Measurement of Branching Fractions of Singly Cabibbo-suppressed Decays and
M. Ablikim1, M. N. Achasov11,b, P. Adlarson70, M. Albrecht4, R. Aliberti31, A. Amoroso69A,69C, M. R. An35, Q. An66,53, X. H. Bai61, Y. Bai52, O. Bakina32, R. Baldini Ferroli26A, I. Balossino1,27A, Y. Ban42,g, V. Batozskaya1,40, D. Becker31, K. Begzsuren29, N. Berger31, M. Bertani26A, D. Bettoni27A, F. Bianchi69A,69C, J. Bloms63, A. Bortone69A,69C, I. Boyko32, R. A. Briere5, A. Brueggemann63, H. Cai71, X. Cai1,53, A. Calcaterra26A, G. F. Cao1,58, N. Cao1,58, S. A. Cetin57A, J. F. Chang1,53, W. L. Chang1,58, G. Chelkov32,a, C. Chen39, Chao Chen50, G. Chen1, H. S. Chen1,58, M. L. Chen1,53, S. J. Chen38, S. M. Chen56, T. Chen1, X. R. Chen28,58, X. T. Chen1, Y. B. Chen1,53, Z. J. Chen23,h, W. S. Cheng69C, S. K. Choi50, X. Chu39, G. Cibinetto27A, F. Cossio69C, J. J. Cui45, H. L. Dai1,53, J. P. Dai73, A. Dbeyssi17, R. E. de Boer4, D. Dedovich32, Z. Y. Deng1, A. Denig31, I. Denysenko32, M. Destefanis69A,69C, F. De Mori69A,69C, Y. Ding36, J. Dong1,53, L. Y. Dong1,58, M. Y. Dong1,53,58, X. Dong71, S. X. Du75, P. Egorov32,a, Y. L. Fan71, J. Fang1,53, S. S. Fang1,58, W. X. Fang1, Y. Fang1, R. Farinelli27A, L. Fava69B,69C, F. Feldbauer4, G. Felici26A, C. Q. Feng66,53, J. H. Feng54, K Fischer64, M. Fritsch4, C. Fritzsch63, C. D. Fu1, H. Gao58, Y. N. Gao42,g, Yang Gao66,53, S. Garbolino69C, I. Garzia27A,27B, P. T. Ge71, Z. W. Ge38, C. Geng54, E. M. Gersabeck62, A Gilman64, K. Goetzen12, L. Gong36, W. X. Gong1,53, W. Gradl31, M. Greco69A,69C, L. M. Gu38, M. H. Gu1,53, Y. T. Gu14, C. Y Guan1,58, A. Q. Guo28,58, L. B. Guo37, R. P. Guo44, Y. P. Guo10,f, A. Guskov32,a, T. T. Han45, W. Y. Han35, X. Q. Hao18, F. A. Harris60, K. K. He50, K. L. He1,58, F. H. Heinsius4, C. H. Heinz31, Y. K. Heng1,53,58, C. Herold55, M. Himmelreich31,d, G. Y. Hou1,58, Y. R. Hou58, Z. L. Hou1, H. M. Hu1,58, J. F. Hu51,i, T. Hu1,53,58, Y. Hu1, G. S. Huang66,53, K. X. Huang54, L. Q. Huang67, L. Q. Huang28,58, X. T. Huang45, Y. P. Huang1, Z. Huang42,g, T. Hussain68, N Hüsken25,31, W. Imoehl25, M. Irshad66,53, J. Jackson25, S. Jaeger4, S. Janchiv29, E. Jang50, J. H. Jeong50, Q. Ji1, Q. P. Ji18, X. B. Ji1,58, X. L. Ji1,53, Y. Y. Ji45, Z. K. Jia66,53, H. B. Jiang45, S. S. Jiang35, X. S. Jiang1,53,58, Y. Jiang58, J. B. Jiao45, Z. Jiao21, S. Jin38, Y. Jin61, M. Q. Jing1,58, T. Johansson70, N. Kalantar-Nayestanaki59, X. S. Kang36, R. Kappert59, M. Kavatsyuk59, B. C. Ke75, I. K. Keshk4, A. Khoukaz63, P. Kiese31, R. Kiuchi1, R. Kliemt12, L. Koch33, O. B. Kolcu57A, B. Kopf4, M. Kuemmel4, M. Kuessner4, A. Kupsc40,70, W. Kühn33, J. J. Lane62, J. S. Lange33, P. Larin17, A. Lavania24, L. Lavezzi69A,69C, Z. H. Lei66,53, H. Leithoff31, M. Lellmann31, T. Lenz31, C. Li43, C. Li39, C. H. Li35, Cheng Li66,53, D. M. Li75, F. Li1,53, G. Li1, H. Li47, H. Li66,53, H. B. Li1,58, H. J. Li18, H. N. Li51,i, J. Q. Li4, J. S. Li54, J. W. Li45, Ke Li1, L. J Li1, L. K. Li1, Lei Li3, M. H. Li39, P. R. Li34,j,k, S. X. Li10, S. Y. Li56, T. Li45, W. D. Li1,58, W. G. Li1, X. H. Li66,53, X. L. Li45, Xiaoyu Li1,58, H. Liang66,53, H. Liang1,58, H. Liang30, Y. F. Liang49, Y. T. Liang28,58, G. R. Liao13, L. Z. Liao45, J. Libby24, A. Limphirat55, C. X. Lin54, D. X. Lin28,58, T. Lin1, B. J. Liu1, C. X. Liu1, D. Liu17,66, F. H. Liu48, Fang Liu1, Feng Liu6, G. M. Liu51,i, H. Liu34,j,k, H. B. Liu14, H. M. Liu1,58, Huanhuan Liu1, Huihui Liu19, J. B. Liu66,53, J. L. Liu67, J. Y. Liu1,58, K. Liu1, K. Y. Liu36, Ke Liu20, L. Liu66,53, Lu