Lonigro, Nicola (2019) STRIKE beamlet parameters retreiving via Convolutional Neural Network. [Laurea triennale]
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STRIKE is a diagnostic calorimeter composed of 16 CFC tiles with unidirectional properties used to study the beams of particles generated in the SPIDER experiment. Two thermal cameras will be used to analyze the temperature of the tiles and reconstruct the bidimensional flux of energy striking the calorimeter. Most of the conventional methods used to evaluate the inverse heat flux are unbearably time consuming; since the objective is having a tool for heat flux evaluation for STRIKE real time operation, the need to have a ready-to-go instrument to understand the beam condition becomes stringent. For this reason a neural network was chosen to perform this analysis. During the thesis work an existing convolutional neural network was optimized to retrieve the parameters of the beam from the thermographic images. In particular, starting from a network able to determine the position and the radius of a singular circular shape on a noiseless background, the possibility to recognize the position of bidimensional gaussians was added, needed to determine the beams divergence. The ability to determine the shape,the amplitude and the angle of rotation of the beams was then developed. Lastly the network was optimized to work in a noisy environment and with a non-constant number of gaussians.
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