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Results for “Solar energetic particles”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

On the sources of solar energetic particles

We have examined the time histories of energetic (greater than 1 MeV) particles as detected by instruments in the earth's neighborhood over an 18 year period commencing mid-1967. The majority (greater than 75 percent) of the events extending to proton energies above 20 MeV have their origins in a flare event which includes H-alpha emission, soft x rays, and metric radio bursts of Type 2 and/or Type 4. We have assembled a list of 241 events for which the sources are thus well identified. Two further particle increases have been associated with nonflare events. Of the 82 events originating in regions to the east of central meridian, the sources of 68 (83 percent) were sufficiently energetic that they also generated interplanetary shocks detected at earth. We suggest that shocks are responsible for particles being detectable from source regions not magnetically connected to earth.

Cane, H. V↗
NASA NTRS2008

Models for Galactic Cosmic Ray and Solar Energetic Particles and their Application to Spacecraft Design

The effects of radiation environment in interplanetary space must be taken into account for spacecraft design. This is done by modeling this environment and propagating it to the electronic parts of interest within the spacecraft then calculating the effects of this radiation on these parts. This talk will present a survey of the existing models for the interplanetary radiation environment and the results of comparing them with measurements. It will also include a survey of radiation transport methods and methods for estimating the effects of this radiation on spacecraft.

Adams, James H., Jr.↗
NASA NTRS2023

Using Random Hiveminds to Predict Solar Energetic Particles (SEPs)

The Problem: The use of conventional neural networks (CoNNs) to predict SEPs has become popular, but neural network models do not follow one-size-fits-all approaches and their chaotic natures can yield completely different results on identical data sets. Committees of neural networks identical in input features have been used to solve this problem by (Aminalragia et al., 2021), but they have the possibility of all agreeing together in lockstep and missing crucial information. The Solution: (O’Keefe et al., 2023) propose a solution consisting of neural network estimators in an ensemble, but with features randomly removed from them in a layout known as a random hivemind (RH). The decision weight, learning rate, and epoch count of each member in this ensemble are boosted in relation to how well its individual features perform in a chi-square test.

SMD↗