Liu39, M. H. Liu10,f, P. L. Liu1, Q. Liu58, S. B. Liu66,53, T. Liu10,f, W. K. Liu39, W. M. Liu66,53, X. Liu34,j,k, Y. Liu34,j,k, Y. B. Liu39, Z. A. Liu1,53,58, Z. Q. Liu45, X. C. Lou1,53,58, F. X. Lu54, H. J. Lu21, J. G. Lu1,53, X. L. Lu1, Y. Lu7, Y. P. Lu1,53, Z. H. Lu1, C. L. Luo37, M. X. Luo74, T. Luo10,f, X. L. Luo1,53, X. R. Lyu58, Y. F. Lyu39, F. C. Ma36, H. L. Ma1, L. L. Ma45, M. M. Ma1,58, Q. M. Ma1, R. Q. Ma1,58, R. T. Ma58, X. Y. Ma1,53, Y. Ma42,g, F. E. Maas17, M. Maggiora69A,69C, S. Maldaner4, S. Malde64, Q. A. Malik68, A. Mangoni26B, Y. J. Mao42,g,g, Z. P. Mao1, S. Marcello69A,69C, Z. X. Meng61, J. G. Messchendorp12,59, G. Mezzadri1,27A, H. Miao1, T. J. Min38, R. E. Mitchell25, X. H. Mo1,53,58, N. Yu. Muchnoi11,b, Y. Nefedov32, F. Nerling17,d, I. B. Nikolaev11, Z. Ning1,53, S. Nisar9,l, Y. Niu45, S. L. Olsen58, Q. Ouyang1,53,58, S. Pacetti26B,26C, X. Pan10,f, Y. Pan52, A. Pathak30, M. Pelizaeus4, H. P. Peng66,53, K. Peters12,d, J. Pettersson70, J. L. Ping37, R. G. Ping1,58, S. Plura31, S. Pogodin32, V. Prasad66,53, F. Z. Qi1, H. Qi66,53, H. R. Qi56, M. Qi38, T. Y. Qi10,f, S. Qian1,53, W. B. Qian58, Z. Qian54, C. F. Qiao58, J. J. Qin67, L. Q. Qin13, X. P. Qin10,f, X. S. Qin45, Z. H. Qin1,53, J. F. Qiu1, S. Q. Qu39, S. Q. Qu56, K. H. Rashid68, C. F. Redmer31, K. J. Ren35, A. Rivetti69C, V. Rodin59, M. Rolo69C, G. Rong1,58, Ch. Rosner17, S. N. Ruan39, H. S. Sang66, A. Sarantsev32,c, Y. Schelhaas31, C. Schnier4, K. Schönning70, M. Scodeggio27A,27B, K. Y. Shan10,f, W. Shan22, X. Y. Shan66,53, J. F. Shangguan50, L. G. Shao1,58, M. Shao66,53, C. P. Shen10,f, H. F. Shen1,58, X. Y. Shen1,58, B. A. Shi58, H. C. Shi66,53, J. Y. Shi1, q. q. Shi50, R. S. Shi1,58, X. Shi1,53, X. D Shi66,53, J. J. Song18, W. M. Song1,30, Y. X. Song42,g, S. Sosio69A,69C, S. Spataro69A,69C, F. Stieler31, K. X. Su71, P. P. Su50, Y. J. Su58, G. X. Sun1, H. Sun58, H. K. Sun1, J. F. Sun18, L. Sun71, S. S. Sun1,58, T. Sun1,58, W. Y. Sun30, X Sun23,h, Y. J. Sun66,53, Y. Z. Sun1, Z. T. Sun45, Y. H. Tan71, Y. X. Tan66,53, C. J. Tang49, G. Y. Tang1, J. Tang54, L. Y Tao67, Q. T. Tao23,h, M. Tat64, J. X. Teng66,53, V. Thoren70, W. H. Tian47, Y. Tian28,58, I. Uman57B, B. Wang1, B. L. Wang58, C. W. Wang38, D. Y. Wang42,g, F. Wang67, H. J. Wang34,j,k, H. P. Wang1,58, K. Wang1,53, L. L. Wang1, M. Wang45, M. Z. Wang42,g, Meng Wang1,58, S. Wang13, S. Wang10,f, T. Wang10,f, T. J. Wang39, W. Wang54, W. H. Wang71, W. P. Wang66,53, X. Wang42,g, X. F. Wang34,j,k, X. L. Wang10,f, Y. D. Wang41, Y. F. Wang1,53,58, Y. H. Wang43, Y. Q. Wang1, Yaqian Wang1,16, Yi Wang56, Z. Wang1,53, Z. Y. Wang1,58, Ziyi Wang58, D. H. Wei13, F. Weidner63, S. P. Wen1, D. J. White62, U. Wiedner4, G. Wilkinson64, M. Wolke70, L. Wollenberg4, J. F. Wu1,58, L. H. Wu1, L. J. Wu1,58, X. Wu10,f, X. H. Wu30, Y. Wu66, Z. Wu1,53, L. Xia66,53, T. Xiang42,g, D. Xiao34,j,k, G. Y. Xiao38, H. Xiao10,f, S. Y. Xiao1, Y. L. Xiao10,f, Z. J. Xiao37, C. Xie38, X. H. Xie42,g, Y. Xie45, Y. G. Xie1,53, Y. H. Xie6, Z. P. Xie66,53, T. Y. Xing1,58, C. F. Xu1, C. J. Xu54, G. F. Xu1, H. Y. Xu61, Q. J. Xu15, S. Y. Xu65, X. P. Xu50, Y. C. Xu58, Z. P. Xu38, F. Yan10,f, L. Yan10,f, W. B. Yan66,53, W. C. Yan75, H. J. Yang46,e, H. L. Yang30, H. X. Yang1, L. Yang47, S. L. Yang58, Tao Yang1, Y. F. Yang39, Y. X. Yang1,58, Yifan Yang1,58, M. Ye1,53, M. H. Ye8, J. H. Yin1, Z. Y. You54, B. X. Yu1,53,58, C. X. Yu39, G. Yu1,58, T. Yu67, X. D. Yu42,g, C. Z. Yuan1,58, L. Yuan2, S. C. Yuan1, X. Q. Yuan1, Y. Yuan1,58, Z. Y. Yuan54, C. X. Yue35, A. A. Zafar68, F. R. Zeng45, X. Zeng6, Y. Zeng23,h, Y. H. Zhan54, A. Q. Zhang1, B. L. Zhang1, B. X. Zhang1, D. H. Zhang39, G. Y. Zhang18, H. Zhang66, H. H. Zhang54, H. H. Zhang30, H. Y. Zhang1,53, J. L. Zhang72, J. Q. Zhang37, J. W. Zhang1,53,58, J. X. Zhang34,j,k, J. Y. Zhang1, J. Z. Zhang1,58, Jianyu Zhang1,58, Jiawei Zhang1,58, L. M. Zhang56, L. Q. Zhang54, Lei Zhang38, P. Zhang1, Q. Y. Zhang35,75, Shulei Zhang23,h, X. D. Zhang41, X. M. Zhang1, X. Y. Zhang45, X. Y. Zhang50, Y. Zhang64, Y. T. Zhang75, Y. H. Zhang1,53, Yan Zhang66,53, Yao Zhang1, Z. H. Zhang1, Z. Y. Zhang71, Z. Y. Zhang39, G. Zhao1, J. Zhao35, J. Y. Zhao1,58, J. Z. Zhao1,53, Lei