Model based Robust Peak Detection Algorithm of Radiation Pulse Shape using Limited Samples

Abstract

Impulses coming from radiation detector are amplified / enhanced using charge sensitive low noise preamplifier which modifies the original pulse shape retaining the measurable properties of input. One of the important parameter of such pulse shape is the pulse height which in most cases is proportional to the energy of in-coming impulses. In application of high resolution measurement like Multi Channel Analyzer, the accuracy of pulse height measurement is an important issue for resolution of energy peak between two adjacent channels. In this paper, we propose a method to estimate the accurate peak using model based computation of radiation pulse shape profile with limited digitized samples on pulse profile to achieve higher energy resolution. Use of limited digitized samples allow us to use low sampling ADC instead of fast flash ADC having sampling speed of 100 to 400 Ms/s. The model is built using multi-layer Feed Forward type Neural Network (FFNN) along with back propagation learning algorithm. It uses 4 to 5 normalized random samples as input to fit the detector and amplifier shape characteristic and provides output indicating its peak. It assumes, shape characteristic remains constant with respect to pulse height variation. To test the model, we have generated simulated pulses similar to the output of CR-RC shaper circuit with pattern signature database for FFNN instead of radiation detector and front end analog signal processing circuits. In training phase, Neural Network is trained using known peak value and 4 samples taken at fixed intervals. The trained FFNN weights are used in operating phase. In operating phase, random 4 sample points are acquired on pulse profile and applied to a trained FFNN model to estimate the peak value using trained FFNN weights. Result obtained with simulated data set is very encouraging. Percentage accuracy of correct prediction of peak height is more than 99.95% which is equivalent to 2 channel shift error in 4096 channel Multi Channel Analyzer (MCA).