← Back to cohort
Study of BESIII trigger efficiencies with the 2018 J/ψ data
F. De Mori, М. Н. Ачасов, Y. Bai, X. Q. Hao, F. A. Harris, K. L. He, F. H. Heinsius, C. H. Heinz, T. Held, Y. K. Heng, C. Herold, M. Himmelreich, O. Bakina, T. Holtmann, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, Y. Hu, G. S. Huang, L. Q. Huang, X. T. Huang, R. Baldini Ferroli, Y. Huang, Z. Huang, T. Hussain, W. Imoehl, M. Irshad, S. Jaeger, S. Janchiv, Q. P. Ji, I. Balossino, X. B. Ji, H. B. Jiang, X. S. Jiang, J. B. Jiao, Z. Jiao, S. Jin, Y. Jin, T. Johansson, N. Kalantar-Nayestanaki, Y. Ban, X. S. Kang, R. Kappert, M. Kavatsyuk, B. C. Ke, I. K. Keshk, A. Khoukaz, P. Kiese, R. Kiuchi, R. Kliemt, L. Koch, K. Begzsuren, O. B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupść, M. G. Kurth, W. Kühn, J. J. Lane, J. S. Lange, P. Larin, N. Berger, A. Lavania, L. Lavezzi, Z. H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. Li, Li, D. M., M. Bertani, F. Li, G. Li, H. Li, J. L. Li, Ke Li, D. Bettoni, L. K. Li, Lei Li, P. R. Li, Sen Li, W. D. Li, Xin Li, Li, Z. Y., H. Liang, F. Bianchi, Y. F. Liang, L. Z. Liao, J. Libby, C. X. Lin, B. J. Liu, C. X. Liu, D. Liu, P. Adlarson, J. Bloms, F. H. Liu, Fang Liu, Feng Liu, H. B. Liu, Huanhuan Liu, Huihui Liu, Jian-Shu Liu, A. Bortone, Ke Liu, Lixue Liu, M. H. Liu, P. L. Liu, Liu, Q., Shuhan Liu, Shuai Liu, I. Boyko, T. Liu, W. M. Liu, X. Liu, Liu, Y. B., Z. A. Liu, X. Lou, F. X. Lu, R. A. Briere, H. J. Lü, J. D. Lu, X. L. Lu, Y. Lu, C. L. Luo, M. X. Luo, P. W. Luo, T. Luo, H. Cai, X. L. Luo, S. Lusso, X. R. Lyu, F. C., H. L., L. L., M. M., Q. M., R. Q., X. Z. Cai, X. X., F. E. Maas, M. Maggiora, S. Maldaner, S. Malde, Q. A. Malik, A. Mangoni, Y. Mao, Z. P. Mao, A. Calcaterra, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, T. J. Min, R. E. Mitchell, X. H. Mo, Y. J. Mo, N. Yu. Muchnoi, H. Muramatsu, G. F. Cao, S. Nakhoul, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, Olsen, S. L., Q. Ouyang, S. Pacetti, X. Pan, N. Cao, Y. Pan, A. Pathak, P. Patteri, M. Pelizaeus, H. P. Peng, Κ. Peters, J. Pettersson, Ping, J. L., R. G. Ping, R. Poling, S. A. Çetin, V. Prasad, H. R. Qi, K. H. Qi, M. Qi, T. Y. Qi, Si-Jin Qian, Z. Qian, S. Ahmed, J. F. Chang, C. F. Qiao, L. Q. Qin, X. S. Qin, J. F. Qiu, S. Q. Qu, K. H. Rashid, K. Ravindran, C. F. Redmer, A. Rivetti, W. L. Chang, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, M. Rump, H. S. Sang, A. Sarantsev, Y. Schelhaas, C. Schnier, K. Schoenning, G. Chelkov, M. Scodeggio, D. C. Shan, W. Shan, X. Y. Shan, J. F. Shangguan, M. Shao, C. P. Shen, P. X. Shen, X. Y. Shen, H. C. Shi, D. Y. Chen, R. S. Shi, X. Shi, W. M. Song, Y. X. Song, S. Sosio, S. Spataro, K. X. Su, P. P. Su, F. F. Sui, Chen, G., G. X. Sun, H. K. Sun, J. F. Sun, L. Sun, S. Sun, T. Sun, W. Y. Sun, Y. J. Sun, Chen, H. S., Z. T. Sun, Y. H. Tan, C. J. Tang, G. Y. Tang, J. Tang, J. X. Teng, V. Thoren, Chen, M. L., I. Uman, B. Wang, C. W. Wang, D. Y. Wang, H. J. Wang, K. Wang, L. L. Wang, Meng Wang, Chen, S. J., Wang, W. H., X. Wang, Y. Wang, Chen, X. R., Ziyi Wang, Ziyi Wang, Zongyuan Wang, D. H. Wei, P. Weidenkaff, F. Weidner, Chen, Y. B., S. P. Wen, D. J. White, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, J. F. Wu, L. H. Wu, X. Wu, M. Albrecht, Z. J Chen, Z. Wu, L. Xia, H. Xiao, S. Y. Xiao, Z. J. Xiao, X. H. Xie, Y. Xie, T. Y. Xing, G. F. Xu, W. S. Cheng, Q. Xu, Wenfang Xu, X. P. Xu, F. Yan, Li Yan, W. B. Yan, Yan Xu, H. J. Yang, G. Cibinetto, Lei Yang, S. L. Yang, Y. Yang, Yifan Yang, Zhi Yang, M. Ye, J. H. Yin, Z. Y. You, Yu Bai, F. Cossio, C. X. Yu, Guowei Yu, J. S. Yu, T. Yu, C. Z. Yuan, L. