Applying machine learning to determine impact parameter in nuclear physics experiments

Understand

Machine Learning (ML) algorithms have been demonstrated to be capable of predicting impact parameter in heavy-ion collisions from transport model simulation events with perfect detector response.

• We extend the scope of ML application to experimental data by incorporating realistic detector response of the S$\pi$RIT Time Projection Chamber into the heavy-ion simulation events generated from the UrQMD model to resemble experimental data.
• At 3 fm, the predicted impact parameter is 2.8 fm if simulation events with perfect detector is used for training and testing; 2.4 fm if detector response is included in the training and testing, and 5.8 fm if ML algorithms trained with perfect detector is applied to testing data that has included detector response.
• The last result is not acceptable illustrating the importance of including the detector response in developing the ML training algorithm.