论文标题

AI辅助优化电子离子对撞机的ECCE跟踪系统

AI-assisted Optimization of the ECCE Tracking System at the Electron Ion Collider

论文作者

Fanelli, C., Papandreou, Z., Suresh, K., Adkins, J. K., Akiba, Y., Albataineh, A., Amaryan, M., Arsene, I. C., Gayoso, C. Ayerbe, Bae, J., Bai, X., Baker, M. D., Bashkanov, M., Bellwied, R., Benmokhtar, F., Berdnikov, V., Bernauer, J. C., Bock, F., Boeglin, W., Borysova, M., Brash, E., Brindza, P., Briscoe, W. J., Brooks, M., Bueltmann, S., Bukhari, M. H. S., Bylinkin, A., Capobianco, R., Chang, W. -C., Cheon, Y., Chen, K., Chen, K. -F., Cheng, K. -Y., Chiu, M., Chujo, T., Citron, Z., Cline, E., Cohen, E., Cormier, T., Morales, Y. Corrales, Cotton, C., Crafts, J., Crawford, C., Creekmore, S., Cuevas, C., Cunningham, J., David, G., Dean, C. T., Demarteau, M., Diehl, S., Doshita, N., Dupre, R., Durham, J. M., Dzhygadlo, R., Ehlers, R., Fassi, L. El, Emmert, A., Ent, R., Fatemi, R., Fegan, S., Finger, M., Finger Jr., M., Frantz, J., Friedman, M., Friscic, I., Gangadharan, D., Gardner, S., Gates, K., Geurts, F., Gilman, R., Glazier, D., Glimos, E., Goto, Y., Grau, N., Greene, S. V., Guo, A. Q., Guo, L., Ha, S. K., Haggerty, J., Hayward, T., He, X., Hen, O., Higinbotham, D. W., Hoballah, M., Horn, T., Hoghmrtsyan, A., Hsu, P. -h. J., Huang, J., Huber, G., Hutson, A., Hwang, K. Y., Hyde, C., Inaba, M., Iwata, T., Jo, H. S., Joo, K., Kalantarians, N., Kalicy, G., Kawade, K., Kay, S. J. D., Kim, A., Kim, B., Kim, C., Kim, M., Kim, Y., Kim, Y., Kistenev, E., Klimenko, V., Ko, S. H., Korover, I., Korsch, W., Krintiras, G., Kuhn, S., Kuo, C. -M., Kutz, T., Lajoie, J., Lawrence, D., Lebedev, S., Lee, H., Lee, J. S. H., Lee, S. W., Lee, Y. -J., Li, W., Li, W. B., Li, X., Li, X., Li, X., Li, X., Liang, Y. T., Lim, S., Lin, C. -h., Lin, D. X., Liu, K., Liu, M. X., Livingston, K., Liyanage, N., Llope, W. J., Loizides, C., Long, E., Lu, R. -S., Lu, Z., Lynch, W., Marchand, D., Marcisovsky, M., Markowitz, P., Marukyan, H., McGaughey, P., Mihovilovic, M., Milner, R. G., Milov, A., Miyachi, Y., Mkrtchyan, A., Monaghan, P., Montgomery, R., Morrison, D., Movsisyan, A., Mkrtchyan, H., Mkrtchyan, A., Camacho, C. Munoz, Murray, M., Nagai, K., Nagle, J., Nakagawa, I., Nattrass, C., Nguyen, D., Niccolai, S., Nouicer, R., Nukazuka, G., Nycz, M., Okorokov, V. A., Oresic, S., Osborn, J. D., O'Shaughnessy, C., Paganis, S., Pate, S. F., Patel, M., Paus, C., Penman, G., Perdekamp, M. G., Perepelitsa, D. V., da Costa, H. Periera, Peters, K., Phelps, W., Piasetzky, E., Pinkenburg, C., Prochazka, I., Protzman, T., Purschke, M. L., Putschke, J., Pybus, J. R., Rajput-Ghoshal, R., Rasson, J., Raue, B., Read, K. F., Roed, K., Reed, R., Reinhold, J., Renner, E. L., Richards, J., Riedl, C., Rinn, T., Roche, J., Roland, G. M., Ron, G., Rosati, M., Royon, C., Ryu, J., Salur, S., Santiesteban, N., Santos, R., Sarsour, M., Schambach, J., Schmidt, A., Schmidt, N., Schwarz, C., Schwiening, J., Seidl, R., Sickles, A., Simmerling, P., Sirca, S., Sharma, D., Shi, Z., Shibata, T. -A., Shih, C. -W., Shimizu, S., Shrestha, U., Slifer, K., Smith, K., Sokhan, D., Soltz, R., Sondheim, W., Song, J., Song, J., Strakovsky, I. I., Steinberg, P., Stepanov, P., Stevens, J., Strube, J., Sun, P., Sun, X., Tadevosyan, V., Tang, W. -C., Araya, S. Tapia, Tarafdar, S., Teodorescu, L., Timmins, A., Tomasek, L., Trotta, N., Trotta, R., Tveter, T. S., Umaka, E., Usman, A., van Hecke, H. W., Van Hulse, C., Velkovska, J., Voutier, E., Wang, P. K., Wang, Q., Wang, Y., Wang, Y., Watts, D. P., Wickramaarachchi, N., Weinstein, L., Williams, M., Wong, C. -P., Wood, L., Wood, M. H., Woody, C., Wyslouch, B., Xiao, Z., Yamazaki, Y., Yang, Y., Ye, Z., Yoo, H. D., Yurov, M., Zachariou, N., Zajc, W. A., Zha, W., Zhang, J., Zhang, Y., Zhao, Y. X., Zheng, X., Zhuang, P.

