Characterizing Solar Spicules and their Role in Solar Wind Production using Machine Learning and the Hough Transform

TL;DR

This study uses machine learning and the Hough transform to analyze solar spicules, revealing their rotational behavior and potential role in solar wind acceleration, thereby enhancing understanding of solar wind origins.

Contribution

It introduces a novel application of machine learning and Hough transform to characterize spicules and their dynamics related to solar wind production.

Findings

Rotating spicules are more common at the poles (21%) than the equator (4%).

Rotating spicules contribute to energy transfer in the solar atmosphere.

Connections between spicules, magnetic reconnection, and solar wind acceleration are suggested.

Abstract

Solar winds originate from the Sun and can be classified as fast or slow. Fast solar winds come from coronal holes at the solar poles, while slow solar winds may originate from the equatorial region or streamers. Spicules are jet-like structures observed in the Sun's chromosphere and transition region. Some spicules exhibit rotating motion, potentially indicating vorticity and Alfven waves. Machine learning and the Hough algorithm were used to analyze over 3000 frames of the Sun, identifying spicules and their characteristics. The study found that rotating spicules, accounting for 21 percent at the poles and 4 percent at the equator, play a role in energy transfer to the upper solar atmosphere. The observations suggest connections between spicules, mini-loops, magnetic reconnection, and the acceleration of fast solar winds. Understanding these small-scale structures is crucial for comprehending the origin and heating of the fast solar wind.