A Comparative Study of Regression Methods for Solving the Timepix Calibration Task
- NázevTitle
- A Comparative Study of Regression Methods for Solving the Timepix Calibration TaskA Comparative Study of Regression Methods for Solving the Timepix Calibration Task
- Druh výsledkuResult type
- Článek v časopiseJournal article
- AutořiAuthors
- J. Broulím, M. Prokop, L. Nouzak, P. Smrčka
- DOIDOI
- 10.3390/s25216714
- Časopis / citaceJournal / citation
- Sensors. 2025, 25(21), 1-21. ISSN 1424-8220.
- RokYear
- 2025
- JazykLanguage
- eng
- WoSWoS
- 001612974500001
- ScopusScopus
- 2-s2.0-105021624932
- RIVRIV
- RIV/68407700:21340/25:00387638!RIV26-MSM-21340___
- ProjektProject
- Institucionální podpora na rozvoj výzkumné org.Institucionální podpora na rozvoj výzkumné org.; Detektory ionizujícího záření ve fundamentálních experimentech a aplikovaném výzkumuDetectors of ionizing radiation in fundamental experiments and applied research
AbstraktAbstract
In this article, we provide a study of the energy calibration model used for Timepix-type detectors. The Timepix detectors, operating in Time-over-Threshold mode, measure information that needs to be mapped into the corresponding energies using a non-linear function. We consider three iterative algorithms, Gradient-Descent, Gauss-Newton and Levenberg-Marquardt algorithm, which we modify according to the calibration model constraints to perform better in terms of the convergence properties. Moreover, based on the variable projection method, we suggest a partial linearization of the calibration problem and provide results for this novel method.
In this article, we provide a study of the energy calibration model used for Timepix-type detectors. The Timepix detectors, operating in Time-over-Threshold mode, measure information that needs to be mapped into the corresponding energies using a non-linear function. We consider three iterative algorithms, Gradient-Descent, Gauss-Newton and Levenberg-Marquardt algorithm, which we modify according to the calibration model constraints to perform better in terms of the convergence properties. Moreover, based on the variable projection method, we suggest a partial linearization of the calibration problem and provide results for this novel method.