Measurements of the branching fractions of $\eta_c\to K^+K^-\pi^0$, $K^0_S K^{\pm}\pi^{\mp}$,$2(\pi^+\pi^-\pi^0)$, and $p \bar{p}$
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, J. Bloms, I. Boyko, R. A. Briere, H. Cai

TL;DR
This study measures the branching fractions of various $ ext{eta}_c$ decay modes using BESIII data, providing new insights into decay probabilities and the first measurement of charged track multiplicity in $ ext{eta}_c$ decays.
Contribution
It presents the first measurement of charged track multiplicity in $ ext{eta}_c$ decays and provides precise branching fractions for multiple decay channels.
Findings
Branching fraction of $ ext{eta}_c o K^+K^-\pi^0$ is approximately 1.15%.
Branching fraction of $ ext{eta}_c o K^0_S K^{\pm}\pi^{\mp}$ is approximately 2.60%.
Branching fraction of $ ext{eta}_c o 2(\pi^+\pi^-\pi^0)$ is approximately 15.2%.
Abstract
Using data samples collected with the BESIII detector at center-of-mass energies and ~\rm{GeV}, we measure the branching fractions of , , , and , via the process , . The corresponding results are , , , and , respectively. Here the first uncertainties are statistical, and the second ones systematic. Additionally, the charged track multiplicity of decays is measured for the first time.
| Decay mode | Other requirements | ||
|---|---|---|---|
| - |
| (GeV) | ||||
|---|---|---|---|---|
| 4.23 | 45 | 25 | 35 | 40 |
| 4.26 | 45 | 15 | 30 | 40 |
| 4.36 | 45 | 25 | 25 | 40 |
| 4.42 | 50 | 20 | 35 | 40 |
| Category | (%) | BF (%) | ||
|---|---|---|---|---|
| Decay modes | ||||
| 4.23 | 15.95 | |||
| 4.26 | 15.33 | |||
| 4.36 | 18.82 | |||
| 4.42 | 17.92 | |||
| sum | - | |||
| 4.23 | 17.50 | |||
| 4.26 | 19.67 | |||
| 4.36 | 20.67 | |||
| 4.42 | 21.22 | |||
| sum | - | |||
| 4.23 | 2.93 | |||
| 4.26 | 2.60 | |||
| 4.36 | 3.38 | |||
| 4.42 | 3.07 | |||
| sum | - | |||
| 4.23 | 34.68 | |||
| 4.26 | 37.67 | |||
| 4.36 | 40.00 | |||
| 4.42 | 40.72 | |||
| sum | - | |||
| Inclusive decays | 4.23 | 40.45 | 8 314584 | - |
| 4.26 | 45.17 | 6 651499 | ||
| 4.36 | 46.59 | 6 420420 | ||
| 4.42 | 46.69 | 11 083615 | ||
| Normalized values | |
|---|---|
| 0 | |
| 2 | |
| 4 | |
| 6 | |
| Category (%) | |||||
|---|---|---|---|---|---|
| Tracking | 4.0 | 3.0 | 4.0 | 4.0 | |
| PID | 2.0 | 2.0 | 4.0 | 2.0 | |
| reconstruction | 3.75 | - | 3.23 | - | |
| Kinematic Fit | 0.46 | 0.30 | 1.09 | 0.07 | |
| reconstruction | - | 1.2 | - | - | |
| MC model | 0.85 | 0.79 | 1.49 | 0.73 | |
| mass window | 1.93 | 2.35 | 3.01 | 5.91 | |
| Fitting | Fitting range | 5.62 | 5.21 | 6.56 | 3.65 |
| Background shape (exclusive) | 0.60 | 0.63 | 5.12 | 8.37 | |
| Sidebands range (inclusive) | 1.17 | 1.26 | 1.25 | 1.14 | |
| Background form (inclusive) | 2.63 | 2.73 | 2.67 | 2.71 | |
| Mass resolution | 0.06 | 0.10 | 0.14 | 0.10 | |
| Resonant parameters of | 0.81 | 0.81 | 0.38 | 0.79 | |
| Damping factors | 0.89 | 1.57 | 1.09 | 1.74 | |
| Total | 9.0 | 7.7 | 11.6 | 12.3 | |
| Category (%) | ||||||
|---|---|---|---|---|---|---|
| Tracking | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | |
| PID | 2.0 | 2.0 | 2.0 | 2.0 | 2.0 | |
| MC model | intermediate states | 4.19 | 3.46 | 5.22 | 7.47 | 7.47 |
| multiplicity | 10.40 | 10.60 | 11.76 | 9.31 | 8.87 | |
| mass window | 11.70 | 3.54 | 3.01 | 5.91 | 15.26 | |
| Fit | Fitting range | 5.92 | 3.84 | 1.13 | 4.28 | 6.34 |
| Background shape | 8.04 | 3.41 | 1.96 | 8.96 | 11.80 | |
| Mass resolution | 0.14 | 0.10 | 0.01 | 0.32 | 0.46 | |
