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The effects of uncertainties in published proton fluence spectra for large solar particle events (SPE) on organ dose estimates are largely unknown since uncertainties in the measured spectra are unknown. In this work, input spectra for several large SPEs are adjusted by as much as 50% to account for the spectrum uncertainties. The BRYNTRN space radiation transport code and CAM human geometry model are used to perform the calculations. The calculations are made assuming three organ doses and four nominal thicknesses of spacecraft aluminum shielding. Discussions of dose variations for several events based on different spectrum uncertainty values are presented.

Calculations of solar energetic particle event (SPE) doses typically utilize SPE proton spectra parameterized with either an exponential in rigidity (momentum per unit charge) or a Weibull form in energy. In this work we report organ doses calculated using these two different parameterizations of proton spectra of four large solar energetic particle events. They are the SPEs of August 4, 1972, August 12, 1989, September 29, 1989 and October 19, 1989. The variations in predicted doses to critical organs introduced by the use of these two parameterizations for these large events could be a factor in evaluating the effectiveness of spacecraft shielding. Events similar to the largest SPEs observed during the space age could deliver large organ doses and the potential for an acute radiation syndrome response in interplanetary crews.

When planning space missions, radiation effects due to large solar particle events (SPEs) can become a major concern since doses can become mission threatening to both the crew and the spacecraft electronic components. As mission duration increases, the possibility that a significant dose is delivered also increases, especially during the more active parts of the solar cycle. Therefore, a method of predicting when certain limiting doses will be reached following the onset of a large SPE needs to be available. Typical dose versus time profiles of a SPE can be represented by a Weibull functional form, which is comprised of three unknown parameters. Since these dose-time profiles are nonlinear functions, the use of artificial neural networks as the forecasting mechanism is ideal.