Zhao66,53, Ling Zhao1, M. G. Zhao39, Q. Zhao1, S. J. Zhao75, Y. B. Zhao1,53, Y. X. Zhao28,58, Z. G. Zhao66,53, A. Zhemchugov32,a, B. Zheng67, J. P. Zheng1,53, Y. H. Zheng58, B. Zhong37, C. Zhong67, X. Zhong54, H. Zhou45, L. P. Zhou1,58, X. Zhou71, X. K. Zhou58, X. R. Zhou66,53, X. Y. Zhou35, Y. Z. Zhou10,f, J. Zhu39, K. Zhu1, K. J. Zhu1,53,58, L. X. Zhu58, S. H. Zhu65, S. Q. Zhu38, T. J. Zhu72, W. J. Zhu10,f, Y. C. Zhu66,53, Z. A. Zhu1,58, 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 Central South University, Changsha 410083, People’s Republic of China
8 China Center of Advanced Science and Technology, Beijing 100190, People’s Republic of China
9 COMSATS University Islamabad, Lahore Campus, Defence Road, Off Raiwind Road, 54000 Lahore, Pakistan
10 Fudan University, Shanghai 200433, People’s Republic of China
11 G.I. Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
12 GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
13 Guangxi Normal University, Guilin 541004, People’s Republic of China
14 Guangxi University, Nanning 530004, People’s Republic of China
15 Hangzhou Normal University, Hangzhou 310036, People’s Republic of China
16 Hebei University, Baoding 071002, People’s Republic of China
17 Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
18 Henan Normal University, Xinxiang 453007, People’s Republic of China
19 Henan University of Science and Technology, Luoyang 471003, People’s Republic of China
20 Henan University of Technology, Zhengzhou 450001, People’s Republic of China
21 Huangshan College, Huangshan 245000, People’s Republic of China
22 Hunan Normal University, Changsha 410081, People’s Republic of China
23 Hunan University, Changsha 410082, People’s Republic of China
24 Indian Institute of Technology Madras, Chennai 600036, India
25 Indiana University, Bloomington, Indiana 47405, USA
26 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
27 INFN Sezione di Ferrara, (A)INFN Sezione di Ferrara, I-44122, Ferrara, Italy; (B)University of Ferrara, I-44122, Ferrara, Italy
28 Institute of Modern Physics, Lanzhou 730000, People’s Republic of China
29 Institute of Physics and Technology, Peace Ave. 54B, Ulaanbaatar 13330, Mongolia
30 Jilin University, Changchun 130012, People’s Republic of China
31 Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
32 Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
33 Justus-Liebig-Universitaet Giessen, II. Physikalisches Institut, Heinrich-Buff-Ring 16, D-35392 Giessen, Germany
34 Lanzhou University, Lanzhou 730000, People’s Republic of China
35 Liaoning Normal University, Dalian 116029, People’s Republic of China
36 Liaoning University, Shenyang 110036, People’s Republic of China
37 Nanjing Normal University, Nanjing 210023, People’s Republic of China
38 Nanjing University, Nanjing 210093, People’s Republic of China
39 Nankai University, Tianjin 300071, People’s Republic of China
40 National Centre for Nuclear Research, Warsaw 02-093, Poland
41 North China Electric Power University, Beijing 102206, People’s Republic of China
42 Peking University, Beijing 100871, People’s Republic of China
43 Qufu Normal University, Qufu 273165, People’s Republic of China
44 Shandong Normal University, Jinan 250014, People’s Republic of China
45 Shandong University, Jinan 250100, People’s Republic of China
46 Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
47 Shanxi Normal University, Linfen 041004, People’s Republic of China
48 Shanxi University, Taiyuan 030006, People’s Republic of China
49 Sichuan University, Chengdu 610064, People’s Republic of China
50 Soochow University, Suzhou 215006, People’s Republic of China
51 South China Normal University, Guangzhou 510006, People’s Republic of China
52 Southeast University, Nanjing 211100, People’s Republic of China