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, X. F. Cui, A. Yuncu, A. A. Zafar, Y. Zeng, B. X. Zhang, Guangyi Zhang, H. Zhang, Junwei Zhang, H. L. Dai, Jianyu Zhang, Jiawei Zhang, L. Q. Zhang, Lei Zhang, S. Zhang, X. Dai, Shulei Zhang, X. D. Zhang, Y. Zhang, Yan Zhang, Yao Zhang, Yi Zhang, A. Dbeyssi, Z. H. Zhang, G. Zhao, J. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, Q. Zhao, R. E. de Boer, S. J. Zhao, Y. B. Zhao, Z. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, Y. Zheng, B. Zhong, D. Dedovich, C. Zhong, L. P. Zhou, Qian Zhou, X. K. Zhou, A. N. Zhu, J. Zhu, K. J. Zhu, R. Aliberti, Z. Y. Deng, S. H. Zhu, T. J. Zhu, W. J. Zhu, Y. C. Zhu, Z. A. Zhu, B. S. Zou, J. H. Zou, A. Denig, I. Denysenko, M. Destefanis, Y. Ding, C. Dong, J. Dong, L. Y. Dong, M. Y. Dong, A. Amoroso, X. Dong, S. X. Du, Y. L. Fan, J. Fang, S. S. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, M. R. An, C. Q. Feng, J. H. Feng, M. Fritsch, C. D. Fu, Y. S. Gao, I. Garzia, P. T. Ge, Q. An, C. Geng, E. Gersabeck, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, M. Greco, L. M. Gu, M. H. Gu, X. H. Bai, S. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, R. P. Guo, Y. P. Guo, A. Guskov, T. T. Han, W. Y. Han
Bibliographic record
VenueGSI Repository (German Federal Government) · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsInstitute of Particle Physics
FundersShanghai Key Laboratory for Particle Physics and CosmologyStrongScience and Technology Facilities CouncilDeutsche ForschungsgemeinschaftIstituto Nazionale di Fisica NucleareVetenskapsrådetNational Natural Science Foundation of ChinaChinese Academy of SciencesRoyal SocietyKnut och Alice Wallenbergs StiftelseInstitute of High Energy PhysicsU.S. Department of Energy
KeywordsPhysicsBhabha scatteringDetectorNuclear physicsHadronParticle physicsScatteringOptics
DOInot available
Abstract
Using a dedicated data sample taken in 2018 on the $J/\psi$ peak, we perform a detailed study of the trigger efficiencies of the BESIII detector. The efficiencies are determined from three representative physics processes, namely Bhabha-scattering, dimuon production and generic hadronic events with charged particles. The combined efficiency of all active triggers approaches $100\%$ in most cases with uncertainties small enough as not to affect most physics analyses.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.386
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 head0.018
GPT teacher head0.261
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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".
Quick stats
Citations4
Published2021
Admission routes1
Has abstractyes