Evaluation of virtual point detector, for HPGe spectrometers of different sizes, using Monte Carlo simulations, and artificial neural networks / Sedigheh Sina …[et al.]

Sina, Sedigheh and Molaeimanesh, Zahra and Karimipoorfard, Mehrnoosh (2020) Evaluation of virtual point detector, for HPGe spectrometers of different sizes, using Monte Carlo simulations, and artificial neural networks / Sedigheh Sina …[et al.]. Scientific Research Journal, 17 (1). pp. 15-26. ISSN 2289-649X

Official URL: https://srj.uitm.edu.my/

Abstract

The virtual point detector concept is useful in gamma-ray spectroscopy. In this study, the virtual point detector, h0, was obtained for High Purity Germanium (HPGe) detectors of different sizes using MCNP5 Monte Carlo simulations. The HPGe detectors with different radii (rd), and height (hd), having aluminum, or Carbon windows, were simulated. A point photon source emitting several gammas with specific energies was defined at a distance x of the detectors. The pulse height distribution was scored using F8 tally. Finally, the artificial neural network was used for predicting the h0 values for every value of hd, rd, and x. Because of the high simulation duration of MCNP code, a trained ANN is used to predict the value of h0 for each detector size. The results indicate that the Artificial Neural Network (ANN) can predict the virtual point detector good accuracy.

Metadata

Item Type: Article
Creators:
Creators
Email / ID Num.
Sina, Sedigheh
samirasina@yahoo.com
Molaeimanesh, Zahra
UNSPECIFIED
Karimipoorfard, Mehrnoosh
UNSPECIFIED
Subjects: Q Science > QC Physics > Radiation physics (General)
Divisions: Universiti Teknologi MARA, Shah Alam > Research Management Centre (RMC)
Journal or Publication Title: Scientific Research Journal
UiTM Journal Collections: UiTM Journal > Scientific Research Journal (SRJ)
ISSN: 2289-649X
Volume: 17
Number: 1
Page Range: pp. 15-26
Keywords: Virtual point detector, Efficiency, Natural radionuclides, HPGe, Artificial neural network
Date: 2020
URI: https://ir.uitm.edu.my/id/eprint/30091
Edit Item
Edit Item

Download

[thumbnail of 30091.pdf] Text
30091.pdf

Download (1MB)

ID Number

30091

Indexing

Statistic

Statistic details