53 State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People’s Republic of China
54 Sun Yat-Sen University, Guangzhou 510275, People’s Republic of China
55 Suranaree University of Technology, University Avenue 111, Nakhon Ratchasima 30000, Thailand
56 Tsinghua University, Beijing 100084, People’s Republic of China
57 Turkish Accelerator Center Particle Factory Group, (A)Istinye University, 34010, Istanbul, Turkey; (B)Near East University, Nicosia, North Cyprus, Mersin 10, Turkey
58 University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
59 University of Groningen, NL-9747 AA Groningen, The Netherlands
60 University of Hawaii, Honolulu, Hawaii 96822, USA
61 University of Jinan, Jinan 250022, People’s Republic of China
62 University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
63 University of Muenster, Wilhelm-Klemm-Str. 9, 48149 Muenster, Germany
64 University of Oxford, Keble Rd, Oxford, UK OX13RH
65 University of Science and Technology Liaoning, Anshan 114051, People’s Republic of China
66 University of Science and Technology of China, Hefei 230026, People’s Republic of China
67 University of South China, Hengyang 421001, People’s Republic of China
68 University of the Punjab, Lahore-54590, Pakistan
69 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
70 Uppsala University, Box 516, SE-75120 Uppsala, Sweden
71 Wuhan University, Wuhan 430072, People’s Republic of China
72 Xinyang Normal University, Xinyang 464000, People’s Republic of China
73 Yunnan University, Kunming 650500, People’s Republic of China
74 Zhejiang University, Hangzhou 310027, People’s Republic of China
75 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
Based on a sample of 4.4 of annihilation data collected in the energy region between 4.6 GeV and 4.7 GeV with the BESIII detector at BEPCII, two singly Cabibbo-suppressed decays and are studied.
The ratio of the branching fraction relative to is measured to be , and the ratio of relative to is measured to be . After taking the world-average branching fractions of the reference decay channels, the branching fractions
and
are determined to be
and
, respectively.
The branching fraction of the decay is measured for the first time.
pacs:
14.20.Lq, 13.30.Eg, 13.66.Bc
I Introduction
The study of charmed baryon decays is valuable for both understanding charmed-baryon dynamics and probing the effects of the weak and strong interactions.
Since the ground state of the charmed baryons was discovered [1], many efforts have been made to predict its branching fractions (BFs) into two-body hadronic final states, [2, 3, 4, 5, 6, 7], where and denote the octet baryon and nonet meson states, respectively. However, progress has been hindered, due to the limited precision of experimental measurements [8] and difficulties in the theoretical treatment of non-perturbative strong interaction effects. For example, before studies of Singly Cabibbo-Suppressed (SCS) decays were performed by the Belle [9] and BaBar [10] collaborations, only one theoretical calculation existed for the BFs of these SCS processes [2].
Throughout this paper, the charge-conjugate modes are implied, unless otherwise stated.
The challenge for theoretical predictions is that the well-known factorization approach, which has been applied successfully to heavy-meson decays, has difficulties in describing charmed-baryon decays [11]. This is because the nonfactorizable terms are sizable or even dominant contributions in hadronic decays [2], compared with the charmed-meson case [12, 13], e.g., the SCS and decays only receive nonfactorizable contribution. These nonfactorizable terms, arising from -exchange or internal -emission [3], can be constrained by precise experimental inputs in charmed-meson decays [14].
