Author: "Rong, P" - Searchworks@Jio Institute Digital Library Search Results

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80,831 results on '"Rong, P"'

1. Search for the FCNC charmonium decay $J/\psi \to D^0 \mu^+ \mu^- + \text{c.c.}$

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, M. H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., 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, Z. K., Choi, S. K., Chu, X., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Ding, Y. X., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Y. Y., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, J. D., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, K. D., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Hu, Z. M., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. J., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lan, Q., Lan, W. N., Lei, T. T., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, C. K., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, Lei, Li, M. H., Li, M. R., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., Li, Y. G., Li, Y. P., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, L. B., Liao, M. H., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, L. Q., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. J., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, W. T., Liu, X., Liu, X. Y., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, Y., Lu, Y. H., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, J. S., Luo, M. X., Luo, T., Luo, X. L., Lv, Z. Y., Lyu, X. R., Lyu, Y. F., Lyu, Y. H., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Melendi, F. M., Meng, Y. H., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, J. L., Qin, L. Q., Qin, L. Y., Qin, P. B., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Rivetti, A., Rolo, M., Rong, G., Rong, S. S., Rosini, F., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, S. Y., Shi, X., Song, H. L., Song, J. J., Song, T. Z., Song, W. M., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, Y. C., Sun, Y. H., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, L. F., Tang, M., Tang, Y. A., Tao, L. Y., Tat, M., Teng, J. X., Tian, J. Y., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wang, B., Wang, Bo, Wang, C., Wang, Cong, Wang, D. Y., Wang, H. J., Wang, J. J., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Yuan, Wang, Z., Wang, Z. L., Wang, Z. Q., Wang, Z. Y., Wei, D. H., Wei, H. R., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, Lianjie, Wu, S. G., Wu, S. M., Wu, X., Wu, X. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, K. J., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, H. Y., Yan, L., Yan, W. B., Yan, W. C., Yan, W. P., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, R. J., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. H., Yang, Y. Q., Yang, Y. X., Yang, Y. Z., Ye, M., Ye, M. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, H., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, Zafar, A. A., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, N., Zhang, P., Zhang, Q., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. L., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Q., Zhou, J. Y., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, W. J., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zhuang, X. Y., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Based on a data sample of $(10087 \pm 44) \times 10^6$ $J/\psi$ events taken with the BESIII detector, we search for the flavor-changing neutral current charmonium decay $J/\psi \to D^{0} \mu^{+} \mu^{-} + \text{c.c.}$. No significant signal above the background is observed, and the upper limit on its branching fraction is set to be $\mathcal{B}(J/\psi \to D^{0}\mu^{+}\mu^{-} + \text{c.c.} ) < 1.1 \times 10^{-7}$ at the 90% confidence level. This marks the first search for a flavor-changing neutral current charmonium decay involving muons in the final state., Comment: 20 pages, 4 figures
Published: 2025

2. Observation of the charmonium decay $\eta_c\to\gamma\gamma$

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, M. H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., 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., Choi, S. K., Chu, X., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Ding, Y. X., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Y. Y., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. J., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lan, Q., Lan, W. N., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, C. K., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, Lei, Li, M. H., Li, M. R., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, L. Q., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. J., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, W. T., Liu, X., Liu, X. Y., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, Y., Lu, Y. H., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, J. S., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Lyu, Y. H., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Y. H., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, P. B., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Rivetti, A., Rolo, M., Rong, G., Rong, S. S., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, Y. C., Sun, Y. H., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, L. F., Tang, M., Tang, Y. A., Tao, L. Y., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wang, B., Wang, Bo, Wang, C., Wang, D. Y., Wang, H. J., Wang, J. J., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, Lianjie, Wu, S. G., Wu, S. M., Wu, X., Wu, X. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, K. J., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, H. Y., Yan, L., Yan, W. B., Yan, W. C., Yan, W. P., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, R. J., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. Q., Yang, Y. X., Yang, Y. Z., Ye, M., Ye, M. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, H., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, Zafar, A. A., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, N., Zhang, P., Zhang, Q., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, W. J., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zhuang, X. Y., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Using $(2712.4\pm14.3)\times10^{6}$ $\psi(3686)$ events collected with the BESIII detector at the BEPCII collider, the decay $\eta_c\to\gamma\gamma$ in $J/\psi\to\gamma\eta_c$ is observed for the first time. We determine the product branching fraction $\mathcal{B}(J/\psi\to\gamma\eta_c)\times\mathcal{B}(\eta_c\to\gamma\gamma)=(5.23\pm0.26_{\rm{stat.}}\pm0.30_{\rm{syst.}})\times10^{-6}$. This result is well consistent with the LQCD calculation $(5.34\pm0.16)\times10^{-6}$ from HPQCD in 2023. By using the world-average values of $\mathcal{B}(J/\psi\to\gamma\eta_c)$ and the total decay width of $\eta_c$, the partial decay width $\Gamma(\eta_c\to\gamma\gamma)$ is determined to be $(11.30\pm0.56_{\rm{stat.}}\pm0.66_{\rm{syst.}}\pm1.14_{\rm{ref.}})~\rm{keV}$, which deviates from the corresponding world-average value by $3.4\sigma$., Comment: 10 pages, 4 figures
Published: 2024

3. Establishing a New Benchmark in Quantum Computational Advantage with 105-qubit Zuchongzhi 3.0 Processor

Author: Gao, Dongxin, Fan, Daojin, Zha, Chen, Bei, Jiahao, Cai, Guoqing, Cai, Jianbin, Cao, Sirui, Zeng, Xiangdong, Chen, Fusheng, Chen, Jiang, Chen, Kefu, Chen, Xiawei, Chen, Xiqing, Chen, Zhe, Chen, Zhiyuan, Chen, Zihua, Chu, Wenhao, Deng, Hui, Deng, Zhibin, Ding, Pei, Ding, Xun, Ding, Zhuzhengqi, Dong, Shuai, Dong, Yupeng, Fan, Bo, Fu, Yuanhao, Gao, Song, Ge, Lei, Gong, Ming, Gui, Jiacheng, Guo, Cheng, Guo, Shaojun, Guo, Xiaoyang, He, Tan, Hong, Linyin, Hu, Yisen, Huang, He-Liang, Huo, Yong-Heng, Jiang, Tao, Jiang, Zuokai, Jin, Honghong, Leng, Yunxiang, Li, Dayu, Li, Dongdong, Li, Fangyu, Li, Jiaqi, Li, Jinjin, Li, Junyan, Li, Junyun, Li, Na, Li, Shaowei, Li, Wei, Li, Yuhuai, Li, Yuan, Liang, Futian, Liang, Xuelian, Liao, Nanxing, Lin, Jin, Lin, Weiping, Liu, Dailin, Liu, Hongxiu, Liu, Maliang, Liu, Xinyu, Liu, Xuemeng, Liu, Yancheng, Lou, Haoxin, Ma, Yuwei, Meng, Lingxin, Mou, Hao, Nan, Kailiang, Nie, Binghan, Nie, Meijuan, Ning, Jie, Niu, Le, Peng, Wenyi, Qian, Haoran, Rong, Hao, Rong, Tao, Shen, Huiyan, Shen, Qiong, Su, Hong, Su, Feifan, Sun, Chenyin, Sun, Liangchao, Sun, Tianzuo, Sun, Yingxiu, Tan, Yimeng, Tan, Jun, Tang, Longyue, Tu, Wenbing, Wan, Cai, Wang, Jiafei, Wang, Biao, Wang, Chang, Wang, Chen, Wang, Chu, Wang, Jian, Wang, Liangyuan, Wang, Rui, Wang, Shengtao, Wang, Xinzhe, Wei, Zuolin, Wei, Jiazhou, Wu, Dachao, Wu, Gang, Wu, Jin, Wu, Shengjie, Wu, Yulin, Xie, Shiyong, Xin, Lianjie, Xu, Yu, Xue, Chun, Yan, Kai, Yang, Weifeng, Yang, Xinpeng, Yang, Yang, Ye, Yangsen, Ye, Zhenping, Ying, Chong, Yu, Jiale, Yu, Qinjing, Yu, Wenhu, Zhan, Shaoyu, Zhang, Feifei, Zhang, Haibin, Zhang, Kaili, Zhang, Pan, Zhang, Wen, Zhang, Yiming, Zhang, Yongzhuo, Zhang, Lixiang, Zhao, Guming, Zhao, Peng, Zhao, Xianhe, Zhao, Xintao, Zhao, Youwei, Zhao, Zhong, Zheng, Luyuan, Zhou, Fei, Zhou, Liang, Zhou, Na, Zhou, Naibin, Zhou, Shifeng, Zhou, Shuang, Zhou, Zhengxiao, Zhu, Chengjun, Zhu, Qingling, Zou, Guihong, Zou, Haonan, Zhang, Qiang, Lu, Chao-Yang, Peng, Cheng-Zhi, Zhu, XiaoBo, and Pan, Jian-Wei
Subjects: Quantum Physics
Abstract: In the relentless pursuit of quantum computational advantage, we present a significant advancement with the development of Zuchongzhi 3.0. This superconducting quantum computer prototype, comprising 105 qubits, achieves high operational fidelities, with single-qubit gates, two-qubit gates, and readout fidelity at 99.90%, 99.62% and 99.18%, respectively. Our experiments with an 83-qubit, 32-cycle random circuit sampling on Zuchongzhi 3.0 highlight its superior performance, achieving one million samples in just a few hundred seconds. This task is estimated to be infeasible on the most powerful classical supercomputers, Frontier, which would require approximately $6.4\times 10^9$ years to replicate the task. This leap in processing power places the classical simulation cost six orders of magnitude beyond Google's SYC-67 and SYC-70 experiments [Nature 634, 328(2024)], firmly establishing a new benchmark in quantum computational advantage. Our work not only advances the frontiers of quantum computing but also lays the groundwork for a new era where quantum processors play an essential role in tackling sophisticated real-world challenges.
Published: 2024

4. Potential signature of new magicity from universal aspects of nuclear charge radii

Author: Yang, Dan, Rong, Yu-Ting, An, Rong, and Shi, Rui-Xiang
Subjects: Nuclear Theory
Abstract: Shell quenching phenomena in nuclear charge radii are typically observed at the well-established neutron magic numbers. However, the recent discovery of potential new magic numbers at the neutron numbers $N = 32$ and $N = 34$ has sparked renewed interest in this mass region. This work further inspects into the charge radii of nuclei around the $N = 28$ shell closure using the relativistic Hartree-Bogoliubov model. We incorporate meson exchange and point-coupling effective nucleon-nucleon interactions alongside the Bogoliubov transformation for pairing corrections. To accurately capture the odd-even staggering and shell closure effects observed in charge radii, neutron-proton correlations around Fermi surface are explicitly considered. The charge radii of Ca and Ni isotopes are used to test the theoretical model and show an improvement with neutron-proton pairing corrections, in particular for neutron-rich isotopes. Our calculations reveal a inverted parabolic-like trend in the charge radii along the $N = 28$ isotones for proton numbers $Z$ between 20 and 28. Additionally, the shell closure effect of $Z = 28$ persists across the $N = 28$, 30, 32, and 34 isotonic chains, albeit with a gradual weakening trend. Notably, the significantly abrupt changes in charge radii are observed across $Z = 22$ along both the $N = 32$ and $N = 34$ isotonic chains. This kink at $Z = 22$ comes from the sudden decrease of the neuron-proton correlation around Fermi surfaces across $Z = 22$ for $N = 30$, 32, and 34 isotones, and might provide a signature for identifying the emergence of neutron magic numbers $N = 32$ and 34. Furthermore, the calculated charge radii for these isotonic chains ($N = 28$, 30, 32, and 34) can serve as reliable guidelines for future experimental measurements.
Published: 2024
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5. Hybrid Parallel Collaborative Simulation Framework Integrating Device Physics with Circuit Dynamics for PDAE-Modeled Power Electronic Equipment

