Solar Particle Event Dose Forecasting Using Regression Techniques. Issue 8 (18th August 2018) - Lovelace, Alan Mitchel; Rashid, Al Maqsudur; de Wet, Wouter C.; Townsend, Lawrence W.; Wesley Hines, J.; Moussa, Hanna
Abstract: Doses from solar particle events can be a serious threat to the wellbeing of crews traveling through space. Therefore, methods for predicting the time such events will take place, methods for forecasting the dose buildup over time, and methods for forecasting the potential total dose from such events are needed to enable crews to take actions to mitigate the effects by entering a shielded area designed for their protection. This work focuses on forecasting the total dose expected for an event, based upon doses obtained very early in the event, using the kernel regression method. The model uses tables of calculated doses for historical solar particle events augmented with hypothetical events similar to the actual ones for training purposes. Reasonably accurate predictions of the total dose expected for an event can be made within the first hour after event onset. Predictive accuracies generally increase as the event progresses in time. The only inputs required are doses and times since event onset as provided by dosimetry devices. One hundred thirteen actual events with total doses between 1 and 1, 000 cGy were tested using the model. At 1 hr into the event, total dose predictions were within ±30% of the actual total doses for 91 events (81%) and within ±15% for 54 of them (48%). Within the first 4 hr following event onset, total dose predictions were within ±30% for 98 events (87%) and within ±15% for 66 of them (58%). A software package implementing the model has been provided to the Space Radiation Analysis Group at NASA Johnson Space for incorporation into their operational procedures for analyzing possible threats to space crews from solar particle events. Key Points: A model utilizing Weibull distributions and regression techniques has been developed for forecasting dose from a solar particle event as the event begins The kernel regression method is used to train the dose forecasting model using dose buildup over time data The model reasonably predicts dose buildup as early as the first hour after event onset, and accuracies generally increase as the event progresses