Search: onr:"swepub:oai:DiVA.org:kth-328795" >
Gamma-ray track rec...
Gamma-ray track reconstruction using graph neural networks
-
- Andersson, Mikael (author)
- KTH,Fysik
-
- Bäck, Torbjörn, 1967- (author)
- KTH,Kärnfysik,Albanova VinnExcellence Center for Protein Technology, ProNova
-
(creator_code:org_t)
- Elsevier BV, 2023
- 2023
- English.
-
In: Nuclear Instruments and Methods in Physics Research Section A. - : Elsevier BV. - 0168-9002 .- 1872-9576. ; 1048
- Related links:
-
https://doi.org/10.1...
-
show more...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- Since the advent of the new generation of germanium detector arrays for low-energy nuclear physics experiments utilizing gamma-ray tracking, the challenges associated with track-reconstruction methods have been extensively studied. In the present work an approach based on recent developments in machine learning was used to address the problem. Here, a graph neural network was constructed and trained on data simulated in Geant4 in order to attempt track reconstruction of gamma rays below 1 MeV in a spherical shell geometry of pure germanium. Using a thick-shell geometry, and simulated data without energy-and position uncertainties the network achieved a reconstruction rate above 80% for complete tracks, and a combined peak-to-total value of 85% for energy spectra with four discrete peaks. For data with added noise, i.e. finite resolution in interaction-point position and energy, the corresponding peak-to-total ratio dropped to 74%. The track reconstruction was stable across multiplicities 1-10 but showed an increased error frequency in the energy range between 50 keV and 250 keV. To specifically study the complication of gamma tracks lost by out -scattering from the detector volume, a thin-shell (9 cm thickness) geometry was used together with a modified version of the GNN framework. By letting the GNN code identify and discriminate the out-scatter events, an improvement of the P/T value from 66% to 75% was found for the packed, noisy data. For the sake of comparison the new GNN model with existing gamma-ray tracking methods, a separate instance of the network was trained on slightly higher energies (up to 1.5 MeV) and multiplicities (up to 15) to evaluate 1.332 MeV photon cascade data in terms of P/T and photo-peak efficiency. The results for this GNN data set, with P/T values at 85% for single tracks and 74% for multiplicity 15, show clear promise when compared to the existing tracking methods.
Subject headings
- NATURVETENSKAP -- Fysik -- Subatomär fysik (hsv//swe)
- NATURAL SCIENCES -- Physical Sciences -- Subatomic Physics (hsv//eng)
Keyword
- Gamma-ray spectroscopy
- Gamma-ray tracking
- Machine learning
- GNN
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
To the university's database