Use of Bayesian Optimization to understand the structure of nuclei

J. Hooker; J. Kovoor; K. L. Jones; R. Kanungo; M. Alcorta; J. Allen; C. Andreoiu; L. Atar; D. W. Bardayan; S. S. Bhattacharjee; D. Blankstein; C. Burbadge; S. Burcher; W. N. Catford; S. Cha; K. Chae; D. Connolly; B. Davids; N. Esker; F. H. Garcia; S. Gillespie; R. Ghimire; A. Gula; G. Hackman; S. Hallam; M. Hellmich; J. Henderson; M. Holl; P. Jassal; S. King; T. Knight; R. Kruecken; A. Lepailleur; J. Liang; L. Morrison; P. D. O'Malley; S. D. Pain; X. Pereira-Lopez; A. Psaltis; A. Radich; A. C. Shotter; M. Vostinar; M. Williams; O. Workman

doi:10.1016/j.nimb.2021.11.014

Use of Bayesian Optimization to understand the structure of nuclei

J. Hooker
, J. Kovoor
, K. L. Jones
, R. Kanungo
, M. Alcorta
, J. Allen
, C. Andreoiu
, L. Atar
, D. W. Bardayan
, S. S. Bhattacharjee
, D. Blankstein
, C. Burbadge
, S. Burcher
, W. N. Catford
, S. Cha
, K. Chae
, D. Connolly
, B. Davids
, N. Esker
, F. H. Garcia

S. Gillespie, R. Ghimire, A. Gula, G. Hackman, S. Hallam, M. Hellmich, J. Henderson, M. Holl, P. Jassal, S. King, T. Knight, R. Kruecken, A. Lepailleur, J. Liang, L. Morrison, P. D. O'Malley, S. D. Pain, X. Pereira-Lopez, A. Psaltis, A. Radich, A. C. Shotter, M. Vostinar, M. Williams, O. Workman

Nuclear Structure and Nuclear Astrophysi

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

5 Scopus citations

Abstract

Monte Carlo simulations are widely used in nuclear physics to model experimental systems. In cases where there are significant unknown quantities, such as energies of states, an iterative process of simulating and fitting is often required to describe experimental data. We describe a Bayesian approach to fitting experimental data, designed for data from a ¹²Be(d,p) reaction measurement, using simulations made with GEANT4. Q-values from the ¹²C(d,p) reaction to well-known states in ¹³C are compared with simulations using BayesOpt. The energies of the states were not included in the simulation to reproduce the situation for ¹³Be where the states are poorly known. Both cases had low statistics and significant resolution broadening owing to large proton energy losses in the solid deuterium target. Excitation energies of the lowest three excited states in ¹³C were extracted to better than 90 keV, paving a way for extracting information on ¹³Be.

Original language	English
Pages (from-to)	6-11
Number of pages	6
Journal	Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms
Volume	512
DOIs	https://doi.org/10.1016/j.nimb.2021.11.014
State	Published - Feb 1 2022

Funding

This material is based upon work was supported in part by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-FG02-96ER40963 (UTK) and the U. S. National Science Foundation under Award Numbers PHY-1404218 (Rutgers) and PHY-2011890 (Notre Dame). This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC05-00OR22725 (ORNL). The authors are grateful for support from NSERC, Canada Foundation for Innovation, Nova Scotia Research and Innovation Trust. The support from RCNP, Osaka, for the target is gratefully acknowledged. TRIUMF receives funding via a contribution through the National Research Council Canada. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) Nos. 2020R1A2C1005981 and 2016R1A5A1013277. This work was partially supported by STFC Grant No. ST/L005743/1 (Surrey).

Keywords

Bayesian optimization
Direct reaction
Geant4 simulation
Low-energy reactions
Radioactive ion beam
Stable ion beam
Transfer reaction

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10.1016/j.nimb.2021.11.014

Cite this

Hooker, J., Kovoor, J., Jones, K. L., Kanungo, R., Alcorta, M., Allen, J., Andreoiu, C., Atar, L., Bardayan, D. W., Bhattacharjee, S. S., Blankstein, D., Burbadge, C., Burcher, S., Catford, W. N., Cha, S., Chae, K., Connolly, D., Davids, B., Esker, N., ... Workman, O. (2022). Use of Bayesian Optimization to understand the structure of nuclei. Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms, 512, 6-11. https://doi.org/10.1016/j.nimb.2021.11.014

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title = "Use of Bayesian Optimization to understand the structure of nuclei",

abstract = "Monte Carlo simulations are widely used in nuclear physics to model experimental systems. In cases where there are significant unknown quantities, such as energies of states, an iterative process of simulating and fitting is often required to describe experimental data. We describe a Bayesian approach to fitting experimental data, designed for data from a 12Be(d,p) reaction measurement, using simulations made with GEANT4. Q-values from the 12C(d,p) reaction to well-known states in 13C are compared with simulations using BayesOpt. The energies of the states were not included in the simulation to reproduce the situation for 13Be where the states are poorly known. Both cases had low statistics and significant resolution broadening owing to large proton energy losses in the solid deuterium target. Excitation energies of the lowest three excited states in 13C were extracted to better than 90 keV, paving a way for extracting information on 13Be.",

keywords = "Bayesian optimization, Direct reaction, Geant4 simulation, Low-energy reactions, Radioactive ion beam, Stable ion beam, Transfer reaction",

author = "J. Hooker and J. Kovoor and Jones, \{K. L.\} and R. Kanungo and M. Alcorta and J. Allen and C. Andreoiu and L. Atar and Bardayan, \{D. W.\} and Bhattacharjee, \{S. S.\} and D. Blankstein and C. Burbadge and S. Burcher and Catford, \{W. N.\} and S. Cha and K. Chae and D. Connolly and B. Davids and N. Esker and Garcia, \{F. H.\} and S. Gillespie and R. Ghimire and A. Gula and G. Hackman and S. Hallam and M. Hellmich and J. Henderson and M. Holl and P. Jassal and S. King and T. Knight and R. Kruecken and A. Lepailleur and J. Liang and L. Morrison and O'Malley, \{P. D.\} and Pain, \{S. D.\} and X. Pereira-Lopez and A. Psaltis and A. Radich and Shotter, \{A. C.\} and M. Vostinar and M. Williams and O. Workman",

