The MoEDAL experiment at CERN (https://home.cern/science/experiments/moedal-mapp) carries out searches for highly ionising exotic particles such as magnetic monopoles. One of the technologies deployed in this task is the Nuclear Track Detector (NTD). In the form of plastic films, these are passive detectors that are low cost and easy to handle. After exposure to the LHC collision environment in the LHCb cavern at point 8 on the LHC ring, they are etched and scanned under a microscope to potentially reveal the etch-pit signature of the passage of an exotic highly ionising particle. The scanning process takes place using microscopes and expert human inspection. With several 10s of metres squared of deployed plastic, and large backgrounds complicating the analysis, the process is highly time consuming.
We have studied the use of AI to identify etch-pits in scanned images of NTDs. A specially prepared stack of NTD plastic films – where one layer has been exposed to the harsh LHC environment and the others have not – is placed in a heavy ion beam to simulate the passage of particles such as magnetic monopoles. The plastic is then etched and optically scanned. The images are used to prepare training and evaluation data sets for three different approaches: a deconvolution-convolution algorithm with machine learning based thresholding, a convolutional neural network, trained as a classifier and then used in a fully convolutional mode, and a convolutional neural network making use of a U-Net based technique.
We present an overview of MoEDAL and our study, the evaluation of the methods, and the prospects for further uses of AI in this area.
|Consider for long presentation||Yes|