Digital multishaping for white noise reduction and its implementation on programmable logic

Abstract: One of the most common noise type in particle physics detectors is white noise. Its impact inversely correlates with the shaping time of the signal. An extended shaping time would significantly improve resolution in pulse height measurement. However, prolonged shaping times elevate the risks associated with pile-up and detector saturation, particularly under conditions of high particle arrival rates. Therefore, it is necessary to reach a compromise on the shaping time in order to achieve a balance between these opposing effects. The originality of this study is to process pulses of a single particle detector with multiple shapers in parallel instead of one, enabling independent pulse analysis for each shaper. Subsequently, a Finite State Machine (FSM) is employed to select the optimal pulse height based on the current pulse arrival rate. The proposed method was implemented and evaluated using both Python and programmable logic. The results show how effective this technique is at reducing noise, which is shown through the analysis of energy histograms. Thus, this strategy offers a promising approach to enhancing particle detector performance, especially at fluctuating particle arrival rates.