Sprecher
Beschreibung
The High Power Laser System (HPLS) at ELI-NP / IFIN-HH operation produces a large quantity of data. The laser beam profile images collected from the diagnostics bench help characterize the quality of the system’s operation. The present work focuses on the problem of image classification in order to augment the beam profile qualitative analysis. This leads to the application of machine learning models to various data provided by the laser systems used at the ELI-NP facility. In this sense, the proposed approach focuses on a comparison of machine learning algorithm’s performances on the problem of abnormality detection on laser beam profiles. The main goal of the study is to deliver a reliable classification model by creating and comparing different models based on supervised machine learning methods. For this, classification models based on convolutional neural networks and support vector machines were trained and validated on datasets consisting of images recorded by the system’s benchmark cameras during operation time, resulting in three models with a validated accuracy of over 90%. These classifiers were then compared based on performance metrics selected to fit the studied problem. The results of this comparison brought forward a laser beam profile classifier with performances such as accuracy, f-score and true positives rate of over 95%. The output of the classifier can help laser system operators in the process of better aligning and focusing the laser beam.