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year = "2022",

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day = "1",

doi = "10.1016/j.nimb.2021.11.014",

language = "English",

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Hooker, J, Kovoor, J, Jones, KL, Kanungo, R, Alcorta, M, Allen, J, Andreoiu, C, Atar, L, Bardayan, DW, Bhattacharjee, SS, Blankstein, D, Burbadge, C, Burcher, S, Catford, WN, Cha, S, Chae, K, Connolly, D, Davids, B, Esker, N, Garcia, FH, Gillespie, S, Ghimire, R, Gula, A, Hackman, G, Hallam, S, Hellmich, M, Henderson, J, Holl, M, Jassal, P, King, S, Knight, T, Kruecken, R, Lepailleur, A, Liang, J, Morrison, L, O'Malley, PD, Pain, SD, Pereira-Lopez, X, Psaltis, A, Radich, A, Shotter, AC, Vostinar, M, Williams, M & Workman, O 2022, 'Use of Bayesian Optimization to understand the structure of nuclei', Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms, vol. 512, pp. 6-11. https://doi.org/10.1016/j.nimb.2021.11.014

TY - JOUR

T1 - Use of Bayesian Optimization to understand the structure of nuclei

AU - Hooker, J.

AU - Kovoor, J.

AU - Jones, K. L.

AU - Kanungo, R.

AU - Alcorta, M.

AU - Allen, J.

AU - Andreoiu, C.

AU - Atar, L.

AU - Bardayan, D. W.

AU - Bhattacharjee, S. S.

AU - Blankstein, D.

AU - Burbadge, C.

AU - Burcher, S.

AU - Catford, W. N.

AU - Cha, S.

AU - Chae, K.

AU - Connolly, D.

AU - Davids, B.

AU - Esker, N.

AU - Garcia, F. H.

AU - Gillespie, S.

AU - Ghimire, R.

AU - Gula, A.

AU - Hackman, G.

AU - Hallam, S.

AU - Hellmich, M.

AU - Henderson, J.

AU - Holl, M.

AU - Jassal, P.

AU - King, S.

AU - Knight, T.

AU - Kruecken, R.

AU - Lepailleur, A.

AU - Liang, J.

AU - Morrison, L.

AU - O'Malley, P. D.

AU - Pain, S. D.

AU - Pereira-Lopez, X.

AU - Psaltis, A.

AU - Radich, A.

AU - Shotter, A. C.

AU - Vostinar, M.

AU - Williams, M.

AU - Workman, O.

PY - 2022/2/1

Y1 - 2022/2/1

N2 - Monte Carlo simulations are widely used in nuclear physics to model experimental systems. In cases where there are significant unknown quantities, such as energies of states, an iterative process of simulating and fitting is often required to describe experimental data. We describe a Bayesian approach to fitting experimental data, designed for data from a 12Be(d,p) reaction measurement, using simulations made with GEANT4. Q-values from the 12C(d,p) reaction to well-known states in 13C are compared with simulations using BayesOpt. The energies of the states were not included in the simulation to reproduce the situation for 13Be where the states are poorly known. Both cases had low statistics and significant resolution broadening owing to large proton energy losses in the solid deuterium target. Excitation energies of the lowest three excited states in 13C were extracted to better than 90 keV, paving a way for extracting information on 13Be.

AB - Monte Carlo simulations are widely used in nuclear physics to model experimental systems. In cases where there are significant unknown quantities, such as energies of states, an iterative process of simulating and fitting is often required to describe experimental data. We describe a Bayesian approach to fitting experimental data, designed for data from a 12Be(d,p) reaction measurement, using simulations made with GEANT4. Q-values from the 12C(d,p) reaction to well-known states in 13C are compared with simulations using BayesOpt. The energies of the states were not included in the simulation to reproduce the situation for 13Be where the states are poorly known. Both cases had low statistics and significant resolution broadening owing to large proton energy losses in the solid deuterium target. Excitation energies of the lowest three excited states in 13C were extracted to better than 90 keV, paving a way for extracting information on 13Be.

KW - Bayesian optimization

KW - Direct reaction

KW - Geant4 simulation

KW - Low-energy reactions

KW - Radioactive ion beam

KW - Stable ion beam

KW - Transfer reaction

UR - https://www.scopus.com/pages/publications/85120731120

U2 - 10.1016/j.nimb.2021.11.014

DO - 10.1016/j.nimb.2021.11.014

M3 - Article

AN - SCOPUS:85120731120

SN - 0168-583X

VL - 512

SP - 6

EP - 11

JO - Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms

JF - Nuclear Instruments and Methods in Physics Research, Section B: Beam Interactions with Materials and Atoms

ER -

Use of Bayesian Optimization to understand the structure of nuclei

Abstract

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Keywords

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