Pulse pile-up correction by auto-regression on linear operations (ARLO) method: A comparison with integration-based algorithms

• Published in: Nuclear engineering and technology Vol. 56; no. 9; pp. 3904 - 3913
• Main Author: Mohammadian-Behbahani, Mohammad-Reza
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2024; Elsevier; 한국원자력학회
• Subjects: Auto-regression on linear operations (ARLO); Exponential fitting; Integral-equation based methods; Pile-up correction; Pulse-tail extrapolation; 원자력공학; Pulse-tail extrapolation; Auto-regression on linear operations (ARLO); Pile-up correction; Integral-equation based methods; Exponential fitting
• ISSN: 1738-5733; 2234-358X; 2234-358X
• DOI: 10.1016/j.net.2024.04.037

Radiation detection at high count rate suffers from pulse pile-up, where the counting data and energy information of the system are affected by the overlapping of the system output pulses. There exist various pile-up correction strategies to recover the true information of the pulses, among which pulse-tail extrapolation is a well-known method focused on in this study. Present work aims to use a mono-exponential model for extrapolating the pileup-distorted trailing edge of a pulse, to provide a reference line for calculating the true amplitude of its subsequent overlapping pulse. To this goal, the auto-regression on linear operations (ARLO) method is examined and compared with two integration-based methods (the Foss and the Matheson methods), as well as the non-linear least squares (NLS) method. Despite a higher sensitivity to noise, the ARLO method was able to provide a simple, non-iterative solution with a performance over 400 times faster than the NLS algorithm, according to the analysis of a high count rate set of experimental pulses from a NaI(Tl) detection system. Foss and Matheson methods also provided solutions reasonably faster than NLS (but not surpassing ARLO), performing exactly the same as each other with results very close to NLS, benefiting from their non-iterative nature.