Scientific computing plan for the ECCE detector at the Electron Ion Collider

Bernauer, J. C.; Dean, C. T.; Fanelli, C.; Huang, J.; Kauder, K.; Lawrence, D.; Osborn, J. D.; Paus, C.; Adkins, J. K.; Akiba, Y.; Al-bataineh, A.; Amaryan, M. J.; Arsene, I. C.; Gayoso, C. Ayerbe; Bae, Junghyun; Bai, Xiaohou; Baker, M. D.; Bashkanov, M.; Bellwied, R.; Benmokhtar, F.; Бердников, В. В.; Bock, F.; Boeglin, W.; Borysova, M.; Brash, E. J.; Brindza, P.; Briscoe, W. J.; Brooks, M. L.; Bueltmann, S.; Bukhari, M. H. S.; Bylinkin, A.; Capobianco, R.; Chang, Wen Chen; Cheon, Y.; Chen, K.; Chen, K. -F.; Cheng, Kun; Chiu, Ming-Chih; Chujo, T.; Citron, Z. H.; Cline, E.; Cohen, E.; Cormier, T. M.; Morales, Y. Corrales; Cotton, C.; Crafts, J.; Crawford, Chad; Creekmore, S.; Cuevas, C.; Cunningham, J. D.; David, G.; Demarteau, M.; Diehl, Stefan; Doshita, N.; Dupré, R.; Durham, J. M.; Dzhygadlo, R.; Ehlers, R. J.; Fassi, L. El; Emmert, A.; Ent, R.; Fatemi, R.; Fegan, S.; Фингер, М.; Frantz, J. E.; Friedman, M.; Friščić, I.; Gangadharan, D. R.; Gardner, S.; Gates, K.; Geurts, F. J. M.; Gilman, R.; Glazier, D. I.; Glimos, Ewa; Goto, Yuichi; Grau, N.; Greene, S.; Guo, A. Q.; Guo, Lingzhong; Ha, S.; Haggerty, J. S.; Hayward, T. B.; He, Xiao‐Gang; Hen, O.; Higinbotham, D. W.; Hoballah, M.; Horn, T.; Hoghmrtsyan, A.; Hsu, P.-h.J.; Huber, G. M.; Hutson, Alek; Hwang, K.Y.; Hyde, Charles; Inaba, M.; Iwata, Tadahisa; Jo, H. S.; Joo, K. K.; Kalantarians, N.; Kalicy, G.; Kawade, K.; Kay, S.; Kim, A.; Kim, B.; Kim, C.; Kim, M.; Kim, Y.; Kistenev, E.; Klimenko, V.; Ko, S.; Korover, I.; Korsch, W.; Krintiras, G.; Kuhn, S. E.; Kuo, C. M.; Kutz, T.; Lajoie, J. G.; Lebedev, S.; Lee, H.; Lee, J. S. H.; Lee, S. W.; Lee, Y. -J.; Li, W.; Li, X.; Liang, Yuming; Lim, S.N.; Lin, Cecilia; Lin, D. X.; Liu, K.; Liu, M. X.; Livingston, K.; Liyanage, N.; Llope, W. J.; Loizides, C.; Long, Erping; Lu, R.-S.; Lu, Zhengding; Lynch, W.; Marchand, D.; Marčišovský, M.; Markowitz, P.; Marukyan, H.; McGaughey, P. L.; Mihovilovič, M.; Milner, R.; Milov, A.; Miyachi, Y.; Monaghan, Peter; Montgomery, R. A.; Morrison, D. R. O.; Movsisyan, A.; Mkrtchyan, H.; Mkrtchyan, A.; Camacho, C. Muñoz; Murray, M.; Nagai, K.; Nagle, JL; Nakagawa, I.; Nattrass, C.; Nguyen, D.; Niccolai, S.; Nouicer, R.; Nukazuka, G.; Nycz, M.; Okorokov, V.; Orešić, S.; O’Shaughnessy, C.; Paganis, E.; Papandreou, Z.; Pate, S. F.; Patel, M.; Penman, G.; Perdekamp, M. Grosse; Perepelitsa, D. V.; Costa, H. Periera da; Peters, K.; Phelps, W.; Piasetzky, E.; Pinkenburg, C.; Procházka, I.; Protzman, T.; Purschke, M. L.; Putschke, J.; Pybus, J.R.; Rajput-Ghoshal, Renuka; Rasson, J.; Raue, B. A.; Read, Kenneth Francis; Røed, K.; Reed, Robert A.; Reinhold, J.; Renner, Elisabeth; Richards, J.; Riedl, C.; Rinn, Timothy Thomas; Roche, J.; Roland, G.; Ron, G.; Rosati, M.; Royon, C.; Ryu, Jong-hyun; Salur, S.; N., Santiesteban,; Santos, R. Felix dos; Sarsour, M.; Schambach, Johann Joachim; Schmidt, A.; Schmidt, N.; Schwarz, C.; Schwiening, J.; Seidl, R.; Sickles, A. M.; Simmerling, P.; Širca, S.; Sharma, Dharmendra; Shi, Z.B.; Shibata, T.; Shih, Chi-Tin; Shimizu, S.; Shrestha, Uttam; Slifer, K.; Smith, K. M.; Sokhan, D.; Soltz, R. A.; Sondheim, W. E.; Song, Jiwei; Strakovsky, I. I.; Steinberg, P.; Stepanov, P.Yu.; Stevens, J.M.G.F.; Strube, J.; Sun, Peng; Sun, X.; Suresh, K.; Tadevosyan, V.; Tang, W.; Araya, S. Tapia; Tarafdar, S.; Teodorescu, L.; Timmins, A. R.; Tomášek, L.; Trotta, N.; Trotta, R.; Tveter, Trine Spedstad; Umaka, E. N.; Usman, Ali Muhammad; Hecke, H. W. van; Hulse, C. Van; Velkovska, J.; Voutier, E.; Wang, P. K.; Wang, Q.; Wang, Y.; Wang, Y.; Watts, D. P.; Wickramaarachchi, N.; Weinstein, Larry; Williams, M.; Wong, Cheuk-Ping; Wood, L.; Wood, M. H.; Woody, C. L.; Wysłouch, B.; Xiao, Zuoli; Yamazaki, Y.; Yang, Youming; Ye, Z.; Yoo, Hwidong; Yurov, M.; Zachariou, N.; Zajc, W. A.; Zhang, J.; Zhang, Y.; Zhao, Ye; Zheng, Xiao; Zhuang, P.

