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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Intense High-Energy ESP Events and Self-Excited Waves at Inner Heliospheric Distances

Energetic Storm Particle (ESP) events are particle intensity increases associated with the passage of interplanetary (IP) shocks. Although ESP events at 1 AU are usually a low-energy phenomenon (i.e., at proton energies <10 MeV), occasionally they may reach energies of >100 MeV. An analysis of the ESP events observed at proton energies >40 MeV over the last four solar cycles shows that these high-energy particle intensity increases tend to occur when unrelated IP structures such as intervening coronal mass ejections and other unlinked shocks are present in the solar wind through which the IP shock generating the ESP event propagates. The acceleration of particles by IP shocks becomes more efficient when particles repeatedly interact with the traveling shocks. These multiple interactions may be favored by either the effects that these unrelated IP structures produce in the particle transport, or by amplified magnetic field fluctuations excited by the accelerated particles. However, the search for these self-excited waves at IP shocks at 1 AU has been often unsuccessful, with just a handful of events showing enhanced field fluctuations that can resonate with the energetic particles. Modeling results indicate that particle acceleration to the highest energies by the shocks occurs close to the Sun, resulting in large intensities of self-excited waves. We have initiated a search for ESP events in the inner heliosphere with evidence for self-excited waves and few cases observed by Parker Solar Probe will be shown.

David Lario↗
NASA NTRSDate not supplied

Solar Particle Event Modeling Compared To Satellite Data

Solar protons are the primary source of ionizing radiation for certain missions. Several models of these solar energetic proton events exist, giving different results. We compare three models with satellite data to assess these differences.

Alexander Henderson↗
NASA NTRSDate not supplied

Fast Machine Learning Lidar Surrogate Simulator: Pristine Clear Sky

The simulations of lidar signals and retrievals rely on a range of optic-physical models, such as radiative transfer models, particle scattering and absorption models, along with the output data from atmospheric physical models. Integrating these different models to represent signals of a lidar system is computationally expensive, and performing backward retrievals can be complex and ambiguous. However, with the advantages of Machine Learning, there is a new potential for building effective lidar signal database linked to corresponding atmospheric profiles. For this project, we are developing a fast pre-trained neural network as the lidar surrogate simulator using simulated data for a CALIPSO-like lidar (355 nm, 532nm, and 1064nm), and a CO2 differential absorption lidar (DIAL) near 1571nm. Specifically, we utilize a long short-term memory (LSTM) model to map the relationships between atmospheric profiles (pressure, temperature, air density and CO2 mixing ratio) and lidar signals. This approach allows us to build machine learning based simulators that can reconstruct lidar signals at specific bands from MERRA reanalysis data, and perform retrievals of atmospheric profiles using lidar signals at various wavelengths. As a first step, the results show the potential of this method to establish a foundational model for sensor signals. This model offers the promise of enabling both accurate predictions and rapid retrievals, providing a more efficient approach to signal processing and analysis.

Shan Zeng↗