Author: Shi, Qingyuan, Zhuang, Chijie, Liu, Jiapeng, Lin, Bo, Peng, Xiyu, Wu, Dan, Liu, Zhicheng, and Zeng, Rong
Subjects: Electrical Engineering and Systems Science - Systems and Control
Abstract: Optimizing high-performance power electronic equipment, such as power converters, requires multiscale simulations that incorporate the physics of power semiconductor devices and the dynamics of other circuit components, especially in conducting Design of Experiments (DoEs), defining the safe operating area of devices, and analyzing failures related to semiconductor devices. However, current methodologies either overlook the intricacies of device physics or do not achieve satisfactory computational speeds. To bridge this gap, this paper proposes a Hybrid-Parallel Collaborative (HPC) framework specifically designed to analyze the Partial Differential Algebraic Equation (PDAE) modeled power electronic equipment, integrating the device physics and circuit dynamics. The HPC framework employs a dynamic iteration to tackle the challenges inherent in solving the coupled nonlinear PDAE system, and utilizes a hybrid-parallel computing strategy to reduce computing time. Physics-based system partitioning along with hybrid-process-thread parallelization on shared and distributed memory are employed, facilitating the simulation of hundreds of partial differential equations (PDEs)-modeled devices simultaneously without compromising speed. Experiments based on the hybrid line commutated converter and reverse-blocking integrated gate-commutated thyristors are conducted under 3 typical real-world scenarios: semiconductor device optimization for the converter; converter design optimization; and device failure analysis. The HPC framework delivers simulation speed up to 60 times faster than the leading commercial software, while maintaining carrier-level accuracy in the experiments. This shows great potential for comprehensive analysis and collaborative optimization of devices and electronic power equipment, particularly in extreme conditions and failure scenarios.
Published: 2025

6. Study of $\eta\rightarrow\pi^+\pi^-l^+l^-$

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., 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, Z. Y., Choi, S. K., Chu, X., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y., Gao, Y. N., Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavania, A., Lavezzi, L., Lei, T. T., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Malik, Q. A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, J. J., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, W. H., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, X. Q., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Using a sample of $(10087\pm44)\times10^{6}$ $J/\psi$ events accumulated with the BESIII detector, we analyze the decays $\eta\rightarrow\pi^+\pi^-l^+l^-$ ($l=e$ or $\mu$) via the process $J/\psi\rightarrow\gamma\eta$. The branching fraction of $\eta\rightarrow\pi^+\pi^-e^+e^-$ is measured to be $\mathcal{B}(\eta\rightarrow\pi^+\pi^-e^+e^-)=(3.07\pm0.12_{\rm{stat.}}\pm0.19_{\rm{syst.}}) \times10^{-4}$. No signal events are observed for the $\eta\rightarrow\pi^{+}\pi^{-}\mu^{+}\mu^{-}$ decay, leading to an upper limit on the branching fraction of $\mathcal{B}(\eta\rightarrow\pi^{+}\pi^{-}\mu^{+}\mu^{-})<4.0\times10^{-7}$ at the 90\% confidence level. Furthermore, the $CP$-violation asymmetry parameter is found to be $\mathcal{A}_{CP}(\eta\rightarrow\pi^{+}\pi^{-}e^{+}e^{-})=(-4.04\pm4.69_{\rm{stat.}}\pm0.14_{\rm{syst.}})\%$, showing no evidence of $CP$-violation with current statistics. Additionally, we extract the transition form factor from the decay amplitude of $\eta\rightarrow\pi^+\pi^-e^+e^-$. Finally, axion-like particles are searched for via the decay $\eta\rightarrow\pi^+\pi^-a, a\rightarrow e^+e^-$, and upper limits on this branching fraction relative to that of $\eta\rightarrow\pi^+\pi^-e^+e^-$ are presented as a function of the axion-like particle mass in the range $5-200\ \mathrm{MeV}/c^{2}$.
Published: 2025

7. Observation of single-photon azimuthal backflow with weak measurement

Author: Zhang, Zhen-Fei, Huang, Peng-Fei, Dong, Shan-Chuan, Rong, Yan-Xin, Xu, Jin-Shi, Gu, Yong-Jian, and Xiao, Ya
Subjects: Physics - Optics, Quantum Physics
Abstract: Quantum backflow, a counterintuitive interference phenomenon where particles with positive momentum can propagate backward, is important in applications involving light-matter interactions. To date, experimental demonstrations of backflow have been restricted to classical optical systems, where momentum is measured using the slit scanning technique or the Shack-Hartmann wavefront sensor technique. However, these techniques have low spatial resolution due to limitations in slit width and Fourier transform lenslet array density. Here, by adopting the technique of weak measurement, we report an observation of azimuthal backflow both theoretically and experimentally. Our results show that a heralded single photon, prepared in specific superposition states with solely negative orbital angular momentum (OAM), exhibits positive OAM. The effects of mode ratio, propagation distance and OAM index on the azimuthal backflow are systematically investigated. Our method avoids using slits and lenslet arrays, allowing for the accurate extraction of photon momentum at each pixel. This work provides new insights and techniques for observing and manipulating backflow in quantum systems., Comment: 5 pages, 3 figures
Published: 2025
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8. Holographic Bound of Casimir Effect in General Dimensions

Author: Miao, Rong-Xin
Subjects: High Energy Physics - Theory, General Relativity and Quantum Cosmology
Abstract: Recently, it has been proposed that holography imposes a universal lower bound on the Casimir effect for 3d BCFTs. This paper generalizes the discussions to higher dimensions. We find Einstein gravity, DGP gravity, and Gauss-Bonnet gravity sets a universal lower bound of the strip Casimir effect in general dimensions. We verify the holographic bound by free theories and $O(N)$ models in the $\epsilon$ expansions. We also derive the holographic bound of the Casimir effect for a wedge and confirm free theories obey it. It implies holography sets a lower bound of the Casimir effect for general boundary shapes, not limited to the strip. Finally, we briefly comment on the impact of mass and various generalizations and applications of our results., Comment: 27 pages, 8 figures. arXiv admin note: text overlap with arXiv:2412.04122
Published: 2025

9. Generative Visual Commonsense Answering and Explaining with Generative Scene Graph Constructing

Author: Yuan, Fan, Fang, Xiaoyuan, Quan, Rong, Li, Jing, Bi, Wei, Xu, Xiaogang, and Li, Piji
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Computation and Language
Abstract: Visual Commonsense Reasoning, which is regarded as one challenging task to pursue advanced visual scene comprehension, has been used to diagnose the reasoning ability of AI systems. However, reliable reasoning requires a good grasp of the scene's details. Existing work fails to effectively exploit the real-world object relationship information present within the scene, and instead overly relies on knowledge from training memory. Based on these observations, we propose a novel scene-graph-enhanced visual commonsense reasoning generation method named \textit{\textbf{G2}}, which first utilizes the image patches and LLMs to construct a location-free scene graph, and then answer and explain based on the scene graph's information. We also propose automatic scene graph filtering and selection strategies to absorb valuable scene graph information during training. Extensive experiments are conducted on the tasks and datasets of scene graph constructing and visual commonsense answering and explaining, respectively. Experimental results and ablation analysis demonstrate the effectiveness of our proposed framework.
Published: 2025

10. LAMOST medium-resolution spectroscopic survey of Galactic Open Clusters (LAMOST-MRS-O): An overview of survey plan and preliminary results

Author: Zhang, Xi, Liu, Chengzhi, Zhong, Jing, Chen, Li, Luo, Ali, Shi, Jian-Rong, Liu, Chao, Chen, JianJun, Zhang, Haotong, and Hou, Jinliang
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: As part of the LAMOST medium-resolution spectroscopic survey, the LAMOST-MRS-O is a non-time domain survey that aims to perform medium-resolution spectral observations for member stars in the open cluster area. This survey plans to obtain the spectroscopic parameters such as radial velocity and metal abundances of member stars and provide data support for further study on the chemical and dynamical characteristics and evolution of open clusters in combination with Gaia data. We have completed the observations on ten open cluster fields and obtained 235184 medium-resolution spectra of 133792 stars. Based on the data analyzed of LAMOST DR11V1.1, for some clusters of particular concern, it is found that the sampling ratio of members stars with Gmag < 15 mag can reach 70%, which indicates that the LAMOST-MRS-O has reached our initial design goal., Comment: 16 pages, 11 figures. Accepted for publication in RAA
Published: 2025
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11. Natural Language-Assisted Multi-modal Medication Recommendation

Author: Tan, Jie, Rong, Yu, Zhao, Kangfei, Bian, Tian, Xu, Tingyang, Huang, Junzhou, Cheng, Hong, and Meng, Helen
Subjects: Computer Science - Artificial Intelligence
Abstract: Combinatorial medication recommendation(CMR) is a fundamental task of healthcare, which offers opportunities for clinical physicians to provide more precise prescriptions for patients with intricate health conditions, particularly in the scenarios of long-term medical care. Previous research efforts have sought to extract meaningful information from electronic health records (EHRs) to facilitate combinatorial medication recommendations. Existing learning-based approaches further consider the chemical structures of medications, but ignore the textual medication descriptions in which the functionalities are clearly described. Furthermore, the textual knowledge derived from the EHRs of patients remains largely underutilized. To address these issues, we introduce the Natural Language-Assisted Multi-modal Medication Recommendation(NLA-MMR), a multi-modal alignment framework designed to learn knowledge from the patient view and medication view jointly. Specifically, NLA-MMR formulates CMR as an alignment problem from patient and medication modalities. In this vein, we employ pretrained language models(PLMs) to extract in-domain knowledge regarding patients and medications, serving as the foundational representation for both modalities. In the medication modality, we exploit both chemical structures and textual descriptions to create medication representations. In the patient modality, we generate the patient representations based on textual descriptions of diagnosis, procedure, and symptom. Extensive experiments conducted on three publicly accessible datasets demonstrate that NLA-MMR achieves new state-of-the-art performance, with a notable average improvement of 4.72% in Jaccard score. Our source code is publicly available on https://github.com/jtan1102/NLA-MMR_CIKM_2024., Comment: 10 pages
Published: 2025
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12. Large Anomalous Hall Effect in a Noncoplanar Magnetic Heterostructure

Author: Song, Anke, Zhang, Jine, Chen, Yequan, Zhang, Zhizhong, Cheng, Xinjuan, Xu, Ruijie, Zhuang, Wenzhuo, Sun, Wenxuan, Zhang, Yong, Zhang, Xu, Chen, Zhongqiang, Song, Fengqi, Zhang, Yue, Zhai, Xuechao, Xu, Yongbing, Zhao, Weisheng, Zhang, Rong, and Wang, Xuefeng
Subjects: Condensed Matter - Materials Science, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Strongly Correlated Electrons
Abstract: The anomalous Hall effect (AHE) occurs in magnetic systems and also unexpectedly in non-magnetic materials adjacent to magnetic insulators via the heterointerface interactions. However, the AHE in heterostructures induced by magnetic proximity effect remains quite weak, restricting their practical device applications. Here, we report a large intrinsic AHE with a resistivity of 114 n{\Omega} cm at 5 K in noncoplanar magnetic heterostructures of Cr5Te6/Pt. This is the record-high AHE value among all the magnetic insulators/heavy metal heterostructures. A reversal of the AHE signal occurs due to the reconstruction of Berry curvature at the Fermi level, which is verified by the first-principles calculations. Topological spin textures at the interface are directly visualized via high-magnetic-field magnetic force microscopy, which accounts for the large AHE, as confirmed by the atomic simulations. These findings open a new avenue for exploring the large AHE in heterointerfaces and facilitate the potential applications in topological spintronic devices., Comment: 23 pages, 15 figures
Published: 2025

13. Detection of AI Deepfake and Fraud in Online Payments Using GAN-Based Models

Author: Ke, Zong, Zhou, Shicheng, Zhou, Yining, Chang, Chia Hong, and Zhang, Rong
Subjects: Computer Science - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Computer Vision and Pattern Recognition
Abstract: This study explores the use of Generative Adversarial Networks (GANs) to detect AI deepfakes and fraudulent activities in online payment systems. With the growing prevalence of deepfake technology, which can manipulate facial features in images and videos, the potential for fraud in online transactions has escalated. Traditional security systems struggle to identify these sophisticated forms of fraud. This research proposes a novel GAN-based model that enhances online payment security by identifying subtle manipulations in payment images. The model is trained on a dataset consisting of real-world online payment images and deepfake images generated using advanced GAN architectures, such as StyleGAN and DeepFake. The results demonstrate that the proposed model can accurately distinguish between legitimate transactions and deepfakes, achieving a high detection rate above 95%. This approach significantly improves the robustness of payment systems against AI-driven fraud. The paper contributes to the growing field of digital security, offering insights into the application of GANs for fraud detection in financial services. Keywords- Payment Security, Image Recognition, Generative Adversarial Networks, AI Deepfake, Fraudulent Activities, Comment: The paper will be published and indexed by IEEE at 2025 8th International Conference on Advanced Algorithms and Control Engineering (ICAACE 2025)
Published: 2025

