Machine learning-based interpolation of density-dependent full-energy peak efficiency in Marinelli geometry using experimental NaI(Tl) calibration data
- DOI
- 10.1016/j.apradiso.2026.112939
- Published
- 2026-12
- Container
- Applied Radiation and Isotopes
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.apradiso.2026.112939,
title = {Machine learning-based interpolation of density-dependent full-energy peak efficiency in Marinelli geometry using experimental NaI(Tl) calibration data},
author = {M. Tamkas},
year = {2026},
journal = {Applied Radiation and Isotopes},
doi = {10.1016/j.apradiso.2026.112939},
url = {https://doi.org/10.1016/j.apradiso.2026.112939}
}RIS
TY - JOUR TI - Machine learning-based interpolation of density-dependent full-energy peak efficiency in Marinelli geometry using experimental NaI(Tl) calibration data AU - M. Tamkas PY - 2026 JO - Applied Radiation and Isotopes DO - 10.1016/j.apradiso.2026.112939 UR - https://doi.org/10.1016/j.apradiso.2026.112939 ER -
APA
Tamkas, M. (2026). Machine learning-based interpolation of density-dependent full-energy peak efficiency in Marinelli geometry using experimental NaI(Tl) calibration data. Applied Radiation and Isotopes. https://doi.org/10.1016/j.apradiso.2026.112939
Source records
- crossref · retrieved 2026-09-25T21:02:17.772Z