AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider

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

doi:10.1016/j.nima.2022.167748

AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider

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

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

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 1.5 T 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.

Original language	English
Article number	167748
Journal	Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Volume	1047
DOIs	https://doi.org/10.1016/j.nima.2022.167748
State	Published - Feb 2023

Funding

We acknowledge support from the Office of Nuclear Physics in the Office of Science in the Department of Energy, USA , the National Science Foundation, USA , and the Los Alamos National Laboratory Laboratory Directed Research and Development (LDRD), USA 20200022DR .

Funders	Funder number
Los Alamos National Laboratory Laboratory Directed Research and Development
National Science Foundation
U.S. Department of Energy
Nuclear Physics
Laboratory Directed Research and Development

Keywords

Artificial Intelligence
Bayesian optimization
ECCE
Electron Ion Collider
Evolutionary algorithms
Tracking

Access to Document

10.1016/j.nima.2022.167748

Cite this

Fanelli, C., Papandreou, Z., Suresh, K., Adkins, J. K., Akiba, Y., Albataineh, A., Amaryan, M., Arsene, I. C., Gayoso, C. A., Bae, J., Bai, X., Baker, M. D., Bashkanov, M., Bellwied, R., Benmokhtar, F., Berdnikov, V., Bernauer, J. C., Bock, F., Boeglin, W., ... Zhuang, P. (2023). AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider. Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 1047, Article 167748. https://doi.org/10.1016/j.nima.2022.167748

@article{a2ed0fb4594d4a5b9aff1291912a2805,

title = "AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider",

abstract = "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 1.5 T 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.",

keywords = "Artificial Intelligence, Bayesian optimization, ECCE, Electron Ion Collider, Evolutionary algorithms, Tracking",

