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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Photometric Properties of Quiet Sun Observed With the DKIST and HINODE

The variability of irradiance is primarily influenced by surface magnetism, such as sunspots, faculae, and network structures. Each of these features contributes differently to the overall variability. However, the impact of small magnetic elements, which are commonly observed in high resolution images of the solar photosphere but remain unresolved in full-disk images used for irradiance modeling, is still a topic of debate. Understanding the influence of these small magnetic elements to the overall brightness, particularly in quiet regions, is fundamental to understanding irradiance fluctuations over decadal and longer temporal scales. We investigate the brightness of small-size magnetic elements using very high spatial resolution spectro-polarimetric observations acquired at different spectral ranges with the National Science Foundations Daniel K. Inouye Solar Telescope (DKIST) and with HINODE/SOT. Our results are compared to 3D MHD Small Solar Dynamo simulations obtained with the MURaM code.

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NASA NTRSDate not supplied

A Machine Learning Ready Dataset of Acoustic Power Maps for Detection of Active Region Emergence

The development of an accurate forecast for solar eruptive activity has become increasingly important in order to prevent any potential impact on activities in space and the Earth's environment. It is therefore crucial to detect active regions before they appear on the solar surface and create early warning capabilities for upcoming Space Weather disturbances. In this work, 9TB of solar data (SDO/HMI dopplergrams, magnetograms and continuum intensity maps) involving the emergence of 61 NOAA solar active regions since 2010 were processed using the NASA HECC capabilities. An acoustic power maps time-series dataset was created (for four different frequency ranges and processed to take into account the solar sphere geometric effect ) which can be used for understanding the dynamics of the solar surface and train a variety of ML models. The calculated acoustic power maps carry precursor information associated with the decrease in continuum intensity on the solar surface, verifying older helioseismology research. Our results show that a Long Short-Term Memory (LSTMs) model, with a modest layer depth and the right hyperparameters tuned, when trained on this solar acoustic power maps dataset can predict without false negatives a drop in intensity (associated with the emergence of the active region), up to 18 hours in advance.

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