| Resonant parameters of | 0.68 | 0.34 | 0.44 | 0.65 | 0.85 | |
| Damping factors | 1.35 | 0.34 | 0.34 | 0.56 | 4.10 | |
| Total | 19.3 | 13.1 | 13.7 | 16.9 | 23.9 | |
| Final states | BF (%) | BF (%) from Ref. guo_aiqiang_etac | BF (%) from PDG pdg |
|---|---|---|---|
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Measurements of the branching fractions of
, , , and
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, J. Bloms50, 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, S. J. Chen33, Y. B. Chen1,42, W. Cheng55C, 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, J. Z. Fan44, J. Fang1,42, S. S. Fang1,46, Y. Fang1, R. Farinelli24A,24B, L. Fava55B,55C, F. Feldbauer4, G. Felici23A, C. Q. Feng52,42, M. Fritsch4, C. D. Fu1, Y. 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, S. Han57, X. Q. Hao16, F. A. Harris47, K. L. He1,46, 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, N. Hüsken50, W. Ikegami Andersson56, W. Imoehl22, 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, 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. Kuemmel4, M. Kuessner4, A. Kupsc56, M. Kurth1, W. Kühn28, J. S. Lange28, P. Larin15, L. Lavezzi55C, 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, Ke Li1, L. K. Li1, Lei Li3, P. L. Li52,42, P. R. Li30, Q. Y. Li36, W. D. Li1,46, W. G. Li1, X. L. Li36, X. N. Li1,42, X. Q. Li34, X. H. Li52,42, 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, 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. D. Lu1,46, J. G. Lu1,42, Y. Lu1, Y. P. Lu1,42, C. L. Luo32, M. X. Luo59, P. W. Luo43, 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. X. Ma1,46, 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,k, S. L. Niu1,42, S. L. Olsen46, Q. Ouyang1,42,46, S. Pacetti23B, Y. Pan52,42, M. Papenbrock56, P. Patteri23A, M. Pelizaeus4, H. P. Peng52,42, K. Peters11,g, J. Pettersson56, J. L. Ping32, R. G. Ping1,46, A. Pitka4, R. Poling49, V. Prasad52,42, 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, M. Rump50, 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, X. D Shi52,42, J. J. Song36, Q. Q. Song52,42, 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, B. Tsednee25, I. Uman45D, B. Wang1, B. L. Wang46, C. W. Wang33, D. Y. Wang35, H. H. Wang36, K. Wang1,42, L. L. Wang1, L. S. Wang1, M. Wang36, Meng Wang1,46, P. Wang1, P. L. Wang1, R. M. Wang58, W. P. Wang52,42, X. Wang35, 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, Y. Xia20, 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, L. Xu1, Q. J. Xu14, W. Xu1,46, 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. 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, Y. Zheng35, Y. H. Zheng46, B. Zhong32, L. Zhou1,42, Q. Zhou1,46, X. Zhou57, X. K. Zhou52,42, X. R. Zhou52,42, Xiaoyu Zhou20, Xu Zhou20, A. N. Zhu1,46, J. Zhu34, J. Zhu43, K. Zhu1, K. J. Zhu1,42,46, 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 University Islamabad, Lahore Campus, 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
k Also at Harvard University, Department of Physics, Cambridge, MA, 02138, USA
Abstract
Using data samples collected with the BESIII detector at center-of-mass energies and GeV, we measure the branching fractions of , , , and , via the process , . The corresponding results are , , , and , respectively. Here the first uncertainties are statistical, and the second ones systematic. Additionally, the charged track multiplicity of decays is measured for the first time.
