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Record W4387075916 · doi:10.48550/arxiv.2309.13883

Measurement of the $e^{+}e^{-} \to K_{S}^{0} K_{L}^{0} π^{0}$ cross sections from $\sqrt{s}=$ 2.000 to 3.080 GeV

2023· preprint· en· W4387075916 on OpenAlexaff
BESIII Collaboration, M. N. Achasov, P. Adlarson, X. Ai, R. Aliberti, A. Amoroso, M. R. An, Q. An, Yu Bai, O. Bakina, I. Balossino, Y. Ban, V. Batozskaya, K. Begzsuren, N. Berger, M. Berłowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, Hao Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, T. T. Chang, W. L. Chang, G. R., G. Chelkov, Chao Chen, G. Chen, H. S. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, W. S. Cheng, S. K. Choi, X. Chu, G. Cibinetto, S. C. Coen, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, K Fischer, M. Fritsch, C. D. Fu, J. Fu, Y. Fu, H. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. Gersabeck, A Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y Guan, Z. L. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, T. T. Han, W. Y. Han, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, J. F. Hu, Yuanyuan Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, T. Hussain, N Hüsken, N. in der Wiesche, M. Irshad, J. Jackson, S. Jaeger, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, X. B. Ji, Xiangdong Ji, Y. Y. Ji, X. Q. Jia, Z. Jiao, H. J. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, Xinhao Jiang, Y. Jiang, J. B. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, T. Johansson, X. K., S. Kabana, N. Kalantar‐Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, A. Khoukaz, R. Kiuchi, R. Kliemt, O. B. Kolcu, B. Kopf, M. Kuessner, A. Kupść, W. Kühn, J. J. Lane, P. Larin, A. Lavania, L. Lavezzi, T. T. Lei, Z. H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. Li, H. B. Li, H. J. Li, H. N. Li, J. R. Li, J. S. Li, J. W. Li, K. L. Li, Ke Li, L. J Li, L. K. Li, Lei Li, Min Li, P. R. Li, Q. X. Li, S. X. Li, T. Li, W. D. Li, W. G. Li, Xinzhu Li, X. L. Li, Xiaoyu Li, Y. G. Li, Z. J. Li, Z. X. Li, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, Y. P. Liao, J. Libby, A. Limphirat, D. X. Lin, Tongyan Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. H. Liu, Fang Liu, Feng Liu, G. M. Liu, M. X. Liu, H. B. Liu, H. M. Liu, Huan‐Huan Liu, Huihui Liu, J. B. Liu, J. L. Liu, Jiayi Liu, K. Y. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, Tianbo Liu, W. K. Liu, W. M. Liu, X. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. Lu, J. G. Lü, X. L. Lu, Y. P. Lu, Zhilan Lü, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C., H. L., Juana Acosta, L. L., M. Maggiora, Q. M., R. Q., R. T., X. Y., Y. Ma, Y. M., F. E. Maas, M. Maggiora, S. Malde, Q. A. Malik, A. Mangoni, Y. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, Κ. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. J. Qin, L. Q. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, Shuheng Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, J. F. Shangguan, L. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, R. S. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, Y. J. Su, G. B. Sun, G. X. Sun, Huawei Sun, H. K. Sun, J. F. Sun, Kuan Sun, L. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. Sun, Y. J. Sun, Y. Z. Sun, Z. T. Sun, Y. X. Tan, C. J. Tang, G. Y. Tang, J. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Yuan Tian, Z. F. Tian, I. Uman, S. J. Wang, B. Wang, B. L. Wang, Bo Wang, C. W. Wang, D. Y. Wang, F. Wang, H. Wang, H. P. Wang, J. P. Wang, K. Wang, Linglu Wang, M. Wang, Meng Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, Yi Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Z. Wang, Z. L. Wang, Z. Y. Wang, Ziyi Wang, D. H. Wei, F. Weidner, S. P. Wen, C. W. Wenzel, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, C. Y. Wu, J. F. Wu, Lihua Wu, L. J. Wu, X. Wu, X. H. Wu, Y. H. Wu, Yue Wu, Zhishen Wu, L. Xia, X. M. Xian, B. H. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. H. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. C. Xu, Z. P. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, Tao Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Yifan Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, C. X. Yu, Guohui Yu, J. S. Yu, T. Yu, X. D. Yu, C. Z. Yuan, L. Yuan, S. C. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, Chuan Yue, A. A. Zafar, F. R. Zeng, X. Zeng, Yunchuan Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, Hongliang Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. Y. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. M. Zhang, L. Q. Zhang, Lei Zhang, P. Zhang, Q. Y. Zhang, Shuihan Zhang, Shulei Zhang, X. D. Zhang, X. M. Zhang, X. Y. Zhang, Xuyan Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Yan Zhang, Yao Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, G. Zhao, J. W. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, Shan Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, Haifeng Zhou, L. P. Zhou, X. K. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. J. Zhu, L. Zhu, Lixing Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. J. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, J. Zu

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsInstitute of Particle Physics
FundersShanghai Key Laboratory for Particle Physics and CosmologyCAS Center for Excellence in Particle PhysicsUniversity of Science and Technology of ChinaVetenskapsrådetNational Natural Science Foundation of ChinaIstituto Nazionale di Fisica NucleareDeutsche ForschungsgemeinschaftStrongNational Research FoundationInstitute of High Energy PhysicsU.S. Department of EnergyEuropean CommissionChinese Academy of SciencesRoyal SocietyNational Research Foundation of Korea
KeywordsPhysicsBar (unit)ColliderElectron–positron annihilationNuclear physicsMesonParticle physicsSigmaAtomic physicsHadron

Abstract

fetched live from OpenAlex

Based on $e^{+}e^{-}$ collision data collected at center-of-mass energies from 2.000 to 3.080 GeV by the BESIII detector at the BEPCII collider, a partial wave analysis is performed for the process $e^{+}e^{-}\to K_{S}^{0} K_{L}^{0} π^{0}$. The results allow the Born cross sections of the process $e^{+}e^{-}\to K_{S}^{0} K_{L}^{0} π^{0}$, as well as its subprocesses $e^{+}e^{-}\to K^{*}(892)^{0}\bar{K}^{0}$ and $K^{*}_{2}(1430)^{0}\bar{K}^{0}$ to be measured. The Born cross sections for $e^{+}e^{-}\to K_{S}^{0}K_{L}^{0}π^{0}$ are consistent with previous measurements by BaBar, but with substantially improved precision. The Born cross section lineshape of the process $e^{+}e^{-}\to K^{*}(892)^{0}\bar{K}^{0}$ is consistent with a vector meson state around 2.2 GeV with a significance of 3.2$σ$. A Breit-Wigner fit determines its mass as $M_Y=(2164.7\pm9.1\pm3.1)~{\rm{MeV}}/c^{2}$ and its width as $Γ_{Y}=(32.4\pm21.0\pm1.8)~\rm{MeV}$.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.135
GPT teacher head0.247
Teacher spread0.112 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
Has abstractyes

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