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Differentiating multi-MeV, multi-ion spectra with CR-39 solid-state nuclear track detectors

Authors :
M. S. Schollmeier
J. J. Bekx
J. Hartmann
E. Schork
M. Speicher
A. F. Brodersen
A. Fazzini
P. Fischer
E. Gaul
B. Gonzalez-Izquierdo
M. M. Günther
A. K. Härle
R. Hollinger
K. Kenney
J. Park
D. E. Rivas
V. Scutelnic
Z. Shpilman
S. Wang
J. J. Rocca
G. Korn
Source :
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Publication Year :
2023
Publisher :
Nature Portfolio, 2023.

Abstract

Abstract The development of high intensity petawatt lasers has created new possibilities for ion acceleration and nuclear fusion using solid targets. In such laser-matter interaction, multiple ion species are accelerated with broad spectra up to hundreds of MeV. To measure ion yields and for species identification, CR-39 solid-state nuclear track detectors are frequently used. However, these detectors are limited in their applicability for multi-ion spectra differentiation as standard image recognition algorithms can lead to a misinterpretation of data, there is no unique relation between track diameter and particle energy, and there are overlapping pit diameter relationships for multiple particle species. In this report, we address these issues by first developing an algorithm to overcome user bias during image processing. Second, we use calibration of the detector response for protons, carbon and helium ions (alpha particles) from 0.1 to above 10 MeV and measurements of statistical energy loss fluctuations in a forward-fitting procedure utilizing multiple, differently filtered CR-39, altogether enabling high-sensitivity, multi-species particle spectroscopy. To validate this capability, we show that inferred CR-39 spectra match Thomson parabola ion spectrometer data from the same experiment. Filtered CR-39 spectrometers were used to detect, within a background of ~ 2 × 1011 sr−1 J−1 protons and carbons, (1.3 ± 0.7) × 108 sr−1 J−1 alpha particles from laser-driven proton-boron fusion reactions.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
20452322
Volume :
13
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Reports
Publication Type :
Academic Journal
Accession number :
edsdoj.4f059e7e7e564090861249acd0d1e141
Document Type :
article
Full Text :
https://doi.org/10.1038/s41598-023-45208-x