14. SplatMAP: Online Dense Monocular SLAM with 3D Gaussian Splatting

Author: Hu, Yue, Liu, Rong, Chen, Meida, Beerel, Peter, and Feng, Andrew
Subjects: Computer Science - Computer Vision and Pattern Recognition, I.4
Abstract: Achieving high-fidelity 3D reconstruction from monocular video remains challenging due to the inherent limitations of traditional methods like Structure-from-Motion (SfM) and monocular SLAM in accurately capturing scene details. While differentiable rendering techniques such as Neural Radiance Fields (NeRF) address some of these challenges, their high computational costs make them unsuitable for real-time applications. Additionally, existing 3D Gaussian Splatting (3DGS) methods often focus on photometric consistency, neglecting geometric accuracy and failing to exploit SLAM's dynamic depth and pose updates for scene refinement. We propose a framework integrating dense SLAM with 3DGS for real-time, high-fidelity dense reconstruction. Our approach introduces SLAM-Informed Adaptive Densification, which dynamically updates and densifies the Gaussian model by leveraging dense point clouds from SLAM. Additionally, we incorporate Geometry-Guided Optimization, which combines edge-aware geometric constraints and photometric consistency to jointly optimize the appearance and geometry of the 3DGS scene representation, enabling detailed and accurate SLAM mapping reconstruction. Experiments on the Replica and TUM-RGBD datasets demonstrate the effectiveness of our approach, achieving state-of-the-art results among monocular systems. Specifically, our method achieves a PSNR of 36.864, SSIM of 0.985, and LPIPS of 0.040 on Replica, representing improvements of 10.7%, 6.4%, and 49.4%, respectively, over the previous SOTA. On TUM-RGBD, our method outperforms the closest baseline by 10.2%, 6.6%, and 34.7% in the same metrics. These results highlight the potential of our framework in bridging the gap between photometric and geometric dense 3D scene representations, paving the way for practical and efficient monocular dense reconstruction.
Published: 2025

15. Harnessing Large Language Models for Disaster Management: A Survey

Author: Lei, Zhenyu, Dong, Yushun, Li, Weiyu, Ding, Rong, Wang, Qi, and Li, Jundong
Subjects: Computer Science - Computation and Language, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract: Large language models (LLMs) have revolutionized scientific research with their exceptional capabilities and transformed various fields. Among their practical applications, LLMs have been playing a crucial role in mitigating threats to human life, infrastructure, and the environment. Despite growing research in disaster LLMs, there remains a lack of systematic review and in-depth analysis of LLMs for natural disaster management. To address the gap, this paper presents a comprehensive survey of existing LLMs in natural disaster management, along with a taxonomy that categorizes existing works based on disaster phases and application scenarios. By collecting public datasets and identifying key challenges and opportunities, this study aims to guide the professional community in developing advanced LLMs for disaster management to enhance the resilience against natural disasters.
Published: 2025