doi:10.1016/j.nima.2022.167859

Cited by 9 publications

(1 citation statement)

References 18 publications

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“…Streaming readout is not required for the TPEX measurements as the time between bunches will be large (∼80 ms) leaving sufficient time to readout the detectors using a simple trigger based on the beam bunch clock. However, at the EIC streaming readout is widely accepted as the preferred and necessary approach to readout the large number channels from various detectors and present the data in a coordinated fashion to the clusters of computers that will select valid events and perform event reconstruction [12].…”

Section: Jinst 18 P09001mentioning

confidence: 99%

Test of streaming and triggered readout schemes for the TPEX lead tungstate calorimeter

Friščić,

Cline,

Bernauer

et al. 2023

J. Inst.

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Tests of a prototype lead tungstate calorimeter were made over two weeks at the end of September, 2019, at the DESY II Test Beam Facility in Hamburg, Germany. The purpose of these tests was to gain experience with the construction, operation, and performance of a simple lead tungstate calorimeter, and also to compare a traditional triggered readout scheme with a streaming readout approach. These tests are important for the proposed Two-Photon Exchange experiment at DESY [1] and for work towards a future electromagnetic calorimeter that could be used in an Electron-Ion Collider detector. Details on the comparison of streaming and triggered readout schemes are presented here.

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Section: Jinst 18 P09001mentioning

confidence: 99%

Test of streaming and triggered readout schemes for the TPEX lead tungstate calorimeter

Friščić,

Cline,

Bernauer

et al. 2023

J. Inst.

Self Cite

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Artificial Intelligence for the Electron Ion Collider (AI4EIC)

Allaire,

Ammendola,

Aschenauer

et al. 2024

Comput Softw Big Sci

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The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments.

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Predicting the exclusive diffractive electron-ion cross section at small x with machine learning in Sartre

Singh

Toll

2023

Computer Physics Communications

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Scientific computing plan for the ECCE detector at the Electron Ion Collider

Cited by 9 publications

References 18 publications

Test of streaming and triggered readout schemes for the TPEX lead tungstate calorimeter

Test of streaming and triggered readout schemes for the TPEX lead tungstate calorimeter

Artificial Intelligence for the Electron Ion Collider (AI4EIC)

Predicting the exclusive diffractive electron-ion cross section at small x with machine learning in Sartre

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