Procesamiento digital de señal aplicado a la detección de partículas energéticas

Abstract

Spectroscopy applied to Nuclear Electronics aims to characterize the energetic particles arriving at a given radiation detector. This characterization is carried out by signal processing within detection chains. The precision to properly characterize the particles is called resolution. The analysis of the resolution in analog spectroscopy systems have been deeply studied in the last four decades. Thus, the resolution is inversely proportional to the noise generated in both, the detectors and associated electronics. This noise may have a different frequency spectrum depending on the components of the detector or the type of chain used. Thanks the development of integrated circuits, digital electronics has been used in particle detection chains, displacing the use of its analog equivalent, with corresponding benefits associated —multistage integration in a single integrated circuit, lower volume and power consumption, reconfigurability, etc.— However, this change of technology, from analog to digital, increases the detection chain complexity and the number of noise sources. This is due to in the basic scheme of a detection chain, two new elements are added: an Analog-to-Digital Converter (ADC) and a digital shaper, both located before the pulse shape analysis stage.This research examines how the digital implementation parameters influence the total resolution spectroscopy, such as the the shaper order, the sampling frequency, quantization noise or shaping type. Besides, different algorithms has been developed in this research to determine the optimal sampling frequency and shaping, for a given energetic particle detection in order to perform a more effective detection. To accelerate the implementation of these algorithms, they have been implemented in hardware using reconfigurable FPGA devices (Field Programmable Gate Array). Other earlier research works can be classified as “basic research” because they were aimed at acquiring new knowledge without including experimental work. However, in this research the different proposed algorithms have been synthesized and tested using FPGAs and therefore it can be considered “applied research”.