The BESIII collaboration [15] has reported significant improvements in the precision of absolute hadronic BFs of the baryon, and the first model-independent measurements near the threshold of production [16]. In addition, the BESIII, LHCb and Belle collaborations have carried out complementary analyses of charmed baryons, such as lifetime measurements [17, 18, 19, 20, 21] and studies of semi-leptonic decays [22, 23, 24, 25, 26].
Improved measurements of the BFs of the decays can provide crucial inputs for the theoretical models [3, 4, 5, 6], in particular those of the SCS decays for which there exists very limited experimental information.
Theoretical predictions for SCS decays are listed in Table 1.
In Refs. [2, 3], factorizable terms are made accessible by inserting vacuum intermediates states [2], which are then reduced to the products of current matrix elements defined with decay constants of the emitted meson and form factors of the transition. Nonfactorizable terms are tackled in the current algebra framework with the pole approximation in Refs. [2, 3]. Ref. [3] uses the MIT bag model [27] to account for baryon pole transition matrix elements, while Ref. [2] makes short-distance QCD corrections to the weak Hamiltonian.
In Ref. [4], a diagrammatic analysis is performed and is predicted to be . Ref. [5] expects that under flavor symmetry. In Ref. [6], the irreducible representation amplitude (IRA) approach is used to extract amplitudes from experimental data inputs, which gives quite different predictions for and . In particular, Ref. [6]
predicts that is about one fifth of , which is far smaller than other predictions.
Table 1 shows the current Particle Data Group (PDG) [28] world average value of based on measurements from the Belle [9] and BaBar [10] collaborations, performed more than a decade ago, while no measurement exits for the decay. All theoretical predictions for are consistent with the experimental value. Those predictions in Refs. [4, 5, 6] are from fits that take an ensemble of measured BFs as inputs, and are limited by the precision of these measurements. Thus, new determinations of the BFs of decays, in particular the mode , are important for validating and improving these theoretical-model calculations. Furthermore, improved measurements may clarify the tension between the predictions in Ref. [6] and Refs. [2, 3, 4, 5].
Table 1: Comparison of various theoretical predictions and the experimental values for (in unit of ). In Ref. [2], alternative assignments to QCD corrections give different predictions as shown in the parentheses. The theoretical uncertainties in Ref. [3] are estimated to be 25%, arising from a slight change of the MIT bag radius.
In this paper, we present a study of the SCS decays and based on 4.4 fb-1 of annihilation data collected at the center-of-mass energies , 4.612, 4.628, 4.641, 4.661, 4.682, 4.699 GeV [29, 30] with the BESIII detector at BEPCII. We report the first study of the channel and provide the BF ratio, , together with an improved measurement of the BF ratio, .
II BESIII Experiment and Monte Carlo Simulation
The BESIII detector [31] records symmetric collisions provided by the BEPCII storage ring [32] in the center-of-mass energy range from 2.0 to 4.95 GeV, with a peak luminosity of achieved at . The cylindrical core of the BESIII detector covers 93% of the full solid angle and 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 identification modules interleaved with steel. The charged-particle momentum resolution at is , and the ionization energy loss resolution is for electrons from Bhabha scattering. The EMC measures photon energies with a resolution of () at in the barrel (end-cap) region. The time resolution in the TOF barrel region is 68 ps, while that in the end-cap region is 110 ps. The end-cap TOF system was upgraded in 2015 using multi-gap resistive plate chamber technology, providing a time resolution of 60 ps [33, *guo:TOF2, *Cao:2020ibk]. More detailed descriptions can be found in Refs. [36, 37].
Simulated data samples are produced with a Geant4-based [38] Monte Carlo (MC) package, which includes the geometric description of the BESIII detector [39, 40] and the detector response. The simulation models the beam-energy spread and initial-state radiation (ISR) in the annihilations with the generator kkmc [41]. The final-state radiation from charged final-state particles is incorporated using photos [42].
The “inclusive MC sample” includes the production of pairs and open-charmed mesons, ISR production of vector charmonium(-like) states, and continuum processes which are incorporated in kkmc [41, 43]. Known decay modes are modeled with evtgen [44, 45] using the BFs taken from the PDG [28]. The BF is assumed to be the same as . The remaining unknown charmonium decays are modeled with lundcharm [46, 47]. The inclusive MC sample is used to study background contributions and to optimize event selections. We denote “Hadron MC” as the inclusive MC sample with pairs removed, which therefore only includes backgrounds for this study.
For the reference mode , the intermediate states are modeled according to an internal partial-wave analysis of this channel. For the reference mode , the angular distributions are described with consideration of the transverse polarization and decay asymmetry parameters of the and its daughter baryons [48]. We use a uniformly distributed phase-space model for the simulation of the signal SCS decays and . The “signal MC” samples, in which the decays exclusively into signal (reference) modes while the decays inclusively, are used to determine the detection efficiencies.
III Event Selection
In this analysis, we reconstruct the two signal modes through the cascade decays
and . The reference modes and are reconstructed through the same decay chains of the and baryons.
As the pair is produced without any accompanying hadrons, it is possible to reconstruct the and infer the presence of the through its recoil mass.
Charged tracks are reconstructed in the MDC, and are required to have a polar angle with respect to the -axis, defined as the symmetry axis of the MDC, satisfying .