16. Study of light-meson resonances decaying to $K^0_{\rm S} K \pi$ in the $B \to (K^0_{\rm S} K \pi) K$ channels

Author: LHCb collaboration, Aaij, R., Abdelmotteleb, A. S. W., Beteta, C. Abellan, Abudinén, F., Ackernley, T., Adefisoye, A. A., Adeva, B., Adinolfi, M., Adlarson, P., Agapopoulou, C., Aidala, C. A., Ajaltouni, Z., Akar, S., Akiba, K., Albicocco, P., Albrecht, J., Alessio, F., Alexander, M., Aliouche, Z., Cartelle, P. Alvarez, Amalric, R., Amato, S., Amey, J. L., Amhis, Y., An, L., Anderlini, L., Andersson, M., Andreianov, A., Andreola, P., Andreotti, M., Andreou, D., Anelli, A., Ao, D., Archilli, F., Argenton, M., Cuendis, S. Arguedas, Artamonov, A., Artuso, M., Aslanides, E., Da Silva, R. Ataíde, Atzeni, M., Audurier, B., Bacher, D., Perea, I. Bachiller, Bachmann, S., Bachmayer, M., Back, J. J., Rodriguez, P. Baladron, Balagura, V., Balboni, A., Baldini, W., Balzani, L., Bao, H., Leite, J. Baptista de Souza, Pretel, C. Barbero, Barbetti, M., Barbosa, I. R., Barlow, R. J., Barnyakov, M., Barsuk, S., Barter, W., Bartz, J., Basels, J. M., Bashir, S., Bassi, G., Batsukh, B., Battista, P. B., Bay, A., Beck, A., Becker, M., Bedeschi, F., Bediaga, I. B., Behling, N. A., Belin, S., Belous, K., Belov, I., Belyaev, I., Benane, G., Bencivenni, G., Ben-Haim, E., Berezhnoy, A., Bernet, R., Andres, S. Bernet, Bertolin, A., Betancourt, C., Betti, F., Bex, J., Bezshyiko, Ia., Bhom, J., Bieker, M. S., Biesuz, N. V., Billoir, P., Biolchini, A., Birch, M., Bishop, F. C. R., Bitadze, A., Bizzeti, A., Blake, T., Blanc, F., Blank, J. E., Blusk, S., Bocharnikov, V., Boelhauve, J. A., Garcia, O. Boente, Boettcher, T., Bohare, A., Boldyrev, A., Bolognani, C. S., Bolzonella, R., Bonacci, R. B., Bondar, N., Bordelius, A., Borgato, F., Borghi, S., Borsato, M., Borsuk, J. T., Bottalico, E., Bouchiba, S. A., Bovill, M., Bowcock, T. J. V., Boyer, A., Bozzi, C., Brandenburg, J. D., Rodriguez, A. Brea, Breer, N., Brodzicka, J., Gonzalo, A. Brossa, Brown, J., Brundu, D., Buchanan, E., Buonincontri, L., Marcos, M. Burgos, Burke, A. T., Burr, C., Butter, J. S., Buytaert, J., Byczynski, W., Cadeddu, S., Cai, H., Caillet, A. C., Calabrese, R., Ramirez, S. Calderon, Calefice, L., Cali, S., Calvi, M., Gomez, M. Calvo, Magalhaes, P. Camargo, Bouzas, J. I. Cambon, Campana, P., Perez, D. H. Campora, Quezada, A. F. Campoverde, Capelli, S., Capriotti, L., Caravaca-Mora, R., Carbone, A., Salgado, L. Carcedo, Cardinale, R., Cardini, A., Carniti, P., Carus, L., Vidal, A. Casais, Caspary, R., Casse, G., Cattaneo, M., Cavallero, G., Cavallini, V., Celani, S., Cesare, S., Chadwick, A. J., Chahrour, I., Charles, M., Charpentier, Ph., Chatzianagnostou, E., Chefdeville, M., Chen, C., Chen, S., Chen, Z., Chernov, A., Chernyshenko, S., Chiotopoulos, X., Chobanova, V., Chrzaszcz, M., Chubykin, A., Chulikov, V., Ciambrone, P., Vidal, X. Cid, Ciezarek, G., Cifra, P., Clarke, P. E. L., Clemencic, M., Cliff, H. V., Closier, J., Toapaxi, C. Cocha, Coco, V., Cogan, J., Cogneras, E., Cojocariu, L., Collaviti, S., Collins, P., Colombo, T., Colonna, M., Comerma-Montells, A., Congedo, L., Contu, A., Cooke, N., Corredoira, I., Correia, A., Corti, G., Meldrum, J. Cottee, Couturier, B., Craik, D. C., Torres, M. Cruz, Rivera, E. Curras, Currie, R., Da Silva, C. L., Dadabaev, S., Dai, L., Dai, X., Dall'Occo, E., Dalseno, J., D'Ambrosio, C., Daniel, J., Danilina, A., d'Argent, P., Darze, G., Davidson, A., Davies, J. E., Davis, A., Francisco, O. De Aguiar, De Angelis, C., De Benedetti, F., de Boer, J., De Bruyn, K., De Capua, S., De Cian, M., Da Graca, U. De Freitas Carneiro, De Lucia, E., De Miranda, J. M., De Paula, L., De Serio, M., De Simone, P., De Vellis, F., de Vries, J. A., Debernardis, F., Decamp, D., Dedu, V., Dekkers, S., Del Buono, L., Delaney, B., Dembinski, H. -P., Deng, J., Denysenko, V., Deschamps, O., Dettori, F., Dey, B., Di Nezza, P., Diachkov, I., Didenko, S., Ding, S., Dittmann, L., Dobishuk, V., Docheva, A. D., Dong, C., Donohoe, A. M., Dordei, F., Reis, A. C. dos, Dowling, A. D., Duan, W., Duda, P., Dudek, M. W., Dufour, L., Duk, V., Durante, P., Duras, M. M., Durham, J. M., Durmus, O. D., Dziurda, A., Dzyuba, A., Easo, S., Eckstein, E., Egede, U., Egorychev, A., Egorychev, V., Eisenhardt, S., Ejopu, E., Eklund, L., Elashri, M., Ellbracht, J., Ely, S., Ene, A., Eschle, J., Esen, S., Evans, T., Fabiano, F., Falcao, L. N., Fan, Y., Fang, B., Fantini, L., Faria, M., Farmer, K., Fazzini, D., Felkowski, L., Feng, M., Feo, M., Casani, A. Fernandez, Gomez, M. Fernandez, Fernez, A. D., Ferrari, F., Rodrigues, F. Ferreira, Ferrillo, M., Ferro-Luzzi, M., Filippov, S., Fini, R. A., Fiorini, M., Firlej, M., Fischer, K. L., Fitzgerald, D. S., Fitzpatrick, C., Fiutowski, T., Fleuret, F., Fontana, M., Foreman, L. F., Forty, R., Foulds-Holt, D., Lima, V. Franco, Sevilla, M. Franco, Frank, M., Franzoso, E., Frau, G., Frei, C., Friday, D. A., Fu, J., Führing, Q., Fujii, Y., Fulghesu, T., Gabriel, E., Galati, G., Galati, M. D., Torreira, A. Gallas, Galli, D., Gambetta, S., Gandelman, M., Gandini, P., Ganie, B., Gao, H., Gao, R., Gao, T. Q., Gao, Y., Martin, L. M. Garcia, Moreno, P. Garcia, Pardiñas, J. García, Gardner, P., Garg, K. G., Garrido, L., Gaspar, C., Gerken, L. L., Gersabeck, E., Gersabeck, M., Gershon, T., Ghizzo, S., Ghorbanimoghaddam, Z., Giambastiani, L., Giasemis, F. I., Gibson, V., Giemza, H. K., Gilman, A. L., Giovannetti, M., Gioventù, A., Girardey, L., Giugliano, C., Giza, M. A., Gkougkousis, E. L., Glaser, F. C., Gligorov, V. V., Göbel, C., Golobardes, E., Golubkov, D., Golutvin, A., Fernandez, S. Gomez, Gomulka, W., Abrantes, F. Goncalves, Goncerz, M., Gong, G., Gooding, J. A., Gorelov, I. V., Gotti, C., Govorkova, E., Grabowski, J. P., Cardoso, L. A. Granado, Graugés, E., Graverini, E., Grazette, L., Graziani, G., Grecu, A. T., Greeven, L. M., Grieser, N. A., Grillo, L., Gromov, S., Gu, C., Guarise, M., Guerry, L., Guliaeva, V., Günther, P. A., Guseinov, A. -K., Gushchin, E., Guz, Y., Gys, T., Habermann, K., Hadavizadeh, T., Hadjivasiliou, C., Haefeli, G., Haen, C., Hallett, G., Halvorsen, M. M., Hamilton, P. M., Hammerich, J., Han, Q., Han, X., Hansmann-Menzemer, S., Hao, L., Harnew, N., Harris, T. H., Hartmann, M., Hashmi, S., He, J., Hemmer, F., Henderson, C., Henderson, R. D. L., Hennequin, A. M., Hennessy, K., Henry, L., Herd, J., Gascon, P. Herrero, Heuel, J., Hicheur, A., Mendizabal, G. Hijano, Horswill, J., Hou, R., Hou, Y., Howarth, N., Hu, J., Hu, W., Hu, X., Huang, W., Hulsbergen, W., Hunter, R. J., Hushchyn, M., Hutchcroft, D., Idzik, M., Ilin, D., Ilten, P., Inglessi, A., Iniukhin, A., Ishteev, A., Ivshin, K., Jacobsson, R., Jage, H., Elles, S. J. Jaimes, Jakobsen, S., Jans, E., Jashal, B. K., Jawahery, A., Jevtic, V., Jiang, E., Jiang, X., Jiang, Y., Jiang, Y. J., John, M., Rajan, A. John Rubesh, Johnson, D., Jones, C. R., Jones, T. P., Joshi, S., Jost, B., Castella, J. Juan, Jurik, N., Juszczak, I., Kaminaris, D., Kandybei, S., Kane, M., Kang, Y., Kar, C., Karacson, M., Karpenkov, D., Kauniskangas, A., Kautz, J. W., Kazanecki, M. K., Keizer, F., Kenzie, M., Ketel, T., Khanji, B., Kharisova, A., Kholodenko, S., Khreich, G., Kirn, T., Kirsebom, V. S., Kitouni, O., Klaver, S., Kleijne, N., Klimaszewski, K., Kmiec, M. R., Koliiev, S., Kolk, L., Konoplyannikov, A., Kopciewicz, P., Koppenburg, P., Korolev, M., Kostiuk, I., Kot, O., Kotriakhova, S., Kozachuk, A., Kravchenko, P., Kravchuk, L., Kreps, M., Krokovny, P., Krupa, W., Krzemien, W., Kshyvanskyi, O., Kubis, S., Kucharczyk, M., Kudryavtsev, V., Kulikova, E., Kupsc, A., Kutsenko, B. K., Lacarrere, D., Gonzalez, P. Laguarta, Lai, A., Lampis, A., Lancierini, D., Gomez, C. Landesa, Lane, J. J., Lane, R., Lanfranchi, G., Langenbruch, C., Langer, J., Lantwin, O., Latham, T., Lazzari, F., Lazzeroni, C., Gac, R. Le, Lee, H., Lefèvre, R., Leflat, A., Legotin, S., Lehuraux, M., Cid, E. Lemos, Leroy, O., Lesiak, T., Lesser, E. D., Leverington, B., Li, A., Li, C., Li, H., Li, K., Li, L., Li, M., Li, P., Li, P. -R., Li, Q., Li, S., Li, T., Li, Y., Lian, Z., Liang, X., Libralon, S., Lin, C., Lin, T., Lindner, R., Linton, H., Lisovskyi, V., Litvinov, R., Liu, F. L., Liu, G., Liu, K., Liu, S., Liu, W., Liu, Y., Liu, Y. L., Ordonez, G. Loachamin, Salvia, A. Lobo, Loi, A., Long, T., Lopes, J. H., Huertas, A. Lopez, Soliño, S. López, Lu, Q., Lucarelli, C., Lucchesi, D., Martinez, M. Lucio, Lukashenko, V., Luo, Y., Lupato, A., Luppi, E., Lynch, K., Lyu, X. -R., Ma, G. M., Maccolini, S., Machefert, F., Maciuc, F., Mack, B., Mackay, I., Mackey, L. M., Mohan, L. R. Madhan, Madurai, M. J., Maevskiy, A., Magdalinski, D., Maisuzenko, D., Majewski, M. W., Malczewski, J. J., Malde, S., Malentacca, L., Malinin, A., Maltsev, T., Manca, G., Mancinelli, G., Mancuso, C., Escalero, R. Manera, Manganella, F. M., Manuzzi, D., Marangotto, D., Marchand, J. F., Marchevski, R., Marconi, U., Mariani, E., Mariani, S., Benito, C. Marin, Marks, J., Marshall, A. M., Martel, L., Martelli, G., Martellotti, G., Martinazzoli, L., Martinelli, M., Gomez, D. Martinez, Santos, D. Martinez, Vidal, F. Martinez, Granollers, A. Martorell i, Massafferri, A., Matev, R., Mathad, A., Matiunin, V., Matteuzzi, C., Mattioli, K. R., Mauri, A., Maurice, E., Mauricio, J., Mayencourt, P., de Cos, J. Mazorra, Mazurek, M., McCann, M., Mcconnell, L., McGrath, T. H., McHugh, N. T., McNab, A., McNulty, R., Meadows, B., Meier, G., Melnychuk, D., Meng, F. M., Merk, M., Merli, A., Garcia, L. Meyer, Miao, D., Miao, H., Mikhasenko, M., Milanes, D. A., Minotti, A., Minucci, E., Miralles, T., Mitreska, B., Mitzel, D. S., Modak, A., Mohammed, R. A., Moise, R. D., Mokhnenko, S., Cardenas, E. F. Molina, Mombächer, T., Monk, M., Monteil, S., Gomez, A. Morcillo, Morello, G., Morello, M. J., Morgenthaler, M. P., Moron, J., Morren, W., Morris, A. B., Morris, A. G., Mountain, R., Mu, H., Mu, Z. M., Muhammad, E., Muheim, F., Mulder, M., Müller, K., Muñoz-Rojas, F., Murta, R., Naik, P., Nakada, T., Nandakumar, R., Nanut, T., Nasteva, I., Needham, M., Neri, N., Neubert, S., Neufeld, N., Neustroev, P., Nicolini, J., Nicotra, D., Niel, E. M., Nikitin, N., Niu, Q., Nogarolli, P., Nogga, P., Normand, C., Fernandez, J. Novoa, Nowak, G., Nunez, C., Nur, H. N., Oblakowska-Mucha, A., Obraztsov, V., Oeser, T., Okamura, S., Okhotnikov, A., Okhrimenko, O., Oldeman, R., Oliva, F., Olocco, M., Onderwater, C. J. G., O'Neil, R. H., Osthues, D., Goicochea, J. M. Otalora, Owen, P., Oyanguren, A., Ozcelik, O., Paciolla, F., Padee, A., Padeken, K. O., Pagare, B., Pais, P. R., Pajero, T., Palano, A., Palutan, M., Pan, X., Panshin, G., Paolucci, L., Papanestis, A., Pappagallo, M., Pappalardo, L. L., Pappenheimer, C., Parkes, C., Parmar, D., Passalacqua, B., Passaleva, G., Passaro, D., Pastore, A., Patel, M., Patoc, J., Patrignani, C., Paul, A., Pawley, C. J., Pellegrino, A., Peng, J., Altarelli, M. Pepe, Perazzini, S., Pereima, D., Da Costa, H. Pereira, Castro, A. Pereiro, Perret, P., Perrevoort, A., Perro, A., Peters, M. J., Petridis, K., Petrolini, A., Pfaller, J. P., Pham, H., Pica, L., Piccini, M., Piccolo, L., Pietrzyk, B., Pietrzyk, G., Pilato, R. N., Pinci, D., Pisani, F., Pizzichemi, M., Placinta, V., Casasus, M. Plo, Poeschl, T., Polci, F., Lener, M. Poli, Poluektov, A., Polukhina, N., Polyakov, I., Polycarpo, E., Ponce, S., Popov, D., Poslavskii, S., Prasanth, K., Prouve, C., Provenzano, D., Pugatch, V., Punzi, G., Qasim, S., Qian, Q. Q., Qian, W., Qin, N., Qu, S., Quagliani, R., Trejo, R. I. Rabadan, Rademacker, J. H., Rama, M., García, M. Ramírez, De Oliveira, V. Ramos, Pernas, M. Ramos, Rangel, M. S., Ratnikov, F., Raven, G., De Miguel, M. Rebollo, Redi, F., Reich, J., Reiss, F., Ren, Z., Resmi, P. K., Ribatti, R., Ricart, G. R., Riccardi, D., Ricciardi, S., Richardson, K., Richardson-Slipper, M., Rinnert, K., Robbe, P., Robertson, G., Rodrigues, E., Alvarez, A. Rodriguez, Fernandez, E. Rodriguez, Lopez, J. A. Rodriguez, Rodriguez, E. Rodriguez, Roensch, J., Rogachev, A., Rogovskiy, A., Rolf, D. L., Roloff, P., Romanovskiy, V., Vidal, A. Romero, Romolini, G., Ronchetti, F., Rong, T., Rotondo, M., Roy, S. R., Rudolph, M. S., Diaz, M. Ruiz, Fernandez, R. A. Ruiz, Vidal, J. Ruiz, Ryzhikov, A., Ryzka, J., Saavedra-Arias, J. J., Silva, J. J. Saborido, Sadek, R., Sagidova, N., Sahoo, D., Sahoo, N., Saitta, B., Salomoni, M., Sanderswood, I., Santacesaria, R., Rios, C. Santamarina, Santimaria, M., Santoro, L., Santovetti, E., Saputi, A., Saranin, D., Sarnatskiy, A., Sarpis, G., Sarpis, M., Satriano, C., Satta, A., Saur, M., Savrina, D., Sazak, H., Sborzacchi, F., Smead, L. G. Scantlebury, Scarabotto, A., Schael, S., Scherl, S., Schiller, M., Schindler, H., Schmelling, M., Schmidt, B., Schmitt, S., Schmitz, H., Schneider, O., Schopper, A., Schulte, N., Schulte, S., Schune, M. H., Schwemmer, R., Schwering, G., Sciascia, B., Sciuccati, A., Segal, I., Sellam, S., Semennikov, A., Senger, T., Soares, M. Senghi, Sergi, A., Serra, N., Sestini, L., Seuthe, A., Shang, Y., Shangase, D. M., Shapkin, M., Sharma, R. S., Shchemerov, I., Shchutska, L., Shears, T., Shekhtman, L., Shen, Z., Sheng, S., Shevchenko, V., Shi, B., Shi, Q., Shimizu, Y., Shmanin, E., Shorkin, R., Shupperd, J. D., Coutinho, R. Silva, Simi, G., Simone, S., Skidmore, N., Skwarnicki, T., Slater, M. W., Smallwood, J. C., Smith, E., Smith, K., Smith, M., Snoch, A., Lavra, L. Soares, Sokoloff, M. D., Soler, F. J. P., Solomin, A., Solovev, A., Solovyev, I., Sommerfeld, N. S., Song, R., Song, Y., Song, Y. S., De Almeida, F. L. Souza, De Paula, B. Souza, Norella, E. Spadaro, Spedicato, E., Speer, J. G., Spiridenkov, E., Spradlin, P., Sriskaran, V., Stagni, F., Stahl, M., Stahl, S., Stanislaus, S., Stefaniak, M., Stein, E. N., Steinkamp, O., Stenyakin, O., Stevens, H., Strekalina, D., Su, Y., Suljik, F., Sun, J., Sun, L., Sundfeld, D., Sutcliffe, W., Swallow, P. N., Swientek, K., Swystun, F., Szabelski, A., Szumlak, T., Tan, Y., Tang, Y., Tat, M. D., Terentev, A., Terzuoli, F., Teubert, F., Thomas, E., Thompson, D. J. D., Tilquin, H., Tisserand, V., T'Jampens, S., Tobin, M., Tomassetti, L., Tonani, G., Tong, X., Tork, T., Machado, D. Torres, Toscano, L., Tou, D. Y., Trippl, C., Tuci, G., Tuning, N., Uecker, L. H., Ukleja, A., Unverzagt, D. J., Urbach, B., Usachov, A., Ustyuzhanin, A., Uwer, U., Vagnoni, V., Cadenas, V. Valcarce, Valenti, G., Canudas, N. Valls, van Eldik, J., Van Hecke, H., van Herwijnen, E., Van Hulse, C. B., Van Laak, R., van Veghel, M., Vasquez, G., Gomez, R. Vazquez, Regueiro, P. Vazquez, Sierra, C. Vázquez, Vecchi, S., Velthuis, J. J., Veltri, M., Venkateswaran, A., Verdoglia, M., Vesterinen, M., Benet, D. Vico, Villalba, P. Vidrier, Diaz, M. Vieites, Vilasis-Cardona, X., Figueras, E. Vilella, Villa, A., Vincent, P., Volle, F. C., Bruch, D. vom, Voropaev, N., Vos, K., Vrahas, C., Wagner, J., Walsh, J., Walton, E. J., Wan, G., Wang, C., Wang, G., Wang, H., Wang, J., Wang, M., Wang, N. W., Wang, R., Wang, X., Wang, X. W., Wang, Y., Wang, Y. W., Wang, Z., Ward, J. A., Waterlaat, M., Watson, N. K., Websdale, D., Wei, Y., Wendel, J., Westhenry, B. D. C., White, C., Whitehead, M., Whiter, E., Wiederhold, A. R., Wiedner, D., Wilkinson, G., Wilkinson, M. K., Williams, M., Williams, M. J., Williams, M. R. J., Williams, R., Williams, Z., Wilson, F. F., Winn, M., Wislicki, W., Witek, M., Witola, L., Wormser, G., Wotton, S. A., Wu, H., Wu, J., Wu, X., Wu, Y., Wu, Z., Wyllie, K., Xian, S., Xiang, Z., Xie, Y., Xing, T. X., Xu, A., Xu, L., Xu, M., Xu, Z., Yang, K., Yang, S., Yang, X., Yang, Y., Yang, Z., Yeroshenko, V., Yeung, H., Yin, H., Yin, X., Yu, C. Y., Yu, J., Yuan, X., Yuan, Y, Zaffaroni, E., Zavertyaev, M., Zdybal, M., Zenesini, F., Zeng, C., Zeng, M., Zhang, C., Zhang, D., Zhang, J., Zhang, L., Zhang, S., Zhang, Y., Zhang, Y. Z., Zhang, Z., Zhao, Y., Zhelezov, A., Zheng, S. Z., Zheng, X. Z., Zheng, Y., Zhou, T., Zhou, X., Zhou, Y., Zhovkovska, V., Zhu, L. Z., Zhu, X., Zhukov, V., Zhuo, J., Zou, Q., Zuliani, D., and Zunica, G.
Subjects: High Energy Physics - Experiment
Abstract: A study is presented of $B^+ \to K^0_{\rm S} K^- \pi^+ K^-$ and $B^+ \to K^0_{\rm S} K^+ \pi^- K^+$ decays based on the analysis of proton-proton collision data collected with the LHCb detector at centre-of-mass energies of 7, 8 and 13 TeV, corresponding to an integrated luminosity of $9 fb^{-1}$. The $K^0_{\rm S} K \pi$ invariant-mass distributions of both $B^+$ decay modes show, in the $m(K^0_{\rm S} K \pi)<1.85$ GeV mass region, a rich spectrum of light-meson resonances, resolved using an amplitude analysis. A complex mixture of $J^{PC}=0^{-+}, 1^{++}$ and $1^{+-}$ resonances is observed, dominated by $\eta(1405)$, $\eta(1470)$, $\eta(1760)$, $f_1(1285)$, $f_1(1420)$ and $h_1(1405)$ resonances. The $K^0_{\rm S} K \pi$ Dalitz plots are dominated by asymmetric crossing $K^* \bar K$ bands which are different for the two $B^+$ decay modes. This is due to a different interference pattern between the $1^{++}$ and $1^{+-}$ amplitudes in the two channels. Branching fractions are measured for each resonant contribution., Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2024-045.html (LHCb public pages)
Published: 2025

