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Fast maximum-likelihood estimation methods for scintillation cameras and other optical sensors
L R Furenlid1, J Y Hesterman2, H H Barrett1
1Department of Radiology, University of Arizona Tucson, AZ 85724 ; College of Optical Sciences, University of Arizona Tucson, AZ 85724.
Proceedings of Spie--The International Society for Optical Engineering
|September 9, 2015
Summary
A novel, fast maximum-likelihood (ML) search algorithm enhances experimental data processing for x-ray and gamma-ray detection. This method improves event position, energy, and timing estimation, with broad applications in scientific testing.
Area of Science:
- Physics
- Data Science
- Signal Processing
Background:
- Maximum-likelihood estimation (MLE) is a powerful statistical method for data analysis.
- Accurate calibration data is crucial for effective information extraction from experimental measurements.
Purpose of the Study:
- To introduce a new, fast maximum-likelihood (ML) search algorithm.
- To demonstrate its applicability to various data processing tasks in experimental physics and beyond.
Main Methods:
- Development of a computationally efficient ML-search algorithm.
- Implementation of the algorithm for hardware or software applications.
- Validation using simulated or experimental data for x-ray and gamma-ray detection.
Main Results:
- The algorithm provides a fast and effective approach to information extraction.
- Demonstrated success in estimating gamma-ray event position, energy, and timing.
- Potential for application in optical testing and wave-front sensing.
Conclusions:
- The proposed ML-search algorithm offers significant advantages for processing experimental data.
- It provides a versatile tool for enhancing precision in scientific measurements and testing.