pacs:
12.38.Qk, 14.40.Pq, 13.25.Gv
I INTRODUCTION
Many new charmonium or charmonium-like states have been discovered recently xyz_states , which broaden our horizon on understanding the charmonium family. These states have led to a revived interest in improving the quark-model picture of hadrons. However, the knowledge of the lowest lying charmonium state, , is relatively poor compared to the other charmonium states. The reason is that most of the measurements involving were performed using M1 transitions from or hindered M1 transitions from . In these decays, the interference between and non- amplitudes affects the lineshape getac . The branching fraction (BF) of decays and the M1 transition rate are entangled. The insufficient understanding of the properties has so far prevented precise studies of decays themselves or of decays involving the . For example, in 2002, the Belle Collaboration release the measurements on the total cross section of the exclusive production of via the annihilation at the center-of-mass collision energy multiplicity with the result of . These measurements were improved as multiplicity_2 . In 2005, the BABAR Collaboration independently measured the total cross section as multiplicity_3 . As the number of charged tracks is required in these measurements, the results will be improved if the charged tracks multiplicity is fully studied.
Recently, the E1 transition was found to be a perfect process to measure both resonant parameters and its decay BFs guo_aiqiang_etac . In addition, the production proceeds via , where the interference effect between and non- is much less than that in radiative transition. One can draw such a conclusion according to the following calculation. The 1 transition rate, , is about 2 orders of magnitude larger than that of the 1 transition pdg . On the other hand, the background that can interfere with the signal comes from charmonium radiative decays, e.g. . If we assume the radiative decay rates of and to be at the same level, therefore, this kind of background in the process should be 1 to 2 orders of magnitude less than in .
BESIII has collected sizable data samples between 4.009 and 4.600 GeV (called “XYZ data” hereafter) since 2013 to study the XYZ states xyz_luminosities . A large production rate of has been found guo_yuping_zc . The total number of events in all these data samples combined is comparable to that from decays in BESIII data, according to the measured cross section and the corresponding integrated luminosity at each energy point. The is tagged by the recoil mass () of in XYZ data, while it is tagged by the recoil mass of in data. Generally, the two-charged-pion mode has lower background and higher detection efficiency than the neutral pion mode.
In this paper, we report a measurement of the BFs of four exclusive decays via the process , . These exclusive decays are , , , and , respectively.
Apart from the BF measurement mentioned above, we also measure the charged tracks multiplicities in inclusive decays by using an unfolding method N_psip .
II METHODOLOGY
The BFs of exclusive decays are obtained by a simultaneous fit to the spectrum of for both inclusive and exclusive modes. The BFs are common parameters independent of the center of mass energy. The numbers of the signal events of the exclusive and inclusive decay modes can be calculated by the following formulas,
[TABLE]
and
[TABLE]
where the subscript denotes the different center-of-mass energy points. and denote the luminosity and cross section, respectively. denotes a certain exclusive decay mode, denotes the possible or final state from decay. denotes the detection efficiency determined by Monte Carlo (MC) simulations.
By comparing Eq. (1) and Eq. (2), can be extracted as
[TABLE]
In the simultaneous fit, the total number of free parameters is less than in the fits taken individually, due to common parameters such as the mass and width, etc. In addition, some parameters, for example, , , are not necessary in the measurement according to Eq. (3), resulting in reduced statistical uncertainties. In addition, systematic uncertainties from the same sources, e.g., the tracking efficiency of two pions from , can be canceled.
III DETECTOR AND DATA SAMPLES
The BESIII detector is a magnetic spectrometer Ablikim:2009aa 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 (MDC), a plastic scintillator time-of-flight system (TOF), and a CsI(Tl) electromagnetic calorimeter (EMC), 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 solid angle. The charged-particle momentum resolution at is , and the specific energy loss () resolution is for the 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.
The data samples collected at 4 center-of-mass energies, i.e. , , , and GeV xyz_luminosities , are used for our studies. Simulated samples produced with the geant4-based geant4 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 (ISR) in the annihilations modeled with the generator kkmc ref:kkmc .
The inclusive MC samples with equivalent luminosities the same as the data samples consist of the production of open charm processes, the ISR production of vector charmonium(-like) states, and the continuum processes incorporated in kkmc ref:kkmc . The known decay modes are modeled with evtgen ref:evtgen using branching fractions taken from PDG pdg , and the remaining unknown decays from the charmonium states with lundcharm ref:lundcharm . The final state radiations (FSR) from charged final state particles are incorporated with the photos package photos .
Signal MC samples with 200 000 events each are generated for each decay mode (inclusive and exclusive decays) at each center-of-mass energy. ISR is simulated using kkmc with a maximum energy for the ISR photon corresponding to the mass threshold. The E1 transition is generated with an angular distribution of , where is the angle of the E1 photon with respect to the helicity direction in the rest frame. The inclusive decays of are produced similarly to the inclusive MC samples.
