
[TABLE]
where is the number of events, represents the branching fraction product and is the detection efficiency, which is % determined by MC simulations as described in Sec. II. Table 2 summarizes the various values that were used as input to Eq. (1). We find an upper limit on the branching fraction of the process at the 90% CL of
[TABLE]
VII Summary
Using the events collected with the BESIII detector, we report a search for the rare decay via . No signal events are observed and the upper limit of the branching fraction for this decay is calculated to be by the Feldman-Cousins method at the % CL, which is one order of magnitude higher than the prediction in Ref. guoxd . Our result shows that, even if there are new particles involved in the Fig. 1(b) process, their contributions will not be too large, and the masses of the possible existing new particles or their coupling information to vector mesons ( and ) can also be constrained.
VIII Acknowledgements
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 Basic Research Program of China under Contract No. 2015CB856700; National Natural Science Foundation of China (NSFC) under Contracts Nos. 11575077, 11475090, 11235011, 11335008, 11425524, 11625523, 11635010, 11735014, 11835012; the Outstanding Youth project of Natural Science Foundation of Hunan Province; 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. U1532257, U1532258, U1732263, U1832207; CAS Key Research Program of Frontier Sciences under Contracts Nos. QYZDJ-SSW-SLH003, QYZDJ-SSW-SLH040; 100 Talents Program of CAS; INPAC and Shanghai Key Laboratory for Particle Physics and Cosmology; German Research Foundation DFG under Contract No. Collaborative Research Center CRC 1044; Istituto Nazionale di Fisica Nucleare, Italy; Koninklijke Nederlandse Akademie van Wetenschappen (KNAW) under Contract No. 530-4CDP03; Ministry of Development of Turkey under Contract No. DPT2006K-120470; National Science and Technology fund; The Knut and Alice Wallenberg Foundation (Sweden) under Contract No. 2016.0157; The Swedish Research Council; U. S. Department of Energy under Contracts Nos. DE-FG02-05ER41374, DE-SC-0010118, DE-SC-0012069; University of Groningen (RuG) and the Helmholtzzentrum für Schwerionenforschung GmbH (GSI), Darmstadt.
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