Bayesian tuning of the Compact Muon Beam Line for the Mu3e experiment

被引:0
作者
Dal Maso, G. [1 ,4 ]
Kettle, P. -R. [1 ]
Knecht, A.
Leonetti, F. [2 ,3 ]
Lospalluto, G. [4 ]
Papa, A. [1 ,2 ,3 ]
Valetov, E. [1 ,5 ]
机构
[1] Paul Scherrer Inst, Forschungsstr 111, CH-5232 Villigen, Switzerland
[2] INFN, Sez Pisa, Largo B Pontecorvo 3, I-56127 Pisa, Italy
[3] Univ Pisa, Dipartimento Fis, Largo B Pontecorvo 3, I-56127 Pisa, Italy
[4] Swiss Fed Inst Technol, Inst Particle Phys & Astrophys, Otto Stern Weg 5, CH-8093 Zurich, Switzerland
[5] Michigan State Univ, Dept Phys & Astron, E Lansing, MI 48824 USA
基金
欧盟地平线“2020”;
关键词
Muon beam; Large scale optimization; Beamline commissioning; Bayesian optimization;
D O I
10.1016/j.nima.2024.169685
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
The Paul Scherrer Institute (PSI) currently delivers the most intense continuous muon beam in the world with up to several 108 8 mu+/s + /s and aims at maintaining its leadership by upgrading its beamlines within the High Intensity Muon Beams project (HIMB) (Eichler et al. 2022; Dal Maso et al. 2023) with projected intensities up to 10 10 mu+/s, + /s, and having a huge impact on low-energy, high-precision muon based searches. To reach such high quality tunes during commissioning, a novel tuning strategy is required, due to the large aberrations introduced by the employment of solenoidal elements along the HIMB beamlines. This paper presents the preliminary tests carried out in December 2023 at the Compact Muon Beam Line (CMBL) at PSI, serving the Mu3e experiment (Arndt et al., 1012) a tuning of low energy muon transfer lines with Bayesian algorithms was performed.
引用
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页数:2
相关论文
共 7 条
[1]  
Akiba T., 2019, P 25 ACM SIGKDD
[2]   Technical design of the phase I Mu3e experiment [J].
Arndt, K. ;
Augustin, H. ;
Baesso, P. ;
Berger, N. ;
Berg, F. ;
Betancourt, C. ;
Bortoletto, D. ;
Bravar, A. ;
Briggl, K. ;
vom Bruch, D. ;
Buonaura, A. ;
Cadoux, F. ;
Barajas, C. Chavez ;
Chen, H. ;
Clark, K. ;
Cooke, P. ;
Corrodi, S. ;
Damyanova, A. ;
Demets, Y. ;
Dittmeier, S. ;
Eckert, P. ;
Ehrler, F. ;
Fahrni, D. ;
Gagneur, S. ;
Gerritzen, L. ;
Goldstein, J. ;
Gottschalk, D. ;
Grab, C. ;
Gredig, R. ;
Groves, A. ;
Hammerich, J. ;
Hartenstein, U. ;
Hartmann, U. ;
Hayward, H. ;
Herkert, A. ;
Hesketh, G. ;
Hetzel, S. ;
Hildebrandt, M. ;
Hodge, Z. ;
Hofer, A. ;
Huang, Q. H. ;
Hughes, S. ;
Huth, L. ;
Immig, D. M. ;
Jones, T. ;
Jones, M. ;
Kaestli, H-C ;
Koeppel, M. ;
Kettle, P-R ;
Kiehn, M. .
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 2021, 1014
[3]  
Dal Maso Giovanni, 2023, EPJ Web of Conferences, DOI 10.1051/epjconf/202328201012
[4]   THE EXPERIMENTAL PHYSICS AND INDUSTRIAL CONTROL-SYSTEM ARCHITECTURE - PAST, PRESENT, AND FUTURE [J].
DALESIO, LR ;
HILL, JO ;
KRAIMER, M ;
LEWIS, S ;
MURRAY, D ;
HUNT, S ;
WATSON, W ;
CLAUSEN, M ;
DALESIO, J .
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT, 1994, 352 (1-2) :179-184
[5]  
Eichler R., 2022, Report No.: 22-01
[6]   BAYESIAN-APPROACH TO GLOBAL OPTIMIZATION AND APPLICATION TO MULTIOBJECTIVE AND CONSTRAINED PROBLEMS [J].
MOCKUS, JB ;
MOCKUS, LJ .
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, 1991, 70 (01) :157-172
[7]   Evolutionary optimal trajectory planning for industrial robot with payload constraints [J].
Saravanan, R. ;
Ramabalan, S. ;
Balamurugan, C. .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2008, 38 (11-12) :1213-1226