IV EVENT SELECTIONS
In this analysis, the signal is tagged with by requiring in signal region. For the inclusive mode, at least two charged tracks and one photon is required. For the exclusive modes, the requirements on charged tracks and photon candidates depend on their respective final state.
Charged tracks at BESIII are reconstructed from MDC hits within a polar-angle () acceptance range of . We require that these tracks pass within 10 cm of the interaction point in the beam direction and within 1 cm in the plane perpendicular to the beam. Tracks used in reconstructing decays are exempted from these requirements.
A vertex fit constrains charged tracks to a common production vertex, which is updated on a run-by-run basis. For each charged track, TOF and information is combined to compute particle identification (PID) confidence levels for the pion, kaon, and proton hypotheses.
Electromagnetic showers are reconstructed by clustering EMC crystal energies. Efficiency and energy resolution are improved by including energy deposits in nearby TOF counters. A photon candidate is defined as an isolated shower with an energy deposit of at least 25 MeV in the barrel region (), or of at least 50 MeV in the end-cap region (). Showers in the transition region between the barrel and the end-cap are not well measured and are rejected. An additional requirement on the EMC hit timing suppresses electronic noise and energy deposits unrelated to the event.
A candidate is reconstructed from pairs of photons with an invariant mass in the range MeV/c2 pdg . A one-constraint (1-C) kinematic fit is performed to improve the energy resolution, with the constrained to the known mass.
We reconstruct candidates using pairs of oppositely charged tracks with an invariant mass in the range MeV/c2, where is the known mass pdg . To reject random combinations, a secondary-vertex fitting algorithm is employed to impose the kinematic constraint between the production and decay vertices vertex . Accepted candidates are required to have a decay length of at least twice the vertex resolution. If there is more than one combinations in an events, the one with the smallest of the secondary vertex fit is retained.
In selecting the candidates of the inclusive decay, all charged tracks are assumed to be pions, and events with at least one combination satisfying and are kept for further analysis. The region satisfying GeV/ is taken as the signal region, while the regions satisfying GeV/ or GeV/ are taken as the sidebands region. Figure 1 shows the distribution of for all combinations from the inclusive decay mode in signal MC and data (summed over four center-of-mass energies), respectively.
For the selection of exclusive decays, the requirements on the number of photons and charged tracks are listed in Table 1. A four-constraint (4C) kinematic fit imposing overall energy-momentum conservation is performed. To determine the species of final state particles and to select the best combination when additional photons (or candidates) are found in an event, the combination with the minimum value of is selected for further analysis, where is the from the four-momentum conservation kinematic fit and is the sum of the 1C (mass constraint of the two daughter photons) of the in the final state. is the from PID of different particle hypothesis, using the energy loss in the MDC and the time measured with the TOF system, is the number of the charged tracks in the final states. is the of the vertex fit in reconstruction. The is required to be not more than 50 depending on the decay modes, which is optimized using the figure of merit , where is the number of signal events obtained from MC simulation (normalized to data luminosity), while is the number of background events obtained from the sidebands of in data.
The requirement on for the different exclusive decay modes are listed in Table 2. In addition, we require the same mass windows on the spectra for both inclusive and exclusive modes.
V Numerical results of
A simultaneous unbinned maximum likelihood fit to the spectrum of the exclusive decays and the inclusive decay of at the four center-of-mass energies is performed to obtain the branching fractions . The fit function is parameterized as follows:
[TABLE]
where the signal function is described by a Breit-Wigner function, , convolved with the detection resolution, . The mass and width of are fixed to the nominal values taken from the PDG pdg . represents the recoil mass . The detection resolution is described by a double Gaussian function, whose parameters are obtained from MC simulations. is the efficiency curve, obtained from a fit of the efficiencies along the spectrum with a polynomial function and fixed in the fit to data. Figure 2 shows the efficiencies along the spectrum for the inclusive decay and the exclusive decay at .
is the energy of the transition photon, where is the mass pdg .
is the damping factor damping_factor_KEDR , where is the most probable transition energy.
denotes the function which is used to describe the background shape. For an exclusive decay mode, a polynomial function is used. For the inclusive decay mode, it is a combination of the distribution from sidebands and a polynomial function.
Figure 3 shows the simultaneous fit results.
The fitted BFs are summarized in Table 3, together with the detection efficiencies and signal yields at each energy point.