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

note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.",

year = "2023",

month = feb,

doi = "10.1016/j.nima.2022.167748",

language = "English",

volume = "1047",

journal = "Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment",

issn = "0168-9002",

publisher = "Elsevier",

}

Fanelli, C, Papandreou, Z, Suresh, K, Adkins, JK, Akiba, Y, Albataineh, A, Amaryan, M, Arsene, IC, Gayoso, CA, Bae, J, Bai, X, Baker, MD, Bashkanov, M, Bellwied, R, Benmokhtar, F, Berdnikov, V, Bernauer, JC, Bock, F, Boeglin, W, Borysova, M, Brash, E, Brindza, P, Briscoe, WJ, Brooks, M, Bueltmann, S, Bukhari, MHS, Bylinkin, A, Capobianco, R, Chang, WC, Cheon, Y, Chen, K, Chen, KF, Cheng, KY, Chiu, M, Chujo, T, Citron, Z, Cline, E, Cohen, E, Cormier, T, Morales, YC, Cotton, C, Crafts, J, Crawford, C, Creekmore, S, Cuevas, C, Cunningham, J, David, G, Dean, CT, Demarteau, M, Diehl, S, Doshita, N, Dupré, R, Durham, JM, Dzhygadlo, R, Ehlers, R, El Fassi, L, Emmert, A, Ent, R, Fatemi, R, Fegan, S, Finger, 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, SV, Guo, AQ, Guo, L, Ha, SK, Haggerty, J, Hayward, T, He, X, Hen, O, Higinbotham, DW, Hoballah, M, Horn, T, Hoghmrtsyan, A, Hsu, PHJ, Huang, J, Huber, G, Hutson, A, Hwang, KY, Hyde, CE, Inaba, M, Iwata, T, Jo, HS, Joo, K, Kalantarians, N, Kalicy, G, Kawade, K, Kay, SJD, Kim, A, Kim, B, Kim, C, Kim, M, Kim, Y, Kistenev, E, Klimenko, V, Ko, SH, Korover, I, Korsch, W, Krintiras, G, Kuhn, S, Kuo, CM, Kutz, T, Lajoie, J, Lawrence, D, Lebedev, S, Lee, H, Lee, JSH, Lee, SW, Lee, YJ, Li, W, Li, WB, Li, X, Liang, YT, Lim, S, Lin, CH, Lin, DX, Liu, K, Liu, MX, Livingston, K, Liyanage, N, Llope, WJ, Loizides, C, Long, E, Lu, RS, Lu, Z, Lynch, W, Mantry, S, Marchand, D, Marcisovsky, M, Markert, C, Markowitz, P, Marukyan, H, McGaughey, P, Mihovilovic, M, Milner, RG, Milov, A, Miyachi, Y, Mkrtchyan, A, Monaghan, P, Montgomery, R, Morrison, D, Movsisyan, A, Mkrtchyan, H, Camacho, CM, Murray, M, Nagai, K, Nagle, J, Nakagawa, I, Nattrass, C, Nguyen, D, Niccolai, S, Nouicer, R, Nukazuka, G, Nycz, M, Okorokov, VA, Orešić, S, Osborn, JD, O'Shaughnessy, C, Paganis, S, Pate, SF, Patel, M, Paus, C, Penman, G, Perdekamp, MG, Perepelitsa, DV, da Costa, HP, Peters, K, Phelps, W, Piasetzky, E, Pinkenburg, C, Prochazka, I, Protzman, T, Purschke, ML, Putschke, J, Pybus, JR, Rajput-Ghoshal, R, Rasson, J, Raue, B, Read, KF, Røed, K, Reed, R, Reinhold, J, Renner, EL, Richards, J, Riedl, C, Rinn, T, Roche, J, Roland, GM, 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, TA, Shih, CW, Shimizu, S, Shrestha, U, Slifer, K, Smith, K, Sokhan, D, Soltz, R, Sondheim, W, Song, J, Strakovsky, II, Steinberg, P, Stepanov, P, Stevens, J, Strube, J, Sun, P, Sun, X, Tadevosyan, V, Tang, WC, Araya, ST, Tarafdar, S, Teodorescu, L, Thomas, D, Timmins, A, Tomasek, L, Trotta, N, Trotta, R, Tveter, TS, Umaka, E, Usman, A, van Hecke, HW, Van Hulse, C, Velkovska, J, Voutier, E, Wang, PK, Wang, Q, Wang, Y, Watts, DP, Wickramaarachchi, N, Weinstein, L, Williams, M, Wong, CP, Wood, L, Wood, MH, Woody, C, Wyslouch, B, Xiao, Z, Yamazaki, Y, Yang, Y, Ye, Z, Yoo, HD, Yurov, M, Zachariou, N, Zajc, WA, Zha, W, Zhang, JL, Zhang, JX, Zhang, Y, Zhao, YX, Zheng, X & Zhuang, P 2023, 'AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider', Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, vol. 1047, 167748. https://doi.org/10.1016/j.nima.2022.167748

TY - JOUR

T1 - AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider

AU - Fanelli, C.

AU - Papandreou, Z.

AU - Suresh, K.

AU - Adkins, J. K.

AU - Akiba, Y.

AU - Albataineh, A.

AU - Amaryan, M.

AU - Arsene, I. C.

AU - Gayoso, C. Ayerbe

AU - Bae, J.

AU - Bai, X.

AU - Baker, M. D.

AU - Bashkanov, M.

AU - Bellwied, R.

AU - Benmokhtar, F.

AU - Berdnikov, V.

AU - Bernauer, J. C.

AU - Bock, F.

AU - Boeglin, W.

AU - Borysova, M.

AU - Brash, E.

AU - Brindza, P.

AU - Briscoe, W. J.

AU - Brooks, M.

AU - Bueltmann, S.

AU - Bukhari, M. H.S.

AU - Bylinkin, A.

AU - Capobianco, R.

AU - Chang, W. C.

AU - Cheon, Y.

AU - Chen, K.

AU - Chen, K. F.

AU - Cheng, K. Y.

AU - Chiu, M.

AU - Chujo, T.

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PY - 2023/2

Y1 - 2023/2

N2 - 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 1.5 T 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.

AB - 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 1.5 T 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.

KW - Artificial Intelligence

KW - Bayesian optimization

KW - ECCE

KW - Electron Ion Collider

KW - Evolutionary algorithms

KW - Tracking

UR - http://www.scopus.com/inward/record.url?scp=85145770275&partnerID=8YFLogxK

U2 - 10.1016/j.nima.2022.167748

DO - 10.1016/j.nima.2022.167748

M3 - Article

AN - SCOPUS:85145770275

SN - 0168-9002

VL - 1047

JO - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

JF - Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment

M1 - 167748

ER -

AI-assisted optimization of the ECCE tracking system at the Electron Ion Collider

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