Autonomous digitizer calibration of a Monte Carlo detector model through evolutionary simulation
Abstract Simulating the response of a radiation detector is a modelling challenge due to the stochastic nature of radiation, often complex geometries, and multi-stage signal processing. While sophisticated tools for Monte Carlo simulation have been developed for radiation transport, emulating signal...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-24022-x |
| _version_ | 1828298378678632448 |
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| author | Matthew Herald Andrei Nicuşan Tzany Kokalova Wheldon Jonathan Seville Christopher Windows-Yule |
| author_facet | Matthew Herald Andrei Nicuşan Tzany Kokalova Wheldon Jonathan Seville Christopher Windows-Yule |
| author_sort | Matthew Herald |
| collection | DOAJ |
| description | Abstract Simulating the response of a radiation detector is a modelling challenge due to the stochastic nature of radiation, often complex geometries, and multi-stage signal processing. While sophisticated tools for Monte Carlo simulation have been developed for radiation transport, emulating signal processing and data loss must be accomplished using a simplified model of the electronics called the digitizer. Due to a large number of free parameters, calibrating a digitizer quickly becomes an optimisation problem. To address this, we propose a novel technique by which evolutionary algorithms calibrate a digitizer autonomously. We demonstrate this by calibrating six free parameters in a digitizer model for the ADAC Forte. The accuracy of solutions is quantified via a cost function measuring the absolute percent difference between simulated and experimental coincidence count rates across a robust characterisation data set, including three detector configurations and a range of source activities. Ultimately, this calibration produces a count rate response with 5.8% mean difference to the experiment, improving from 18.3% difference when manually calibrated. Using evolutionary algorithms for model calibration is a notable advancement because this method is novel, autonomous, fault-tolerant, and achieved through a direct comparison of simulation to reality. The software used in this work has been made freely available through a GitHub repository. |
| first_indexed | 2024-04-13T12:39:45Z |
| format | Article |
| id | doaj.art-186a7a4643064c2ca572468be0c55844 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T12:39:45Z |
| publishDate | 2022-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-186a7a4643064c2ca572468be0c558442022-12-22T02:46:32ZengNature PortfolioScientific Reports2045-23222022-11-0112111210.1038/s41598-022-24022-xAutonomous digitizer calibration of a Monte Carlo detector model through evolutionary simulationMatthew Herald0Andrei Nicuşan1Tzany Kokalova Wheldon2Jonathan Seville3Christopher Windows-Yule4School of Chemical Engineering, University of BirminghamSchool of Chemical Engineering, University of BirminghamSchool of Physics and Astronomy, University of BirminghamSchool of Chemical Engineering, University of BirminghamSchool of Chemical Engineering, University of BirminghamAbstract Simulating the response of a radiation detector is a modelling challenge due to the stochastic nature of radiation, often complex geometries, and multi-stage signal processing. While sophisticated tools for Monte Carlo simulation have been developed for radiation transport, emulating signal processing and data loss must be accomplished using a simplified model of the electronics called the digitizer. Due to a large number of free parameters, calibrating a digitizer quickly becomes an optimisation problem. To address this, we propose a novel technique by which evolutionary algorithms calibrate a digitizer autonomously. We demonstrate this by calibrating six free parameters in a digitizer model for the ADAC Forte. The accuracy of solutions is quantified via a cost function measuring the absolute percent difference between simulated and experimental coincidence count rates across a robust characterisation data set, including three detector configurations and a range of source activities. Ultimately, this calibration produces a count rate response with 5.8% mean difference to the experiment, improving from 18.3% difference when manually calibrated. Using evolutionary algorithms for model calibration is a notable advancement because this method is novel, autonomous, fault-tolerant, and achieved through a direct comparison of simulation to reality. The software used in this work has been made freely available through a GitHub repository.https://doi.org/10.1038/s41598-022-24022-x |
| spellingShingle | Matthew Herald Andrei Nicuşan Tzany Kokalova Wheldon Jonathan Seville Christopher Windows-Yule Autonomous digitizer calibration of a Monte Carlo detector model through evolutionary simulation Scientific Reports |
| title | Autonomous digitizer calibration of a Monte Carlo detector model through evolutionary simulation |
| title_full | Autonomous digitizer calibration of a Monte Carlo detector model through evolutionary simulation |
| title_fullStr | Autonomous digitizer calibration of a Monte Carlo detector model through evolutionary simulation |
| title_full_unstemmed | Autonomous digitizer calibration of a Monte Carlo detector model through evolutionary simulation |
| title_short | Autonomous digitizer calibration of a Monte Carlo detector model through evolutionary simulation |
| title_sort | autonomous digitizer calibration of a monte carlo detector model through evolutionary simulation |
| url | https://doi.org/10.1038/s41598-022-24022-x |
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