Those tracks that are not used in the reconstruction of and candidates must
have a distance of closest approach to the interaction point (IP) smaller than 10 cm along the -axis () and smaller than 1 cm in the perpendicular plane (). Particle identification (PID) for charged tracks is implemented [49] using combined information from the flight time measured in the TOF and the measured in the MDC. Charged tracks are identified as protons when they satisfy , and , where is the PID probability for each hadron hypothesis with . Charged tracks are identified as pions when they satisfy . In the selection of and decays, no PID requirement is imposed for the bachelor kaons and pions, but the for kaon (pion) hypothesis is retained for further analysis in the signal-candidate selection.
Photon candidates from and decays are reconstructed from the electromagnetic showers detected in the EMC crystals. The deposited energy is required to be larger than in the barrel region with and larger than in the end-cap region with . To further suppress fake photon candidates due to electronic noise or beam-related background, the measured EMC time is required to be within 700 ns from the event start time. To reconstruct candidates, the invariant mass of a photon pair is required to satisfy . To further improve the momentum resolution, the invariant mass of the photon pair is constrained to the
known mass [28] by applying a one-constraint kinematic fit, the of which is required to be less than 200. The momentum of the after the fit is used in the subsequent analysis.
Neutral kaon candidates are reconstructed through the decay by combining all pairs of oppositely charged tracks with both pions passing the PID requirement. These tracks, and also those used to build candidates, must satisfy and , while no requirement is applied. A vertex fit is applied to pairs of charged tracks, constraining them to originate from a common decay vertex, and the of this vertex fit is required to be less than 100. The invariant mass of the pair needs to satisfy . Here, is calculated with the pions constrained to originate at the decay vertex. To further suppress background, we require the ratio of the decay length to the resolution of decay length to be greater than 2. An analogous normalised decay-length requirement is imposed on candidates reconstructed in the final state . Here a PID requirement is imposed on the proton candidate, but not on the candidate. The invariant mass of the combination must satisfy .
Protons and reconstructed mesons are used to form candidates. The invariant mass of the pair is required to be , which is a loose requirement since this kinematic variable is fitted to determine the signal.
When selecting decays we veto events where the invariant mass of the pair satisfies , in order to avoid cross-contamination between and .
Possible backgrounds from in the final states are also rejected by requiring lies outside the range . Since the background is not significant in decays, a veto is not imposed for the mode. If there are multiple combinations in a single event, we choose the candidate with the minimum magnitude of the energy difference, defined as
,
where is the energy of the detected candidate in the rest frame of the initial collision system, and is the beam energy. Furthermore, the requirement is imposed as illustrated in Fig. 1, where the cut values are decided from inspection of the the figure-of-merit , where is the number of signal (background) events in the inclusive MC samples.
The candidates are reconstructed from photon candidates and candidates. To suppress backgrounds from energetic showers, we further require the deposited energies of the photon candidates to be less than in reconstruction. The invariant mass of the pair is required to lie within .
When reconstructing the and signal decays, we perform a kinematic fit that constrains the invariant mass of the recoil side of the candidate to the known mass of the [28]. To improve the resolution of photon momenta originating from , we also constrain the invariant masses of the and to the known masses of and , respectively. The fitted momenta from this three-constraint (3C) kinematic fit are used in the subsequent analysis.
The aforementioned metric can be used for selection the only candidate when there are multiple candidates in the event. But the metric gives better signal significance, where is the 3C fit quality and, is the quality of the vertex fit of the reconstruction. The is defined as [49]
(1)
where and are the measured TOF and ionization
energy loss, the index “” denotes the values
expected for the hypotheses and and
are the resolutions of TOF and measurements, respectively. No explicit PID requirement is imposed on the particle that
accompanies the , and the assignment of and
modes is only based on with the and hypotheses, respectively.
To suppress backgrounds, a requirement of is imposed for both modes, which gives best FOM values.
Figure 1: distributions for the data samples passing the event selections. The arrows indicate the requirements for . and for and modes are required to be within resolutions around individual peaks. The momenta before the kinematic fit are used for calculating of and modes.
IV Relative branching fraction measurements
To determine the yields for the four signal decay modes, we use the the beam-constrained mass , which is defined as
(2)
where is the three-momentum of the tagged candidate in the rest frame of the initial collision system.
To mitigate systematic uncertainties associate with the detection, we measure the
relative BFs of and .
To determine , the distributions of the and decays, as illustrated in Fig. 2, are fitted simultaneously for the seven energy points. In the unbinned extended maximum-likelihood fit, the signal shapes are derived from MC simulations convolved with Gaussian functions to account for the potential difference between data the MC simulations, due to imperfect modeling in MC simulation and the beam-energy spread. The parameters of the Gaussian functions in the signal mode are the same as the reference mode, which are floating in the fit. The combinatorial background is described by an ARGUS function [50]
(3)
where the parameter is different in the signal and reference modes and the cut-off parameter is fixed to the beam energy at each energy point. The observed yield of decays is related to , the yield of decays, by
(4)
where () is the detection efficiency of () estimated with the signal MC samples. The relative BF is obtained directly in the simultaneous fit as summarized in Table 2. The fit results are shown in Fig. 2.
Figure 2: The simultaneous fit results of the distributions for the and candidates at different energy points, where the black points with error bars denote data, the red solid lines denote the fit results, the green dotted lines denote the signal components and the orange dashed lines denote combinatorial backgrounds.
