Event hazard function learning and survival analysis for tearing mode onset characterization.

It is shown that concepts from survival analysis (branch of statistics dealing with various types of time-to-event data) are helpful when trying to quantify and understand the onset of tearing modes in tokamaks. It is argued that a probabilistic event prediction problem should be decomposed into (i) dynamical system evolution and (ii) event hazard function integration. Successful machine learning of a hazard (events per time) function from experimental data is demonstrated. The hazard function exhibits statistical properties that are consistent with expectation. A specific tearing delta-prime proxy is found to not contribute to the likelihood of the hazard function for the present case. Although in this paper the event is the onset of a tearing mode in a particular plasma scenario, these ideas should be equally applicable to disruption events. [ABSTRACT FROM AUTHOR]