17. A Diffusive Data Augmentation Framework for Reconstruction of Complex Network Evolutionary History

Author: Xu, En, Rong, Can, Ding, Jingtao, and Li, Yong
Subjects: Computer Science - Artificial Intelligence
Abstract: The evolutionary processes of complex systems contain critical information regarding their functional characteristics. The generation time of edges provides insights into the historical evolution of various networked complex systems, such as protein-protein interaction networks, ecosystems, and social networks. Recovering these evolutionary processes holds significant scientific value, including aiding in the interpretation of the evolution of protein-protein interaction networks. However, existing methods are capable of predicting the generation times of remaining edges given a partial temporal network but often perform poorly in cross-network prediction tasks. These methods frequently fail in edge generation time recovery tasks for static networks that lack timestamps. In this work, we adopt a comparative paradigm-based framework that fuses multiple networks for training, enabling cross-network learning of the relationship between network structure and edge generation times. Compared to separate training, this approach yields an average accuracy improvement of 16.98%. Furthermore, given the difficulty in collecting temporal networks, we propose a novel diffusion-model-based generation method to produce a large number of temporal networks. By combining real temporal networks with generated ones for training, we achieve an additional average accuracy improvement of 5.46% through joint training.
Published: 2025

18. Search for $K^0_S$ invisible decays

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., 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., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lan, W. N., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, Lei, Li, M. H., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Y. H., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, Bo, Wang, C., Wang, D. Y., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, Lianjie, Wu, X., Wu, X. H., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, W. P., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, R. J., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Y. Z., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, W. J., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Based on $(1.0087\pm0.0044)\times10^{10}$ $J/\psi$ events collected with the BESIII detector at the BEPCII $e^+e^-$ storage ring, we search for $K_{S}^{0}$ invisible decays via the $J/\psi \to \phi K_{S}^{0} K_{S}^{0}$ process. No significant signal is observed, and the upper limit of the branching fraction of these invisible decays is set at 8.4 $\times$ $10^{-4}$ at the 90\% confidence level. This is the first experimental search for $K^0_S$ invisible decays.
Published: 2025

19. Tunable superconductivity coexisting with the anomalous Hall effect in 1T'-WS2

Author: Hossain, Md Shafayat, Zhang, Qi, Graf, David, Iraola, Mikel, Müller, Tobias, Mardanya, Sougata, Tu, Yi-Hsin, Lai, Zhuangchai, Soldini, Martina O., Li, Siyuan, Yao, Yao, Jiang, Yu-Xiao, Cheng, Zi-Jia, Litskevich, Maksim, Casas, Brian, Cochran, Tyler A., Yang, Xian P., Kim, Byunghoon, Watanabe, Kenji, Taniguchi, Takashi, Chowdhury, Sugata, Bansil, Arun, Zhang, Hua, Chang, Tay-Rong, Fischer, Mark, Neupert, Titus, Balicas, Luis, and Hasan, M. Zahid
Subjects: Condensed Matter - Superconductivity, Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science, Physics - Applied Physics
Abstract: Transition metal dichalcogenides are a family of quasi-two-dimensional materials that display a high technological potential due to their wide range of electronic ground states, e.g., from superconducting to semiconducting, depending on the chemical composition, crystal structure, or electrostatic doping. Here, we unveil that by tuning a single parameter, the hydrostatic pressure P, a cascade of electronic phase transitions can be induced in the few-layer transition metal dichalcogenide 1T'-WS2, including superconducting, topological, and anomalous Hall effect phases. Specifically, as P increases, we observe a dual phase transition: the suppression of superconductivity with the concomitant emergence of an anomalous Hall effect at P=1.15 GPa. Remarkably, upon further increasing the pressure above 1.6 GPa, we uncover a reentrant superconducting state that emerges out of a state still exhibiting an anomalous Hall effect. This superconducting state shows a marked increase in superconducting anisotropy with respect to the phase observed at ambient pressure, suggesting a different superconducting state with a distinct pairing symmetry. Via first-principles calculations, we demonstrate that the system concomitantly transitions into a strong topological phase with markedly different band orbital characters and Fermi surfaces contributing to the superconductivity. These findings position 1T'-WS2 as a unique, tunable superconductor, wherein superconductivity, anomalous transport, and band features can be tuned through the application of moderate pressures.
Published: 2025

20. 40 Years of Interdisciplinary Research: Phases, Origins, and Key Turning Points (1981-2020)

Author: Rong, Guoyang, Chen, Ying, Ma, Feicheng, and Koch, Thorsten
Subjects: Statistics - Applications, Computer Science - Digital Libraries, Physics - Physics and Society
Abstract: This study examines the historical evolution of interdisciplinary research (IDR) over a 40-year period, focusing on its dynamic trends, phases, and key turning points. We apply time series analysis to identify critical years for interdisciplinary citations (CYICs) and categorizes IDR into three distinct phases based on these trends: Period I (1981-2002), marked by sporadic and limited interdisciplinary activity; Period II (2003-2016), characterized by the emergence of large-scale IDR led primarily by Medicine, with significant breakthroughs in cloning and medical technology; and Period III (2017-present), where IDR became a widely adopted research paradigm. Our findings indicate that IDR has been predominantly concentrated within the Natural Sciences, with Medicine consistently at the forefront, and highlights increasing contributions from Engineering and Environmental disciplines as a new trend. These insights enhance the understanding of the evolution of IDR, its driving factors, and the shifts in the focus of interdisciplinary collaborations., Comment: 7 pages, 3 figures
Published: 2025

21. Interpretable Enzyme Function Prediction via Residue-Level Detection

Author: Yang, Zhao, Su, Bing, Chen, Jiahao, and Wen, Ji-Rong
Subjects: Quantitative Biology - Biomolecules, Computer Science - Machine Learning
Abstract: Predicting multiple functions labeled with Enzyme Commission (EC) numbers from the enzyme sequence is of great significance but remains a challenge due to its sparse multi-label classification nature, i.e., each enzyme is typically associated with only a few labels out of more than 6000 possible EC numbers. However, existing machine learning algorithms generally learn a fixed global representation for each enzyme to classify all functions, thereby they lack interpretability and the fine-grained information of some function-specific local residue fragments may be overwhelmed. Here we present an attention-based framework, namely ProtDETR (Protein Detection Transformer), by casting enzyme function prediction as a detection problem. It uses a set of learnable functional queries to adaptatively extract different local representations from the sequence of residue-level features for predicting different EC numbers. ProtDETR not only significantly outperforms existing deep learning-based enzyme function prediction methods, but also provides a new interpretable perspective on automatically detecting different local regions for identifying different functions through cross-attentions between queries and residue-level features. Code is available at https://github.com/yangzhao1230/ProtDETR.
Published: 2025

22. Search for the leptonic decay $D^{+}\to e^{+}\nu_{e}$

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., 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, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: We search for the leptonic decay $D^+\to e^+\nu_{e}$ using an $e^+e^-$ collision data sample with an integrated luminosity of 20.3~fb$^{-1}$ collected with the BESIII detector at the center-of-mass energy of 3.773~GeV. No significant signal is observed and an upper limit on the branching fraction of $D^+\to e^+\nu_{e}$ is set as $9.7 \times 10^{-7}$, at the 90\% confidence level. Our upper limit is an order of magnitude smaller than the previous limit for this decay mode.
Published: 2025

23. Observation of the $W$-annihilation process $D_s^+ \to \omega\rho^+$ and measurement of $D_s^+ \to \phi\rho^+$ in $D^+_s\to \pi^+\pi^+\pi^-\pi^0\pi^0$ decays

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., 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., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lan, W. N., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, Lei, Li, M. H., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Y. H., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, Bo, Wang, C., Wang, D. Y., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, Lianjie, Wu, X., Wu, X. H., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, W. P., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, R. J., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Y. Z., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, W. J., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: We present the first amplitude analysis and branching fraction measurement of the decay $D^+_s\to \pi^+\pi^+\pi^-\pi^0\pi^0$, using $e^+e^-$ collision data collected with the BESIII detector at center-of-mass energies between 4.128 and 4.226 GeV corresponding to an integrated luminosity of 7.33 fb$^{-1}$, and report the first observation of the pure $W$-annihilation decay $D_s^+ \to \omega\rho^+$ with a branching fraction of $(0.99\pm0.08_{\rm stat}\pm0.07_{\rm syst})\%$. In comparison to the low significance of the $\mathcal{D}$ wave in the decay $D_s^+ \to \phi\rho^+$, the dominance of the $\mathcal{D}$ wave over the $\mathcal{S}$ and $\mathcal{P}$ waves, with a fraction of $(51.85\pm7.28_{\rm stat}\pm7.90_{\rm syst})\%$ observed in the decay, provides crucial information for the``polarization puzzle", as well as for the understanding of charm meson decays. The branching fraction of $D^+_s\to \pi^+\pi^+\pi^-\pi^0\pi^0$ is measured to be $(4.41\pm0.15_{\rm stat}\pm0.13_{\rm syst})\%$. Moreover, the branching fraction of $D_s^+ \to \phi\rho^+$ is measured to be $(3.98\pm0.33_{\rm stat}\pm0.21_{\rm syst})\%$, and the $R_{\phi}= {\mathcal{B}(\phi\to\pi^+\pi^-\pi^0)}/{\mathcal{B}(\phi\to K^+K^-)}$ is determined to be $(0.222\pm0.019_{\rm stat}\pm0.016_{\rm syst}$), which is consistent with the previous measurement based on charm meson decays, but deviates from the results from $e^+e^-$ annihilation and $K$-$N$ scattering experiments by more than 3$\sigma$.
Published: 2025

24. Study of the electromagnetic Dalitz decay $J/\psi \to e^+e^- \pi^0$

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., 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, Z. Y., Choi, S. K., Chu, X., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y., Gao, Y. N., Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Malik, Q. A., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, J. J., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, W. H., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, X. Q., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: We study the electromagnetic Dalitz decay $J/\psi \to e^+e^- \pi^0$ using $(10087 \pm 44) \times 10^6$ $J/\psi$ events collected by the \bes detector. The di-electron-invariant-mass dependent transition form factor of this decay is explored for the first time. A significant resonant structure corresponding to the $\rho/\omega$ resonance is observed, which cannot be described by existing theoretical models, due to contributions from the isospin-conserving $J/\psi \to \rho \pi^0$ and isospin-volating $J/\psi \to \omega \pi^0$ decays. The observed $\rho$--$\omega$ interference is consistent with that of the pion form factor but features a relatively narrow $\rho$ peak. By taking into account the contribution of this resonant structure, the branching fraction of $J/\psi \to e^+e^- \pi^0$ in the full $e^+e^-$ invariant mass spectrum range is also measured for the first time to be $(8.06 \pm 0.31 (\rm{stat}) \pm 0.38 (\rm{syst}))\times 10^{-7}$, which is two times larger than the prediction of the Vector Meson Dominance model due to the observed resonant contribution of $\rho/\omega$ resonances., Comment: 9 pages, 4 figures, Submitted to Phys. Rev. Lett
Published: 2025

