Anomaly Detection for GONG Doppler Imagery Using a Binary Classification Neural Network
arxiv(2024)
摘要
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.
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