Counting neutrons with a commercial S-CMOS camera

It is possible to detect individual flashes from thermal neutron impacts in a ZnS scintillator using a CMOS camera looking at the scintillator screen, and off line image processing. Some preliminary results indicated that the efficiency of recognition could be improved by optimizing the light collection and the image processing. We will report on this ongoing work which is a result from the collaboration between ESS Bilbao and the ILL. The main progress to be reported is situated on the level of the on-line treatment of the imaging data. If this technology is to work on a genuine scientific instrument, it is necessary that all the processing happens on line, to avoid the accumulation of large amounts of image data to be analyzed off line. An FPGA-based real-time full-deca mode VME-compatible CameraLink board has been developed at the SCI of the ILL, which is able to manage the data flow from the camera and convert it in a reasonable “neutron impact” data flow like from a usual neutron counting detector. The main challenge of the endeavor is the optical light collection from the scintillator. While the light yield of a ZnS scintillator is a priori rather important, the amount of light collected with a photographic objective is small. Different scintillators and different light collection techniques have been experimented with and results will be shown for different setups improving upon the light recuperation on the camera sensor. Improvements on the algorithm side will also be presented. The algorithms have to be at the same time efficient in their recognition of neutron signals, in their rejection of noise signals (internal and external to the camera) but also have to be simple enough to be easily implemented in the FPGA. The path from the idea of detecting individual neutron impacts with a CMOS camera to a practical working instrument detector is challenging, and in this paper we will give an overview of the part of the road that has already been walked

A Comparison of Parametric Classification Procedures of Remotely Sensed Data Applied on Different Landscape Units

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