The decay includes many resonant enhancements which may peak in the spectrum and potentially bias the measurement of . A two-dimensional, i.e., and , unbinned fit is performed to distinguish the signal decay from other contributions.
In the simultaneous fit to the signal and the reference modes, we adopt two-dimensional signal shapes from MC simulations convolved with two uncorrelated Gaussian functions to determine the yield for decays, which is related to by
(5)
where () is the detection efficiency of () estimated with signal MC samples, and is taken from the PDG [28].
Three types of background components are included: (a) combinatorial backgrounds with no peaking structures in both dimensions, (b) non- background (mainly coming from non-signal decays), and (c) non- background from inclusive production. The combinatorial background component is described by a product of an ARGUS function in the dimension and a linear function in the dimension
(6)
where the parameter of the function is different from that in the fit of and decays, and , are coefficients of the linear function.
The approach for modeling the non- background is different between the signal and reference modes. For the reference mode, the shape of non- background is modeled with a product of the signal shape in and a linear function in . For the signal mode, the shape of the non- background coming from decays is described by a two-dimensional shape derived from MC simulation in which intermediate states of the process are considered. Other non- backgrounds from , etc., are found to be negligible. The shape of non- backgrounds is described by a product of an ARGUS function in and the signal shape derived from MC simulations in . The yields of these background components are free parameters in the fit. When fitting , an extra background component is included that accounts for non- contamination from the decays, whose
shape is fixed according to MC simulations. The yield of this background is related to the fitted yield in the decay as follows:
(7)
Here is the contamination rate of decays into the sample. The relative BF is obtained from the simultaneous fit of and decays, as summarized in Table 2. The and projections of the simultaneous two-dimensional fit of the signal and reference modes are shown in Fig. 3 and Fig. 4.
Table 2: Integrated luminosities taken from Refs. [29, 30], yields and detection efficiencies for the signal modes and , as well as those for the reference modes and modes at the seven energy points. The uncertainties are statistical. Also listed are the results for the relative BFs, where the first uncertainties are statistical, and the second are systematic.
(GeV)
(pb-1)
4.600
566.9
4.612
103.8
4.628
521.5
4.641
552.4
4.661
529.6
4.682
1669.3
4.699
536.5
Figure 3: The projections of the simultaneous two-dimensional fit of the and candidates at different energy points, where the black points with error bars denote data, the red solid lines denote the fit results, and the other colored curves denote the different components. In the left panel, the green dotted lines denote signal. In the right side, the blue dashed-dotted lines denote signal.
Figure 4: The projections of the simultaneous two-dimensional fit of the and candidates at different energy points, where the black points with error bars denote data, the red solid lines denote the fit results, and the other colored curves denote the different components. In the left panel, the green dotted lines denote signal. In the right side, the blue dashed-dotted lines denote signal.
V Systematic uncertainties
In the measurements of and , the uncertainties associated with the and reconstruction cancel in the ratio. In the measurement, the equal number of charged tracks in the signal and reference modes means that any uncertainty in the tracking efficiency also cancels. Similarly, in the measurement, there in no uncertainty from PID as the same PID requirements are imposed in the reconstruction and for the reference mode . Those uncertainties that do not cancel are summarized in Table 3 and discussed below.
Table 3: Summary of systematic uncertainties for relative BF measurements of the and decays. The total systematic uncertainty is the sum in quadrature of the individual components. “—–” indicates cases where there is no uncertainty.
Source
(%)
(%)
PID
0.9
—–
Tracking
—–
1.0
reconstruction
—–
1.0
veto region
—–
0.4
veto region
—–
0.4
requirement
0.7
—–
requirement
—–
0.5
Signal model
0.5
2.5
Fitting model
0.6
1.6
—–
0.1
Total
1.4
3.4
(I) PID. To account for the difference between data and MC simulation, we study a series of control samples of
, events [51] to determine the and PID efficiencies. The detection efficiencies for the four decay modes are recalculated after reweighting the corresponding MC samples on an event-by-event basis according to the momentum-dependent efficiency differences between data and MC simulations. The corrected efficiencies are then input to the simultaneous fits and the resultant changes are taken as the systematic uncertainty. The statistical fluctuations in the control samples also induce uncertainties in the reweighting procedure, which are evaluated in the different momentum regions and propagated to the final measurement. The average uncertainty is calculated as
(8)
where is the size of the MC samples and is the statistical uncertainty of the control samples in the momentum region where the -th event in the MC sample is found. We add these two contributions in quadrature to arrive at the total uncertainty from PID, which is 0.9% for .
(II) Tracking. Using the same control samples to determine the tracking efficiencies, we reweight the detection efficiency for and evaluate the systematic uncertainty in the same way as for the PID, which results in a contribution of 1.0% for .
(III) reconstruction. The reconstruction efficiencies are studied by using control samples of , and decays. The systematic uncertainty is again evaluated in the same way as for the PID, which gives a contribution of 1.0% for .
(IV) veto region. For the decay mode, the veto may have a different efficiency in data and MC simulation. To bound any such bias, we remove the veto and reevaluate the relative BFs. The relative difference between the reevaluated result and the nominal result is taken as the systematic uncertainty, which is 0.4%(0.4%) for .