25. An exceptional surface and its topology

Author: Yang, Shou-Bang, Han, Pei-Rong, Ning, Wen, Wu, Fan, Yang, Zhen-Biao, and Zheng, Shi-Biao
Subjects: Quantum Physics
Abstract: Non-Hermitian (NH) systems can display exceptional topological defects without Hermitian counterparts, exemplified by exceptional rings in NH two-dimensional systems. However, exceptional topological features associated with higher-dimension topological defects remain unexplored yet. We here investigate the topology for the singularities in an NH three-dimensional system. We find that the three-order singularities in the parameter space form an exceptional surface (ES), on which all the three eigenstates and eigenenergies coalesce. Such an ES corresponds to a two-dimensional extension of a point-like synthetic tensor monopole. We quantify its topology with the Dixmier-Douady invariant, which measures the quantized flux associated with the synthetic tensor field. We further propose an experimentally feasible scheme for engineering such an NH model. Our results pave the way for investigations of exceptional topology associated with topological defects with more than one dimension.
Published: 2025

26. Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing Images

Author: Wang, Yuze, Xiao, Rong, Li, Haifeng, Belgiu, Mariana, and Tao, Chao
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: In remote sensing scene classification, leveraging the transfer methods with well-trained optical models is an efficient way to overcome label scarcity. However, cloud contamination leads to optical information loss and significant impacts on feature distribution, challenging the reliability and stability of transferred target models. Common solutions include cloud removal for optical data or directly using Synthetic aperture radar (SAR) data in the target domain. However, cloud removal requires substantial auxiliary data for support and pre-training, while directly using SAR disregards the unobstructed portions of optical data. This study presents a scene classification transfer method that synergistically combines multi-modality data, which aims to transfer the source domain model trained on cloudfree optical data to the target domain that includes both cloudy optical and SAR data at low cost. Specifically, the framework incorporates two parts: (1) the collaborative transfer strategy, based on knowledge distillation, enables the efficient prior knowledge transfer across heterogeneous data; (2) the information regulation mechanism (IRM) is proposed to address the modality imbalance issue during transfer. It employs auxiliary models to measure the contribution discrepancy of each modality, and automatically balances the information utilization of modalities during the target model learning process at the sample-level. The transfer experiments were conducted on simulated and real cloud datasets, demonstrating the superior performance of the proposed method compared to other solutions in cloud-covered scenarios. We also verified the importance and limitations of IRM, and further discussed and visualized the modality imbalance problem during the model transfer. Codes are available at https://github.com/wangyuze-csu/ESCCS
Published: 2025

27. Elongated particles in flow: Commentary on small angle scattering investigations

Author: Huang, Guan-Rong, Porcar, Lionel, Murphy, Ryan P., Shinohara, Yuya, Wang, Yangyang, Carrillo, Jan-Michael, Sumpter, Bobby G., Tung, Chi-Huan, Do, Changwoo, and Chen, Wei-Ren
Subjects: Condensed Matter - Soft Condensed Matter
Abstract: This work thoroughly examines several analytical tools, each possessing a different level of mathematical intricacy, for the purpose of characterizing the orientation distribution function of elongated objects under flow. Our investigation places an emphasis on connecting the orientation distribution to the small angle scattering spectra measured experimentally. The diverse range of mathematical approaches investigated herein provides insights into the flow behavior of elongated particles from different perspectives and serves as powerful tools for elucidating the complex interplay between flow dynamics and the orientation distribution function.
Published: 2025

28. CFFormer: Cross CNN-Transformer Channel Attention and Spatial Feature Fusion for Improved Segmentation of Low Quality Medical Images

Author: Li, Jiaxuan, Xu, Qing, He, Xiangjian, Liu, Ziyu, Zhang, Daokun, Wang, Ruili, Qu, Rong, and Qiu, Guoping
Subjects: Computer Science - Computer Vision and Pattern Recognition, I.2, I.4, I.5
Abstract: Hybrid CNN-Transformer models are designed to combine the advantages of Convolutional Neural Networks (CNNs) and Transformers to efficiently model both local information and long-range dependencies. However, most research tends to focus on integrating the spatial features of CNNs and Transformers, while overlooking the critical importance of channel features. This is particularly significant for model performance in low-quality medical image segmentation. Effective channel feature extraction can significantly enhance the model's ability to capture contextual information and improve its representation capabilities. To address this issue, we propose a hybrid CNN-Transformer model, CFFormer, and introduce two modules: the Cross Feature Channel Attention (CFCA) module and the X-Spatial Feature Fusion (XFF) module. The model incorporates dual encoders, with the CNN encoder focusing on capturing local features and the Transformer encoder modeling global features. The CFCA module filters and facilitates interactions between the channel features from the two encoders, while the XFF module effectively reduces the significant semantic information differences in spatial features, enabling a smooth and cohesive spatial feature fusion. We evaluate our model across eight datasets covering five modalities to test its generalization capability. Experimental results demonstrate that our model outperforms current state-of-the-art (SOTA) methods, with particularly superior performance on datasets characterized by blurry boundaries and low contrast., Comment: The article consists of 15 pages, including 10 figures and 7 tables. The code will be made open-source once the article is accepted by the journal
Published: 2025

29. TransientVerse: A Comprehensive Real-Time Alert and Multi-Wavelength Analysis System for Transient Astronomical Events

Author: Fang, Jian-Hua, Li, Di, Wang, Pei, Chen, Hua-Xi, Wang, Han, Zhou, Deng-Ke, Bao, Qin-Ping, Li, Hai-Yan, Hu, Jing-Jing, Xie, Jin-Tao, Ge, Xiao-Dong, Feng, Yi, Quan, Dong-Hui, Kang, Zhi-Xuan, Guo, Xue-Rong, Jin, Chen-Wu, Wang, Zhi-Lin, Xu, Jia-Ying, Miao, Chen-Chen, Zhao, Ru-Shuang, and Niu, Chen-Hui
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: Transient astrophysical events are characterized by short timescales, high energy, and multi-wavelength radiation, often accompanied by violent energy releases. These phenomena are a major focus of modern astronomical research. To reveal their underlying physical mechanisms, near-real-time, multi-wavelength, and multi-messenger follow-up observations are essential. However, current transient alert systems face multiple challenges, including fragmented messages, inconsistent formats, and difficulties in retrospective analysis, all of which hinder the efficiency of triggering observations. This paper presents \textbf{TransientVerse}, an innovative real-time database platform to integrate and disseminate transient alerts. The platform uses an automated pipeline to integrate real-time alerts from multiple sources (e.g., ATel, VOEvent, and GCN). It structures unstructured text data into a dual-format database for transient alerts by using open-source large language models. TransientVerse offers retrospective searches, data visualization, literature reviews, and customized subscriptions for efficient event tracking and analysis. Additionally, for Fast Radio Bursts (FRBs), the platform provides real-time statistics on repeat burst rates across different time intervals and alerts astronomers about high-frequency burst sources, enabling rapid follow-up observations and optimizing the use of limited observation windows. TransientVerse improves the efficiency of acquiring transient events in real time, lowers the technical barriers for simultaneous observations, and provides robust technical support for multi-wavelength, multi-messenger time-domain astronomy and astrophysics studies.
Published: 2025

30. The S-PLUS Ultra-Short Survey: Photometric Re-calibration with the BEst STar Database

Author: Li, Xiaolu, Xiao, Kai, Huang, Yang, Yuan, Haibo, Tang, Yanke, Beers, Timothy C., Huang, Bowen, Ma, Mingyang, Humire, Pedro K., Alvarez-Candal, Alvaro, Sestito, Federico, Gai, Ning, Mao, Yongna, Gu, Hongrui, Tao, Zhenzhao, Yang, Lin, Xu, Shuai, and Hu, Rong
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract: We present an independent validation and comprehensive re-calibration of S-PLUS Ultra-Short Survey (USS) DR1 12-band photometry using about 30,000--70,000 standard stars from the BEst STar (BEST) database. We identify spatial variation of zero-point offsets, up to 30--40\,mmag for blue filters ($u$, $J0378$, $J0395$) and 10\,mmag for others, predominantly due to the higher uncertainties of the technique employed in the original USS calibration. Moreover, we detect large- and medium-scale CCD position-dependent systematic errors, up to 50\,mmag, primarily caused by different aperture and flat-field corrections. We then re-calibrate the USS DR1 photometry by correcting the systematic shifts for each tile using second-order two-dimensional polynomial fitting combined with a numerical stellar flat-field correction method. The re-calibrated results from the XPSP and the SCR standards are consistent within 6\,mmag in the USS zero-points, demonstrating both the typical precision of re-calibrated USS photometry and a sixfold improvement in USS zero-point precision. Further validation using SDSS and Pan-STARRS1, as well as LAMOST DR10 and Gaia photometry, also confirms this precision for the re-calibrated USS photometry. Our results clearly demonstrate the capability and the efficiency of the BEST database in improving calibration precision to the milli-magnitude level for wide-field photometric surveys. The re-calibrated USS DR1 photometry is publicly available: doi: 10.12149/101503 (https://nadc.china-vo.org/res/r101504/)., Comment: 28 pages, 25 figures, ApJS accepted, see main results in Figures 4 and 7
Published: 2025

31. Convergence of a particle method for gradient flows on the $L^p$-Wasserstein space

Author: Lei, Rong
Subjects: Mathematics - Numerical Analysis, Mathematics - Optimization and Control
Abstract: We study the particle method to approximate the gradient flow on the $L^p$-Wasserstein space. This method relies on the discretization of the energy introduced by [3] via nonoverlapping balls centered at the particles and preserves the gradient flow structure at the particle level. We prove the convergence of the discrete gradient flow to the continuum gradient flow on the $L^p$-Wasserstein space over $\mathbb R$, specifically to the doubly nonlinear diffusion equation in one dimension., Comment: arXiv admin note: text overlap with arXiv:1605.08086 by other authors
Published: 2025

32. Probing the Merger Rates of Supermassive Black Holes and Galaxies with Gravitational Waves

Author: Fang, Yun and Cai, Rong-Gen
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The mergers of galaxies and supermassive black holes (SMBHs) are key drivers of galaxy evolution, contributing to the growth of both galaxies and their central black holes. Current projects like Pulsar Timing Arrays (PTAs) and upcoming missions such as the Laser Interferometer Space Antenna (LISA), Taiji, and Tianqin are designed to detect gravitational waves (GWs) emitted by SMBH binaries during their inspiral and merger phases. We investigate the capability to probe the merger rates of SMBHs and their host galaxies by combining current PTA detections and mock GW data for LISA-like detectors, while incorporating observational constraints from the $M_{\bullet}-M_*$ relationship and galaxy stellar mass functions. Our findings highlight the critical role of GW detections with LISA-like detectors in exploring the merger rates of galaxies and SMBHs and the timescale of SMBH mergers. Additionally, incorporating PTA constraints on the stochastic gravitational wave background further refines model parameters and reduces uncertainties. Gravitational wave detections offer an independent method for estimating galaxy merger rates, providing a valuable consistency check against rates derived from galaxy pair observations and cosmological simulations. Furthermore, comparing SMBH mass assembly through mergers with growth via accretion provides key insights into the evolutionary history of SMBHs, with the timescale of SMBH binary mergers playing a significant role in shaping their merger rates and merger mass assembly., Comment: 10 pages, 8 figures
Published: 2025

