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Isotope Spectrum Builder: An Application for Modeling Gamma-Ray Spectra of Common Isotopes Using Monte Carlo Pools and Experimental Backgrounds Cover

Isotope Spectrum Builder: An Application for Modeling Gamma-Ray Spectra of Common Isotopes Using Monte Carlo Pools and Experimental Backgrounds

Open Access
|Jul 2026

Abstract

The Isotope Spectrum Builder is an open-source software framework for generating realistic gamma-ray spectra by combining physics-based Monte Carlo signal event pools with experimentally derived background radiation data. Signal events are generated offline using detailed Geant4 simulations and incorporate detector-response effects via energy smearing, while background events are obtained by resampling measured laboratory background spectra acquired with the same detector technologies.

The software supports interactive and automated spectrum generation through a web interface and command-line batch tools, enabling users to configure the detector type, isotope, energy range, binning from 1024 to 4096 channels, signal and background statistics, and normalization mode, including raw counts, counts per second, or unit-area normalization. Generated spectra can be exported as CSV files and annotated PNG figures, or produced in large batches for machine-learning and algorithm-development workflows. The package is distributed through PyPI, allowing users to install it directly with pip and access the spectrum generation functionality as a reusable Python library.

The Isotope Spectrum Builder is intended for education, algorithm benchmarking, and data-driven research in nuclear spectroscopy, offering a lightweight yet reproducible alternative to on-demand Monte Carlo simulation.

DOI: https://doi.org/10.5334/jors.740 | Journal eISSN: 2049-9647
Language: English
Page range: 52 - 52
Submitted on: May 8, 2026
Accepted on: Jun 16, 2026
Published on: Jul 9, 2026
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services

© 2026 Konstantinos Karafasoulis, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.