VI CHARGED TRACK MULTIPLICITY OF
INCLUSIVE DECAYS
The MC simulation for the inclusive decay has been introduced in section III. The performance of the inclusive simulation, to some extent, can be investigated by the consistency of the charged track multiplicity R_scan ; N_Jpsi ; N_psip . Below, we introduce how to obtain the true charged track multiplicity of inclusive decay. An even number of charged tracks is generated in an event due to the charge conservation, while any number of charged tracks can be observed due to the detector acceptance and reconstruction efficiency. The observed charged track multiplicity of can be obtained by fitting for the signal in the recoil mass with the number of extra candidate tracks required to be 0, 1, 2, 3, , respectively. To obtain the charged track multiplicity at the production level, an unfolding method is employed based on an efficiency matrix, whose matrix elements, , represent the probabilities of an event generated with tracks being observed with tracks. The efficiency matrix is determined from the inclusive MC samples. The unfolding of data is achieved by minimizing a value, defined as
[TABLE]
where the values are the observed multiplicities of charged tracks in the data sample, are the corresponding uncertainties, while are the true multiplicities of charged tracks at the production level in the data sample. For simplicity, the events with eight or more tracks are considered in a single value, , so are the efficiencies, .
Figure 4 shows the charged track multiplicity distribution of inclusive decays after combining the data at the four center-of-mass energies.
According to Eq. (5), the normalized numerical results are summarized in Table 4.
VII SYSTEMATIC UNCERTAINTIES
VII.1 measurement of
The systematic uncertainties on the BF measurements for exclusive decays from different sources are described below and listed in Table 5. The total systematic uncertainty is determined by the sum in quadrature of the individual values, assuming all sources to be independent.
VII.1.1 MDC tracking and PID
The uncertainty from the tracking efficiency and PID for the two soft pions in the process cancels since the BFs are measured by a relative method, as mentioned in the introduction. We only consider the uncertainty from tracking efficiency and PID of the decay products. The involved charged tracks are pions (not including the pions from decay), kaons, and protons. Their uncertainties are studied with different control samples, for pions and kaons, () for protons, The uncertainties from tracking efficiency are 1% for each pion, and 2% for each kaon or proton. The uncertainties for PID are 1% for each pion, kaon or proton.
VII.1.2 reconstruction
The systematic uncertainty from reconstruction is studied with using events and using a data sample of collected at the resonance. The uncertainty as a function of momentum is determined. The uncertainty from reconstruction is calculated with the function, according to the momentum distribution of the in the decays studied.
VII.1.3 Kinematic fit
The systematic uncertainty from the kinematic fit is estimated by correcting the helix parameters of the charged tracks in the MC simulation kinematic_fit . The differences in the detection efficiency between the MC samples with and without the corrections are taken as the uncertainties due to the kinematic fit.
VII.1.4 reconstruction
The reconstruction is studied with two control samples, and . The difference in the reconstruction efficiency between the MC simulation and the data is 1.2% ks , which is taken as the uncertainty due to reconstruction.
VII.1.5 MC model
In the MC simulation, the process is modeled with a phase space (phsp) distribution. In fact, there is a confirmed intermediate state and a potential intermediate state , in the final state. The uncertainty caused by the intermediate states is estimated by mixing the MC events including / component according to the measured fractions guo_yuping_zc ; zc_3900 . The difference in the detection efficiency is taken as the uncertainty.
For the exclusive decay modes, intermediate resonant states may affect the detection efficiency. MC samples related to multi-body decays are generated by sampling according to the invariant mass distribution or mixing the known intermediate states, or changing the decay model used in the MC simulation. The difference in the efficiency with and without intermediate states is taken as the uncertainty.
The uncertainty due to the inconsistency between data and MC simulation on the charged track multiplicity in inclusive decays is estimated based on the multiplicity obtained by the unfolding method mentioned in Sec. VI. The detection efficiency for inclusive decay can also be re-calculated with the following formula,
[TABLE]
where are the normalized multiplicities in data, listed in Table 4, and are the elements of the efficiency matrix in Eq. (5). The differences between this result and the original one are taken into account in the simultaneous fit. It is found that the influence on is negligible.
VII.1.6 mass window
The uncertainty from the mass window is estimated by randomly changing the low and high boundaries of the signal region in the ranges of and and fitting the spectrum with efficiencies estimated in the corresponding intervals. The procedure is repeated for 800 times, and the distributions of the fitted BFs follow Gaussian functions. The obtained standard deviations are taken as the uncertainties due to the mass window selection.