33. OpenGU: A Comprehensive Benchmark for Graph Unlearning

Author: Fan, Bowen, Ai, Yuming, Li, Xunkai, Guo, Zhilin, Li, Rong-Hua, and Wang, Guoren
Subjects: Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract: Graph Machine Learning is essential for understanding and analyzing relational data. However, privacy-sensitive applications demand the ability to efficiently remove sensitive information from trained graph neural networks (GNNs), avoiding the unnecessary time and space overhead caused by retraining models from scratch. To address this issue, Graph Unlearning (GU) has emerged as a critical solution, with the potential to support dynamic graph updates in data management systems and enable scalable unlearning in distributed data systems while ensuring privacy compliance. Unlike machine unlearning in computer vision or other fields, GU faces unique difficulties due to the non-Euclidean nature of graph data and the recursive message-passing mechanism of GNNs. Additionally, the diversity of downstream tasks and the complexity of unlearning requests further amplify these challenges. Despite the proliferation of diverse GU strategies, the absence of a benchmark providing fair comparisons for GU, and the limited flexibility in combining downstream tasks and unlearning requests, have yielded inconsistencies in evaluations, hindering the development of this domain. To fill this gap, we present OpenGU, the first GU benchmark, where 16 SOTA GU algorithms and 37 multi-domain datasets are integrated, enabling various downstream tasks with 13 GNN backbones when responding to flexible unlearning requests. Based on this unified benchmark framework, we are able to provide a comprehensive and fair evaluation for GU. Through extensive experimentation, we have drawn $8$ crucial conclusions about existing GU methods, while also gaining valuable insights into their limitations, shedding light on potential avenues for future research., Comment: under review
Published: 2025

34. The Influence of General Relativity on the Spins of Celestial Bodies in Inclined Orbits

Author: Yuan, Huan-rong, Wang, Ying, Wu, Xin, Xie, Ji-wei, Liu, Hui-gen, Zhou, Ji-lin, and Sun, Wei
Subjects: Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Solar and Stellar Astrophysics, General Relativity and Quantum Cosmology, 83-10, 85-10, 70F15, J.2.3
Abstract: Through the Rossiter-McLaughlin effect, several hot Jupiters have been found to exhibit spin-orbit misalignment, and even retrograde orbits. The high obliquity observed in these planets can be attributed to two primary formation mechanisms, as summarized in the existing literature. First, the host star's spin becomes misaligned with the planetary disk during the late stages of star formation, primarily due to chaotic accretion and magnetic interactions between the star and the planetary disk. Second, the orbital inclination of an individual planet can be excited by dynamical processes such as planet-planet scattering, the Lidov-Kozai cycle, and secular chaos within the framework of Newtonian mechanics. This study introduces a third mechanism, where, within the framework of general relativity, the post-Newtonian spin-orbit coupling term induces precession of the host star's spin around the orbital angular momentum. The orbital inclination, relative to a reference plane, can expand the range of deviation in the spatial orientation of the bodies' spins from the plane's normal. The varying amplitude and period of spin precession for both the star and the planet are derived theoretically, and the results, which can be applied without restriction, agree well with numerical simulations., Comment: 20 pages 17 figures, ApJ accepted
Published: 2025
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35. Observation of $\psi(3686) \to K^{-}\Lambda(1520)\bar{\Xi}^{+} + c.c.$

Author: BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., 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., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lan, W. N., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, Lei, Li, M. H., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Y. H., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, Bo, Wang, C., Wang, D. Y., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, Lianjie, Wu, X., Wu, X. H., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, W. P., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, R. J., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Y. Z., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, W. J., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: Based on $(2712.4 \pm 14.3)\times 10^6$ $\psi(3686)$ events collected at the BESIII detector operating at the BEPCII collider, we present the first observation of the decay $\psi(3686) \to K^{-}\Lambda(1520)\bar{\Xi}^{+} + c.c.$. The product branching fraction ${\cal B}[\psi(3686) \to K^{-}\Lambda(1520)\bar{\Xi}^{+} + c.c.] \times {\cal B}[\Lambda(1520) \to pK^{-}]$ is measured to be $(9.5 \pm 0.8 \pm 1.1) \times 10^{-7}$, where the first uncertainty is statistical and the second systematic.
Published: 2025

36. Mapping Transient Structures of Cyclo[18]Carbon by Computational X-Ray Spectra

Author: Wei, Minrui, Wang, Sheng-Yu, Zhang, Jun-Rong, Zhang, Lu, Ge, Guoyan, Liu, Zeyu, and Hua, Weijie
Subjects: Physics - Chemical Physics, Physics - Atomic and Molecular Clusters, Physics - Computational Physics, Physics - Optics
Abstract: The structure of cyclo[18]carbon (C$_{18}$), whether in its polyynic form with bond length alternation (BLA) or its cumulenic form without BLA, has long fascinated researchers, even prior to its successful synthesis. Recent studies suggest a polyynic ground state and a cumulenic transient state; however, the dynamics remain unclear and lack experimental validation. This study presents a first-principles theoretical investigation of the bond lengths ($R_1$ and $R_2$) dependent two-dimensional potential energy surfaces (PESs) of C$_{18}$, concentrating on the ground state and carbon 1s ionized and excited states. We examine the potential of X-ray spectra for determining bond lengths and monitoring transient structures, finding that both X-ray photoelectron (XPS) and absorption (XAS) spectra are sensitive to these variations. Utilizing a library of ground-state minimum structures optimized with 14 different functionals, we observe that core binding energies predicted with the $\omega$B97XD functional can vary by 0.9 eV (290.3--291.2 eV). Unlike the ground state PES, which predicts minima at alternating bond lengths, the C1s ionized state PES predicts minima with equivalent bond lengths. In the XAS spectra, peaks 1$\pi^*$ and 2$\pi^*$ show a redshift with increasing bond lengths along the line where $R_1 = R_2$. Additionally, increasing $R_2$ (with $R_1$ fixed) results in an initial redshift followed by a blueshift, minimizing at $R_1 = R_2$. Major peaks indicate that both 1$\pi^*$ and 2$\pi^*$ arise from two channels: C1s$\rightarrow\pi^*_{z}$ (out-of-plane) and C1s$\rightarrow\pi^*_{xy}$ (in-plane) transitions at coinciding energies., Comment: 3 figures
Published: 2025

37. The pseudospectrum for the Kerr black hole: spin $s=0$ case

Author: Cai, Rong-Gen, Cao, Li-Ming, Chen, Jia-Ning, Guo, Zong-Kuan, Wu, Liang-Bi, and Zhou, Yu-Sen
Subjects: General Relativity and Quantum Cosmology
Abstract: We investigate the pseudospectrum of the Kerr black hole, which indicates the instability of the spectrum of QNMs of the Kerr black hole. Methodologically, we use the hyperboloidal framework to cast the quasinormal mode (QNM) problem into a two-dimensional eigenvalue problem associated with a non-self-adjoint operator, and then the spectrum and pseudospectrum are solved by imposing the two-dimensional Chebyshev collocation method. The (energy) norm is constructed by using the conserved current method for the spin $s=0$ case. In order to study the convergence issues, an extra $H^k$-norm term is added into the original energy norm. For the finite rank approximation of the operator, it is found that as $k$ increases, the pseudospectrum becomes sharp when the complex number $\omega$ is close to the QNM spectra. In contrast, when $\omega$ moves away from the QNM spectra, the pseudospectrum becomes mild. Furthermore, we discussed the convergence of pseudospectra with different $H^k$-norms. As $k$ increases, the convergence of the pseudospectrum improves. However, at the same time, an increase in the imaginary part of $\omega$ can deteriorate the convergence of the pseudospectrum under the condition of the same norms., Comment: 17 pages, 6 figures
Published: 2025

38. How to determine nucleon polarization at existing collider experiments?

Author: Liang, Yu-Tie, Lv, Xiao-Rong, Kupsc, Andrzej, Gou, Boxing, and Li, Hai-Bo
Subjects: High Energy Physics - Phenomenology, High Energy Physics - Experiment
Abstract: We propose a novel approach to measure spin polarization of nucleons produced in electron--positron collisions. Using existing tracking devices and supporting structure material, general-purpose spectrometers can be utilized as a large-acceptance polarimeter without hardware upgrade. With the proposed approach, the spin polarization of nucleons can be revealed, providing a complementary and accurate description of the final-state particles. This could have far-reaching implications, such as enabling the complete determination of the time-like electromagnetic form factors of nucleons., Comment: 5 pages, 5 figures
Published: 2025

39. Traversable Wormhole in AdS and Entanglement

Author: Liu, Hua-Chao and Miao, Rong-Xin
Subjects: High Energy Physics - Theory, General Relativity and Quantum Cosmology
Abstract: A traversable wormhole generally violates the averaged null energy condition, usually requiring exotic matter. Recently, it has been found that the traversable wormhole can be realized by non-exotic matter in Einstein-Dirac-Maxwell theories in flat space. This paper generalizes discussions to the AdS spacetime and finds traversable wormholes with spherical and planar topologies. Furthermore, based on the AdS/CFT correspondence, we compute the entanglement entropy of strips and disks on two AdS boundaries of the wormhole. We find that entanglement entropy undergoes a phase transition as the subsystem size increases., Comment: 22 pages, 16 figures
Published: 2025

40. Progress of the anti-obesity of Berberine

Author: Yue, Kong, haokun, Yang, Rong, Nie, Xuxiang, Zhang, Hongtao, Zhang, and Xin, Nian
Subjects: Quantitative Biology - Biomolecules
Abstract: Obesity is defined as the excessive accumulation or abnormal distribution of body fat. According to data from World Obesity Atlas 2024, the increase in prevalence of obesity has become a major worldwide health problem in adults as well as among children and adolescents. Although an increasing number of drugs have been approved for the treatment of obesity in recent years, many of these drugs have inevitable side effects which have increased the demand for new safe, accessible and effective drugs for obesity and prompt interest in natural products. Berberine (BBR) and its metabolites, known for their multiple pharmacological effects. Recent studies have emphatically highlighted the anti-obesity benefits of BBR and the underlying mechanisms have been gradually elucidated. However, its clinical application is limited by poor oral absorption and low bioavailability. Based on this, this review summarizes current research on the anti-obesity effects of BBR and its metabolites, including advancements in clinical trail results, understanding potential molecular mechanisms and absorption and bioavailability. As a natural compound derived from plants, BBR holds potential as an alternative approach for managing obesity., Comment: 54 pages,9 figures
Published: 2025

41. Search for $\eta_c(2S)\to p\bar{p}K^+K^-$ and measurement of $\chi_{cJ}\to p\bar{p}K^+K^-$ in $\psi(3686)$ radiative decays

Author: Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., 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., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., H"olzken, F., H"usken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., K"uhn, W., Lan, W. N., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, Lei, Li, M. H., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Y. H., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, Bo, Wang, C., Wang, D. Y., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, Lianjie, Wu, X., Wu, X. H., Wu, Y. H., Wu, Y. 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Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
Subjects: High Energy Physics - Experiment
Abstract: A search for $\eta_c(2S)\to p\bar{p}K^+K^-$, together with measurement of branching fractions of $\chi_{cJ(J=0,1,2)}\to p\bar{p}K^+K^-$ in the $\psi(3686) \to \gamma \eta_c(2S)$ and the $\psi(3686) \to \gamma \chi_{cJ}$ radiative decays, is performed with $(2712.4\pm14.3)\times 10^6$ $\psi(3686)$ events collected with the BESIII detector at the BEPCII collider. An evidence for $\eta_c(2S)\to p\bar{p}K^+K^-$ is found, with a significance of $3.3\sigma$. The product branching fraction of $\mathcal{B}[\psi(3686)\to\gamma\eta_c(2S)]\cdot\mathcal{B}[\eta_c(2S)\to p\bar{p}K^+K^-]$ is determined to be $(1.98\mkern 2mu\pm\mkern 2mu0.41_{\text{stat.}}\mkern 2mu\pm\mkern 2mu0.99_{\text{syst.}})\times 10^{-7}$. The product branching fractions of $\mathcal{B}[\psi(3686)\to\gamma\chi_{cJ}]\cdot\mathcal{B}[\chi_{cJ}\to p\bar{p}K^+K^-]$ are measured to be $(2.49\mkern 2mu\pm\mkern 2mu 0.03_{\text{stat.}}\mkern 2mu\pm\mkern 2mu 0.15_{\text{syst.}})\times 10^{-5}$, $(1.83\mkern 2mu \pm\mkern 2mu 0.02_{\text{stat.}}\mkern 2mu \pm\mkern 2mu 0.11_{\text{syst.}})\times 10^{-5}$, and $(2.43\mkern 2mu\pm\mkern 2mu 0.02_{\text{stat.}}\mkern 2mu\pm\mkern 2mu 0.15_{\text{syst.}})\times 10^{-5}$, for $J=0,\ 1$, and 2, respectively., Comment: 12 pages, 2 figures
Published: 2025

