A stochastic process approach to multilayer neutron detectors
(2019) In Scandinavian Journal of Statistics 46(2). p.621-635- Abstract
The sparsity of the isotope Helium-3, ongoing since 2009, has initiated a new generation of neutron detectors. One particularly promising development line for detectors is the multilayer gaseous detector. In this paper, a stochastic process approach is used to determine the neutron energy from the additional data afforded by the multilayer nature of these novel detectors. The data from a multilayer detector consist of counts of the number of absorbed neutrons along the sequence of the detector's layers, in which the neutron absorption probability is unknown. We study the maximum likelihood estimator for the intensity and absorption probability and show its consistency and asymptotic normality, as the number of incoming neutrons goes to... (More)
The sparsity of the isotope Helium-3, ongoing since 2009, has initiated a new generation of neutron detectors. One particularly promising development line for detectors is the multilayer gaseous detector. In this paper, a stochastic process approach is used to determine the neutron energy from the additional data afforded by the multilayer nature of these novel detectors. The data from a multilayer detector consist of counts of the number of absorbed neutrons along the sequence of the detector's layers, in which the neutron absorption probability is unknown. We study the maximum likelihood estimator for the intensity and absorption probability and show its consistency and asymptotic normality, as the number of incoming neutrons goes to infinity. We combine these results with known results on the relation between the absorption probability and the wavelength to derive an estimator of the wavelength and to show its consistency and asymptotic normality.
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- author
- Anevski, Dragi LU ; Hall-Wilton, Richard LU ; Kanaki, Kalliopi LU and Pastukhov, Vladimir LU
- organization
- publishing date
- 2019
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- maximum likelihood, multinomial thinning, neutron detection, Poisson process
- in
- Scandinavian Journal of Statistics
- volume
- 46
- issue
- 2
- pages
- 621 - 635
- publisher
- Wiley-Blackwell
- external identifiers
-
- scopus:85058934255
- ISSN
- 0303-6898
- DOI
- 10.1111/sjos.12374
- language
- English
- LU publication?
- yes
- id
- 9d0cf664-cee4-45f7-8039-d1b9684307a0
- date added to LUP
- 2019-01-08 13:03:19
- date last changed
- 2022-04-25 20:28:09
@article{9d0cf664-cee4-45f7-8039-d1b9684307a0,
abstract = {{<p>The sparsity of the isotope Helium-3, ongoing since 2009, has initiated a new generation of neutron detectors. One particularly promising development line for detectors is the multilayer gaseous detector. In this paper, a stochastic process approach is used to determine the neutron energy from the additional data afforded by the multilayer nature of these novel detectors. The data from a multilayer detector consist of counts of the number of absorbed neutrons along the sequence of the detector's layers, in which the neutron absorption probability is unknown. We study the maximum likelihood estimator for the intensity and absorption probability and show its consistency and asymptotic normality, as the number of incoming neutrons goes to infinity. We combine these results with known results on the relation between the absorption probability and the wavelength to derive an estimator of the wavelength and to show its consistency and asymptotic normality.</p>}},
author = {{Anevski, Dragi and Hall-Wilton, Richard and Kanaki, Kalliopi and Pastukhov, Vladimir}},
issn = {{0303-6898}},
keywords = {{maximum likelihood; multinomial thinning; neutron detection; Poisson process}},
language = {{eng}},
number = {{2}},
pages = {{621--635}},
publisher = {{Wiley-Blackwell}},
series = {{Scandinavian Journal of Statistics}},
title = {{A stochastic process approach to multilayer neutron detectors}},
url = {{http://dx.doi.org/10.1111/sjos.12374}},
doi = {{10.1111/sjos.12374}},
volume = {{46}},
year = {{2019}},
}