Speaker
Description
In High Energy Physics (HEP), new discoveries can be enabled by the development of new experiments and the construction of new detectors. Nowadays, many experimental projects rely on the deployment of new detection technologies to build large scale detectors. The validation of these new technologies and their large scale production require an extensive effort in terms of Quality Control.
In order to improve the reliability and efficiency of the Quality Control (QC) of new detector components, We propose a new framework based on advanced Machine Learning techniques. Our efforts are focused on the Visual Inspection of such components to help prevent future failures and improve fabrication processes. Our tool aims to combine two complementary algorithms based on Anomaly Detection techniques and Computer Vision algorithms to facilitate the identification of a wide range of defects. This framework has been tested in the context of the production of pixel modules for the new Inner Tracker (ITk) to be deployed in the ATLAS detector for the High Luminosity upgrade. We will show the current development status and successful integration to the QC procedure of the pixel module produced in Japan.
| Would you like to be considered for an oral presentation? | Yes |
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