论文摘要

电子离子对撞机(EIC)是一种尖端的加速器设施,它将研究“胶水”的性质,该设施结合了宇宙中可见物质的构件。拟议的实验将在大约10年内在布鲁克黑文国家实验室实现,目前正在进行探测器设计和研发。值得注意的是,EIC是最早从设计和研发阶段开始的人工智能(AI)的大规模设施之一。 EIC综合染色体动力学实验(ECCE)是一个财团,提出了基于1.5T螺线管的检测器设计。 EIC检测器提案审查得出的结论是,ECCE设计将作为EIC检测器的参考设计。在此,我们使用AI描述了ECCE跟踪器的全面优化。该工作需要对模拟检测器系统进行复杂的参数化。我们的方法在多维设计空间中处理了一个优化问题,该空间由多个目标驱动的,这些目标编码检测器性能,同时满足了几个机械约束。我们描述了我们的策略并显示了ECCE跟踪系统获得的结果。 AI辅助设计对模拟框架不可知,可以扩展到其他子检测器或子检测器系统,以进一步优化EIC检测器的性能。

The Electron-Ion Collider (EIC) is a cutting-edge accelerator facility that will study the nature of the "glue" that binds the building blocks of the visible matter in the universe. The proposed experiment will be realized at Brookhaven National Laboratory in approximately 10 years from now, with detector design and R&D currently ongoing. Notably, EIC is one of the first large-scale facilities to leverage Artificial Intelligence (AI) already starting from the design and R&D phases. The EIC Comprehensive Chromodynamics Experiment (ECCE) is a consortium that proposed a detector design based on a 1.5T solenoid. The EIC detector proposal review concluded that the ECCE design will serve as the reference design for an EIC detector. Herein we describe a comprehensive optimization of the ECCE tracker using AI. The work required a complex parametrization of the simulated detector system. Our approach dealt with an optimization problem in a multidimensional design space driven by multiple objectives that encode the detector performance, while satisfying several mechanical constraints. We describe our strategy and show results obtained for the ECCE tracking system. The AI-assisted design is agnostic to the simulation framework and can be extended to other sub-detectors or to a system of sub-detectors to further optimize the performance of the EIC detector.

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