(V) Joint fit-quality requirement. Joint fit-quality requirements are imposed on the and decay modes. To evaluate the effect of any difference between data and MC simulation, we fit to the distribution in data with the shapes derived from MC simulations convolved with a floating Gaussian function.
The detection efficiency is remeasured after resampling the corresponding variable in MC samples according to the fitted Gaussian function, and is updated accordingly.
The Gaussian parameters are randomly varied within the fitted uncertainties, and the corresponding value of is recorded. The mean and the standard deviation of the distribution is measured and is assigned as the the corresponding systematic uncertainty, which is 0.7%.
(VI) Energy difference requirement. The energy difference requirement is used in the measurement of . Following a similar procedure as for the uncertainty, we first obtain the difference of distributions in data and MC simulations, and then study the distribution of the corresponding values with updated efficiencies, which leads to the assignment of a systematic uncertainty of 0.5% for .
(VII) Signal model. We use the phase-space model for simulating the and decay modes to obtain our central values, since the decay-asymmetry parameters are not known. As a systematic check, we test several extreme cases of the decay-asymmetry parameters and recalculate their detection efficiencies based on the corresponding signal MC samples. The resultant changes on the relative BFs are taken as the systematic uncertainties, which are 0.5% and 2.5% for and , respectively.
(VIII) Fitting model. The fitting procedure has uncertainties arising from both the signal and background shapes. For the signal shapes, the convolved Gaussian functions are varied by around their baseline values. For the mode, we also consider the effect of the correlation between the convolved Gaussian functions in and by including a correlation coefficient determined from MC and verify that this leads to a negligible bias.
For the background shapes, the parameter in the ARGUS function of Eq. (3) is randomly varied by MeV and the shape of the background component is replaced by a shape from an alternative partial wave analysis, in which more intermediate states are considered than those in the nominal signal MC simulation.
To take into account these effects, 5000 pseudo data sets are sampled according to the bootstrap method [52].
For each pseudo data set,
the fitting models are varied randomly.
Pull distributions are inspected from these pseudo data sets, and the small biases of 0.6% and 1.6% are assigned as the systematic uncertainties for and , respectively.
(IX) . The uncertainty on the BF [28] is propagated to give a systematic uncertainty of 0.1% in .
VI Summary
In summary,
based on 4.4 fb-1 of annihilation data collected in the energy region between 4.6 GeV and 4.7 GeV with the BESIII detector at
BEPCII, we report the measurements of BFs of the two singly Cabibbo-suppressed decay modes and relative to the reference modes and , respectively. The BF of relative to is measured to be , while the BF of relative to is measured to be .
Taking the world-average BFs and from the PDG [28],
yields the the absolute BF ,
and .
This is the first measurement of the branching fraction. The BF is measured with a comparable precision to the combined result from the Belle [9] and BaBar [10] collaborations.
The ratio is determined to be , which is consistent with the predictions in Refs. [3, 5] under flavor symmetry and disfavors the prediction in Ref. [6]. The prediction for in Ref. [6] differs from our result by 2.5, indicating a reassessment of the IRA method may be needed. Though our work is generally consistent with the predictions in Refs. [2, 3, 4, 5] within , these theoretical predictions generally overestimate the BFs.
The systematic uncertainties of our results are smaller than those of from the Belle and BaBar collaborations, but our precision is limited by the relatively low sample size. With the additional data which are foreseen to be collected near the threshold in the coming years [53], we expect our measurements to improve in precision, and shed more light on the topic of charmed baryon decays.
Note: After the publication of our measurement, we were contacted by the authors of Ref. [6] that there was a numerical mistake in their calculation of . The erratum can be found in Ref. [54]. The result has been changed to , which is consistent with our measurement within .
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 R&D Program of China under Contracts Nos. 2020YFA0406400, 2020YFA0406300; National Natural Science Foundation of China (NSFC) under Contracts Nos. 11635010, 11675275, 11735014, 11822506, 11835012, 11935015, 11935016, 11935018, 11961141012, 12022510, 12025502, 12035009, 12035013, 12175321, 12192260, 12192261, 12192262, 12192263, 12192264, 12192265; the Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program; Joint Large-Scale Scientific Facility Funds of the NSFC and CAS under Contract No. U1832207, U1932101; State Key Laboratory of Nuclear Physics and Technology, PKU under Grant No. NPT2020KFY04; CAS Key Research Program of Frontier Sciences under Contract No. QYZDJ-SSW-SLH040; 100 Talents Program of CAS; the Fundamental Research Funds for the Central Universities; 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, GRK 2149; Istituto Nazionale di Fisica Nucleare, Italy; Ministry of Development of Turkey under Contract No. DPT2006K-120470; National Science and Technology fund; National Science Research and Innovation Fund (NSRF) via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation under Contract No. B16F640076; STFC (United Kingdom); Suranaree University of Technology (SUT), Thailand Science Research and Innovation (TSRI), and National Science Research and Innovation Fund (NSRF) under Contract No. 160355; The Royal Society, UK under Contracts Nos. DH140054, DH160214; The Swedish Research Council; U. S. Department of Energy under Contract No. DE-FG02-05ER41374.