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Mitchell Creelman

Publications and source records attributed to Mitchell Creelman.

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Improvements to the NSO Farside Mapping Pipeline: Noise Reduction Updates

The National Solar Observatory (NSO)'s Farside Pipeline is a critical tool of the space weather industry. It enables the detection and tracking of solar active regions that have rotated to the farside (invisible surface) of the Sun without relying on direct observational platforms such as satellites. By applying the technique of helioseismic holography to continuous Doppler images of the front side (visible surface), the pipeline infers the size and location of these regions through the acoustic signatures. These farside maps, produced using data from the NSO's GONG Network, allow scientists and solar observers to monitor the behavior of solar active regions. They support efforts to protect vital telecommunications and national interest infrastructure. While the data from this pipeline are widely used to many scientific, industrial, and national security applications, global helioseismic monitoring remains a developing field, with ongoing refinements in methodology and reliability. In this report, we will outline the updates made to the NSO's Farside Pipeline which have resulted in more accurate and consistent helioseismic maps, strengthening its value for both operational forecasting and scientific research.

astro-ph.SR

Anomaly Detection for GONG Doppler Imagery Using a Binary Classification Neural Network

One of the products of the National Solar Observatory's Integrated Synoptic Program (NISP) is the farside seismic map which shows the magnetic activity on the unobserved side of the Sun. The production of these rudimentary maps began in 2006, and they have since proven to be a valuable tool in tracking solar activity which cannot be directly observed from the earth's surface. The continuous tracking of solar active regions allows space weather forecasters to monitor critical solar events which may have larger economic and societal impacts here on Earth. In an effort to improve these maps, several steps are underway through the Windows on the Universe project (WoU) funded by the NSF. One of these steps is to improve the quality assurance measures for the images collected at individual sites throughout the GONG network and is used to develop the farside maps. To this end, we have designed a binary classification neural network to determine which of these site images should and should not be included in the farside pipeline that produces the end product maps. This convolutional neural network is a highly effective and computationally efficient method of significantly improving the quality of the farside maps currently produced by the NISP program.

astro-ph.SR