Speaker
Description
This work explores the application of machine learning methods to predict the luminosity of the VEPP-4M electron-positron collider. Historical data collected during operation are used to train and evaluate several machine learning models. A comparative analysis is conducted to assess the performance of different modeling approaches. The study aims to investigate whether data-driven methods can effectively capture the complex relationships between collider conditions and luminosity. The results indicate that machine learning can serve as a complementary tool for understanding and monitoring collider behavior. This approach is relevant in the context of growing interest in automation, instant diagnostics and predictive analytics in accelerator operations.
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