A key challenge in data quality monitoring in high-energy physics is the need for online monitoring and control of the experiment with the data that is sensitive to underlying conditions and the constantly evolving state of the detector components. Machine learning models can be useful in identifying anomalies in the data and monitoring the quality of the data. At the same time, continuous learning techniques may be necessary to avoid machine learning model sensitivity to changing data inputs, avoiding the need to frequently re-train models. This proposal seeks to address this challenge by exploring continuous learning models capable of adapting to changing detector conditions and systems over time.
Total project length: 175 hours.
C++, Python, PyTorch, Tensorflow, previous experience in Deep Learning.
Medium
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