Abstract

Dead-time losses are well recognized and studied drawbacks in counting and spectroscopic systems. In this work the abilities on dead-time correction of a real-time digital pulse processing (DPP) system for high-rate high-resolution radiation measurements are presented. The DPP system, through a fast and slow analysis of the output waveform from radiation detectors, is able to perform multi-parameter analysis (arrival time, pulse width, pulse height, pulse shape, etc.) at high input counting rates (ICRs), allowing accurate counting loss corrections even for variable or transient radiations. The fast analysis is used to obtain both the ICR and energy spectra with high throughput, while the slow analysis is used to obtain high-resolution energy spectra. A complete characterization of the counting capabilities, through both theoretical and experimental approaches, was performed. The dead-time modeling, the throughput curves, the experimental time-interval distributions (TIDs) and the counting uncertainty of the recorded events of both the fast and the slow channels, measured with a planar CdTe (cadmium telluride) detector, will be presented. The throughput formula of a series of two types of dead-times is also derived. The results of dead-time corrections, performed through different methods, will be reported and discussed, pointing out the error on ICR estimation and the simplicity of the procedure. Accurate ICR estimations (nonlinearity < 0.5%) were performed by using the time widths and the TIDs (using 10 ns time bin width) of the detected pulses up to 2.2 Mcps. The digital system allows, after a simple parameter setting, different and sophisticated procedures for dead-time correction, traditionally implemented in complex/dedicated systems and time-consuming set-ups.

Highlights

  • Quantitative analysis in X-ray and -ray experiments requires accurate and precise estimation of input photon counting rate (ICR or ) and photon energy, even at high counting rate conditions

  • When the arrival of events is random in time (Bateman, 2000), deadtimes are classified into two main categories: (i) non-paralyzable dead-time (Yu & Fessler, 2000) and (ii) paralyzable deadtime (Yu & Fessler, 2000)

  • Since the dead-time models of type I and II lead to identical results in the limit of small dead-time losses, the exponential modeling of the time-interval distributions (TIDs) of the fast shaped pulses can be compared with the similar behaviour of the non-paralyzable dead-time, which is characterized by zero value for t F and by an exponential TID for t > F (Muller, 1967; Pomme, 1999)

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Summary

Introduction

Quantitative analysis in X-ray and -ray experiments requires accurate and precise estimation of input photon counting rate (ICR or ) and photon energy, even at high counting rate conditions. Pulse shape discrimination (PSD) techniques were successfully used, in our previous works (Abbene & Gerardi, 2011; Abbene et al, 2012, 2013a,b, 2015; Gerardi & Abbene, 2014), to minimize incomplete charge collection effects, pile-up and charge sharing We stress that this PSHA, performed on isolated time windows containing a single CSP pulse, allows a strong reduction of the corruptions that the traditional analysis produces to adjacent pulses (residual tails at the end of shaped pulses), minimizing baseline shifts at high ICRs. The output results from both channels are provided in listing mode, where each list is characterized by a user-chosen number of event-sequences (typical fast channel sequence: arrival time, fast energy and pulse width; typical slow channel sequence: arrival time, slow energy and peaking time). The data within each list (i.e. the sequences: arrival time, energy, etc.) allow a finer analysis of the time evolution of the energy spectra (e.g. changes of the rate of some energy lines in the spectrum) and loss-counting corrections can be performed

Dead-time of the fast channel
Dead-time of the slow channel
Experimental procedures
Dead-time correction and counting rates in the fast channel
Dead-time correction and counting rates in the slow channel
Conclusions
New York
14. New York
Findings
Methods
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