Machine Learning (ML) represents cutting-edge data processing technologies in the field of information science. They are expected to enable various data processing tasks, as they can build a data “model” (a description of the relationship between input and output variables) using information derived from vast amounts of data, even without a pre-existing, explicit model.
Since various recent ML techniques provide more effective and precise data processing in accelerator physics experiments, we formed a group with information scientists to apply ML to particle accelerator physics as an RCNP research project in Osaka in 2018.
In this seminar, some of our ML application activities in accelerator tuning, physics analysis, and data calibration will be introduced.