Unfolding of sparse high-energy \(\gamma\)-ray spectra from LaBr\(_{3}\):Ce detectors
Here we report on the characterization of one of the large-volume LaBr\(_{3}\):Ce detectors for the ELIGANT project at ELI-NP. The main focus of this work is the response function for high-energy \(\gamma\) rays of such detectors. In particular, we compare a selection of unfolding methods to resolve...
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Published in | arXiv.org |
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Main Authors | , , , , , , , , , , , , , , , |
Format | Paper |
Language | English |
Published |
Ithaca
Cornell University Library, arXiv.org
16.10.2019
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Subjects | |
Online Access | Get full text |
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Summary: | Here we report on the characterization of one of the large-volume LaBr\(_{3}\):Ce detectors for the ELIGANT project at ELI-NP. The main focus of this work is the response function for high-energy \(\gamma\) rays of such detectors. In particular, we compare a selection of unfolding methods to resolve small structures in \(\gamma\)-ray spectra with high-energies. Three methods have been compared using \(\gamma\)-ray spectra with energies up to 12 MeV obtained in an experiment at the 3 MV Tandetron\texttrademark\ facility at IFIN-HH. The results show that the iterative unfolding approach gives the best qualitative reproduction of the emitted \(\gamma\)-ray spectrum. Furthermore, the correlation fluctuations in high-energy regime from the iterative method are two orders of magnitude smaller than when using the matrix inversion approach with second derivative regularization. In addition, the iterative method is computationally faster as it does not contain large matrix inversions. The matrix inversion method does, however, give more consistent results over the full energy range and in the low-statistics limit. Our conclusion is that the performance of the iterative approach makes it well suitable for semi-online analysis of experimental data. These results will be important, both for experiments with the ELIGANT setup, and for on-line diagnostics of the energy spread of the \(\gamma\)-ray beam which is under implementation at ELI-NP. |
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ISSN: | 2331-8422 |
DOI: | 10.48550/arxiv.1910.07251 |