VII.1.7 Fit procedure
This uncertainty arises from the fit range, the background shape, the mass resolution, the parameters of the resonance, the efficiency curves, and the damping factor.
The uncertainty from the fit range is estimated by randomly changing the lower side in the range of GeV/ and higher side in GeV/ and repeating the fit for 800 times. The root mean square (RMS) of the resulting distributions are taken as the systematic uncertainties from the fit range.
The uncertainty due to the assumed background shape in the exclusive modes is estimated by changing the order of the Chebychev polynomial functions. For the inclusive decay mode, the sidebands need to be considered as well, whose systematic uncertainty is estimated by randomly changing the left and right margins of the lower and upper sidebands and repeating the fit. The procedure is performed 800 times. The left and right margins of the sidebands are changed in the ranges of and for the lower and upper sideband regions, respectively. The distributions of the fitted results follow Gaussian functions, and the standard deviations are taken as the uncertainties from the sidebands selection. The uncertainty from the polynomial is estimated by changing the order of the polynomial.
The discrepancy between data and MC simulation on detection resolution is estimated by a control sample, , , . By fitting the signals, we can obtain the mass resolution for both data and MC. We change the mass resolutions according to the result obtained from control sample to re-fit the . The differences on the BFs with and without changing the mass resolution are taken as the systematic uncertainties.
The resonance parameters are fixed to the world average values in the fit. We change these values by , and the larger difference is taken as the uncertainty.
The efficiency curves, as shown in Fig. 2, change slowly with . We find only a very small change in results when constant efficiencies are used. Therefore, the uncertainties due to efficiencies can be neglected.
The uncertainty from the damping factor is estimated by using an alternative form of the damping factor, which is used in the CLEO’s published paper cleo . The differences between the results with the two forms of damping factor are taken as the systematic uncertainty.
VII.2 Charged track multiplicity
The systematic uncertainties on the charged track multiplicity in inclusive decay from different sources are described below and listed in Table 6. They are estimated in a similar way as introduced in Sec. VII.1. The total systematic uncertainty is determined by the sum in quadrature of the individual values, assuming that all the sources are independent.
VII.2.1 MDC tracking and PID
The uncertainties from MDC tracking and PID are the same as those in the measurement of .
VII.2.2 mass window
The uncertainties are estimated by changing the mass window from to and . The largest changes on the multiplicity are taken as the uncertainty.
VII.2.3 MC model
Similar to that in measurement of , the uncertainty due to MC model mainly comes from the potential intermediate states and the inclusive decay. The uncertainty from the former is estimated as before, while the latter is estimated by removing the unknown modes simulated by lundcharm model, and only considering the known decay modes.
VII.2.4 Fit
The uncertainties due to the fit to the recoil mass spectra of are evaluated by varying the fit range, sideband ranges, mass resolution, resonant parameters of , and damping factors used in the fit, in similar ways as introduced in Sec. VII.1. The spreads of the results obtained with the alternative assumptions are used to assign the systematic uncertainties.
VIII SUMMARY
In summary, with the data samples collected at 4.23, 4.26, 4.36, and 4.42 GeV, by comparing the exclusive and inclusive decays of , we determine the BFs for , , , and via , . The results are presented in Table 7; they agree with previous measurements by BESIII guo_aiqiang_etac within uncertainties, while the accuracy of these BFs is improved. With this improved accuracy, the measurements of the M1 transitions of and can be more precise, since such measurements provide combined results of .
Moreover, the charged track multiplicity of inclusive decay at production level is quantitatively presented for the first time in Table 4. The good consistency between data and MC simulation for this charged track multiplicity indicates that the current MC simulation works generally well. With this charged track multiplicity, many studies with in the final state b2 are possible with higher precision than previously.
IX 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 Basic Research Program of China under Contract No. 2015CB856700; National Natural Science Foundation of China (NSFC) under Contracts Nos. 11335008, 11425524, 11625523, 11635010, 11735014; the Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program; the CAS Center for Excellence in Particle Physics (CCEPP); Joint Large-Scale Scientific Facility Funds of the NSFC and CAS under Contracts Nos. U1532257, U1532258, U1732263; 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 Nos. Collaborative Research Center CRC 1044, FOR 2359; 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 Swedish Research Council; U. S. Department of Energy under Contracts Nos. DE-FG02-05ER41374, DE-SC-0010118, DE-SC-0010504, DE-SC-0012069; University of Groningen (RuG) and the Helmholtzzentrum fuer Schwerionenforschung GmbH (GSI), Darmstadt.
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