42. Inference in matrix-valued time series with common stochastic trends and multifactor error structure

Author: Chen, Rong, Giannerini, Simone, Goracci, Greta, and Trapani, Lorenzo
Subjects: Statistics - Methodology
Abstract: We develop an estimation methodology for a factor model for high-dimensional matrix-valued time series, where common stochastic trends and common stationary factors can be present. We study, in particular, the estimation of (row and column) loading spaces, of the common stochastic trends and of the common stationary factors, and the row and column ranks thereof. In a set of (negative) preliminary results, we show that a projection-based technique fails to improve the rates of convergence compared to a "flattened" estimation technique which does not take into account the matrix nature of the data. Hence, we develop a three-step algorithm where: (i) we first project the data onto the orthogonal complement to the (row and column) loadings of the common stochastic trends; (ii) we subsequently use such "trend free" data to estimate the stationary common component; (iii) we remove the estimated common stationary component from the data, and re-estimate, using a projection-based estimator, the row and column common stochastic trends and their loadings. We show that this estimator succeeds in refining the rates of convergence of the initial, "flattened" estimator. As a by-product, we develop consistent eigenvalue-ratio based estimators for the number of stationary and nonstationary common factors.
Published: 2025

43. Virgo: A Preliminary Exploration on Reproducing o1-like MLLM

Author: Du, Yifan, Liu, Zikang, Li, Yifan, Zhao, Wayne Xin, Huo, Yuqi, Wang, Bingning, Chen, Weipeng, Liu, Zheng, Wang, Zhongyuan, and Wen, Ji-Rong
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract: Recently, slow-thinking reasoning systems, built upon large language models (LLMs), have garnered widespread attention by scaling the thinking time during inference. There is also growing interest in adapting this capability to multimodal large language models (MLLMs). Given that MLLMs handle more complex data semantics across different modalities, it is intuitively more challenging to implement multimodal slow-thinking systems. To address this issue, in this paper, we explore a straightforward approach by fine-tuning a capable MLLM with a small amount of textual long-form thought data, resulting in a multimodal slow-thinking system, Virgo (Visual reasoning with long thought). We find that these long-form reasoning processes, expressed in natural language, can be effectively transferred to MLLMs. Moreover, it seems that such textual reasoning data can be even more effective than visual reasoning data in eliciting the slow-thinking capacities of MLLMs. While this work is preliminary, it demonstrates that slow-thinking capacities are fundamentally associated with the language model component, which can be transferred across modalities or domains. This finding can be leveraged to guide the development of more powerful slow-thinking reasoning systems. We release our resources at https://github.com/RUCAIBox/Virgo., Comment: Technical Report on Slow Thinking with LLMs: Visual Reasoning
Published: 2025

44. Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking

Author: Cheng, Xiaoxue, Li, Junyi, Zhao, Wayne Xin, and Wen, Ji-Rong
Subjects: Computer Science - Computation and Language
Abstract: Large language models (LLMs) demonstrate exceptional capabilities, yet still face the hallucination issue. Typical text generation approaches adopt an auto-regressive generation without deliberate reasoning, which often results in untrustworthy and factually inaccurate responses. In this paper, we propose HaluSearch, a novel framework that incorporates tree search-based algorithms (e.g. MCTS) to enable an explicit slow thinking generation process for mitigating hallucinations of LLMs during inference. Specifically, HaluSearch frames text generation as a step-by-step reasoning process, using a self-evaluation reward model to score each generation step and guide the tree search towards the most reliable generation pathway for fully exploiting the internal knowledge of LLMs. To balance efficiency and quality, we introduce a hierarchical thinking system switch mechanism inspired by the dual process theory in cognitive science, which dynamically alternates between fast and slow thinking modes at both the instance and step levels, adapting to the complexity of questions and reasoning states. We conduct extensive experiments on both English and Chinese datasets and the results show that our approach significantly outperforms baseline approaches.
Published: 2025

45. Leverage Cross-Attention for End-to-End Open-Vocabulary Panoptic Reconstruction

Author: Yu, Xuan, Xie, Yuxuan, Liu, Yili, Lu, Haojian, Xiong, Rong, Liao, Yiyi, and Wang, Yue
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Robotics
Abstract: Open-vocabulary panoptic reconstruction offers comprehensive scene understanding, enabling advances in embodied robotics and photorealistic simulation. In this paper, we propose PanopticRecon++, an end-to-end method that formulates panoptic reconstruction through a novel cross-attention perspective. This perspective models the relationship between 3D instances (as queries) and the scene's 3D embedding field (as keys) through their attention map. Unlike existing methods that separate the optimization of queries and keys or overlook spatial proximity, PanopticRecon++ introduces learnable 3D Gaussians as instance queries. This formulation injects 3D spatial priors to preserve proximity while maintaining end-to-end optimizability. Moreover, this query formulation facilitates the alignment of 2D open-vocabulary instance IDs across frames by leveraging optimal linear assignment with instance masks rendered from the queries. Additionally, we ensure semantic-instance segmentation consistency by fusing query-based instance segmentation probabilities with semantic probabilities in a novel panoptic head supervised by a panoptic loss. During training, the number of instance query tokens dynamically adapts to match the number of objects. PanopticRecon++ shows competitive performance in terms of 3D and 2D segmentation and reconstruction performance on both simulation and real-world datasets, and demonstrates a user case as a robot simulator. Our project website is at: https://yuxuan1206.github.io/panopticrecon_pp/, Comment: 18 pages, 10 figures
Published: 2025

46. MSC-Bench: Benchmarking and Analyzing Multi-Sensor Corruption for Driving Perception

Author: Hao, Xiaoshuai, Liu, Guanqun, Zhao, Yuting, Ji, Yuheng, Wei, Mengchuan, Zhao, Haimei, Kong, Lingdong, Yin, Rong, and Liu, Yu
Subjects: Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
Abstract: Multi-sensor fusion models play a crucial role in autonomous driving perception, particularly in tasks like 3D object detection and HD map construction. These models provide essential and comprehensive static environmental information for autonomous driving systems. While camera-LiDAR fusion methods have shown promising results by integrating data from both modalities, they often depend on complete sensor inputs. This reliance can lead to low robustness and potential failures when sensors are corrupted or missing, raising significant safety concerns. To tackle this challenge, we introduce the Multi-Sensor Corruption Benchmark (MSC-Bench), the first comprehensive benchmark aimed at evaluating the robustness of multi-sensor autonomous driving perception models against various sensor corruptions. Our benchmark includes 16 combinations of corruption types that disrupt both camera and LiDAR inputs, either individually or concurrently. Extensive evaluations of six 3D object detection models and four HD map construction models reveal substantial performance degradation under adverse weather conditions and sensor failures, underscoring critical safety issues. The benchmark toolkit and affiliated code and model checkpoints have been made publicly accessible.
Published: 2025

47. Cavity Plasmon: Enhanced Luminescence Effect on InGaN Light Emitting Diodes

Author: Li, Yuyin, Zhou, Jing, Yan, Ziwen, Zhang, Xianfei, Xie, Zili, Xiu, Xiangqian, Chen, Dunjun, Liu, Bin, Zhao, Hong, Shi, Yi, Zhang, Rong, Zheng, Youdou, and Chen, Peng
Subjects: Physics - Optics, Physics - Applied Physics
Abstract: We fabricated polygonal nanoholes in the top p-GaN layer of the InGaN/GaN light-emitting diode, followed by the deposition of Au/Al metal thin film within the nanoholes to create metal microcavities, thereby constructing the surface plasmon structure. The findings indicate that with increased current injection, the light output of the LEDs rose by 46%, accompanied by a shift of the gain peak position towards the plasmon resonance energy. The maximum enhancement factor increases to 2.38 as the coupling distance decreases from 60 nm to 30 nm. Interestingly, time-resolved photoluminescence data showed that the spontaneous emission decay time lengthened due to the plasmon coupling, suggesting the presence of a new plasmon coupling mechanism. Finite-Difference Time-Domain simulation results show that the electric field is localized at certain locations around the metal microcavity, generating a new type of shape-sensitive plasmon, named Cavity Plasmon here. This intense localization leads to a longer lifetime and enhances the recombination efficiency of excitons. We discuss several unique properties of the cavity plasmon generated by the polygonal metal microcavity with several specific angular shapes. The results demonstrate that the cavity plasmon generated by the polygonal metal microcavity is a highly promising technique for enhancing the light emission performance of of relevant semiconductor optoelectronic devices., Comment: 12 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:2104.00849
Published: 2024

48. Enhanced optical performance of GaN Micro-light-emitting diodes with a single porous layer

Author: Yan, Ziwen, Zhang, Xianfei, Li, Yuyin, Xie, Zili, Xiu, Xiangqian, Chen, Dunjun, Han, Ping, Shi, Yi, Zhang, Rong, Zheng, Youdou, and Chen, Peng
Subjects: Physics - Optics, Physics - Applied Physics
Abstract: High-efficiency micro-light-emitting diodes (Micro-LEDs) are key devices for next-generation display technology. However, when the mesa size is reduced to around tens of micrometers or less, the luminous efficiency is constrained by the "efficiency-on-size effect". This work details the fabrication of gallium nitride (GaN) based Micro-LEDs with various mesa shapes and a single porous layer under the active region. A modified green LED epitaxial structure with different doped n-GaN layers combined with electrochemical etching created the porous layer. The strong light confinement achieved by the porous layer and the polygonal mesa greatly enhances spontaneous emission. The luminous intensity of the Micro-LEDs with the porous layer is approximately 22 times greater than those Micro-LEDs without the porous layer. A significant reduction in minimum full width at half maximum (FWHM) was observed in polygonal devices, suggesting a change in the luminescence mechanism. The influence of varying device geometry on emission performance was investigated. Experimental results reveal that, unlike circular porous Micro-LEDs, square and hexagonal porous Micro-LEDs exhibit more pronounced resonant emission, which provides a new technological approach for the further development of high-performance Micro-LEDs and lasers., Comment: 11 pages, 5 figures
Published: 2024

49. Study on the efficiency droop in high-quality GaN material under high photoexcitation intensity

Author: Chen, Peng, Xie, Zili, Xiu, Xiangqian, Chen, Dunjun, Liu, Bin, Zhao, Hong, Shi, Yi, Zhang, Rong, and Zheng, Youdou
Subjects: Physics - Optics
Abstract: III-V nitride semiconductors, represented by GaN, have attracted significant research attention. Driven by the growing interest in smart micro-displays, there is a strong desire to achieve enhanced light output from even smaller light-emitting diode (LED) chips. However, the most perplexing phenomenon and the most significant challenge in the study of emission properties under high-injection conditions in GaN has always been efficiency droop for decades, where LEDs exhibit a substantial loss in efficiency at high driving currents. In this paper, we present our study on the intrinsic emission properties of high-quality GaN material based on the density of states and the principles of momentum conservation. Our theoretical calculations reveal a momentum distribution mismatch between the non-equilibrium excess electrons and holes, which becomes more significant as the carrier concentration increases. Our excitation-dependent photoluminescence measurements conducted at 6 K exhibited a clear droop for all exciton recombinations, but droop-free for phonon-assisted recombination due to phonons compensating for the momentum mismatch. These findings indicate that the momentum distribution mismatch between the non-equilibrium excess electrons and holes is one of the intrinsic causes of the efficiency droop, which originates from the intrinsic band properties of GaN. These results suggest that proper active region design aimed at reducing this mismatch will contribute to the development of ultra-highly efficient lighting devices in the future., Comment: 10 pages, 9 figures
Published: 2024

50. Orbital-selective electron correlations in high-$T_{\rm c}$ bilayer nickelates: from a global phase diagram to implications for spectroscopy

Author: Liao, Zhiguang, Wang, Yiming, Chen, Lei, Duan, Guijing, Yu, Rong, and Si, Qimiao
Subjects: Condensed Matter - Superconductivity, Condensed Matter - Strongly Correlated Electrons
Abstract: Motivated by the high temperature superconductivity observed in the bilayer nickelate La$_3$Ni$_2$O$_7$ and the spectroscopic evidences of strong electron correlations in this compound, we address the role of its multiorbital electron correlations by proposing a global phase diagram of a bilayer two-orbital Hubbard model. We find a Mott transition developing at half filling, and identify strong orbital selectivity when the system is at the physical electron count. The orbital selectivity is manifested in the formation of interlayer spin singlets between electrons in the $z^2$ orbitals. These features lead to a strong renormalization of the electronic band structure while sustaining a sizable splitting between the bonding and antibonding $z^2$ bands. The proposed orbital-selective correlations naturally explain a series of features as observed in the angular resolved photoemission spectroscopy (ARPES) and optical conductivity measurements in La$_3$Ni$_2$O$_7$. Our results provide a basis to understand both the normal state and the high temperature superconductivity of multilayer nickelates and thereby elucidate correlated superconductivity in general., Comment: 6+5 pages, 4+5 figures
Published: 2024

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