Connecting Solar Physics Past to Its Machine Learning Future

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People have been studying the Sun for hundreds of years and drawing maps of what they see, the earliest dating back almost 900 years. These hand-drawn maps of the solar surface have been invaluable for modern-day physicists to understand how the Sun changes over the decades. Between 1964 and 2009, Dr. Patrick McIntosh made his own carefully constructed maps of the Sun, documenting many interesting solar features and magnetic fields. However, since the last maps were made in 2009, McIntosh’s archive has languished and has not been updated. The purpose of this 3-year project is to use the latest space- and ground-based images of the Sun to reproduce the McIntosh’s techniques, update the archive through today, and develop tools for current machine learning algorithms to produce these maps going into the future.All solar magnetic fields are fundamentally driven by an internal dynamo. To study this pervasive magnetic field, scientists rely on indirect measurements of visible plasma features at the surface that are produced by the evolving magnetic field. Large-scale atmospheric features, such as filaments, active regions, sunspots, polarity boundaries, and coronal holes are distinct signatures of the ever-changing dynamo. These visible features were captured by Dr. Patrick McIntosh from 1964 through 2009 in hand-drawn maps of the Sun. This 3-year project aims to preserve multiple decades of accumulated specialized knowledge of the Sun, thus opening up new avenues for studies of the solar cycle by linking the McIntosh-identified solar features to modern images of the Sun through reproducing his techniques with digital tools. The result of this effort is a new archive of solar maps to effectively extend the existing catalogue to cover solar cycle 24. The catalogue will be open for public download and well documented for supervised machine learning tools to take advantage of this unique labeled data representing over 55 years of solar measurements. The research and EPO agenda of this project supports the Strategic Goals of the AGS Division in discovery, learning, diversity, and interdisciplinary research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.