LAUSR

Abstract Radioactive particle tracking (RPT) is a non-invasive technique used to monitor opaque multiphase flow systems. Achieving highly accurate particle tracing is challenging and time-consuming because of the need to build a new RPT model from calibration data each time the experimental conditions change. This paper aims to examine if RPT calibration data under previous conditions can be leveraged with the help of transfer learning (TL) when creating an RPT model for a new condition. Several TL strategies for exploiting historical calibration data are evaluated in conjunction with Geant4 simulations to understand their applicability to RPT. The results show that when it is impractical to collect a lot of calibration data, TL is often superior to training an RPT model only on new data. Moreover, when new calibration data collection is not feasible, an RPT model trained on the historical data can be very accurate if the new condition is sufficiently similar to the historical conditions.