Identifying Spicules in Mg II: Statistics and Comparisons with H{\alpha}

TL;DR

This paper develops an automated method to identify and analyze solar spicules in Mg II spectral data, enabling comprehensive statistical studies of their properties and role in solar energy transfer.

Contribution

It introduces a numerical definition for Mg II spicules, an algorithm for automatic detection, and uses clustering to classify spicule spectral shapes, advancing analysis in the upper chromosphere.

Findings

Defined numerical criteria for Mg II spicule identification

Developed an automatic detection algorithm

Applied clustering to classify spectral shapes

Abstract

The Sun's chromosphere is a critical region to understand when considering energy and mass deposition into the transition region and corona, but many of the smaller, faster events which transport a portion of this mass and energy are still difficult to observe, identify and model. Solar Spicules are small, spike-like events in the solar chromosphere that have the potential to transfer energy and mass to the transition region, but whose energetic origins are still being researched. Chromospheric spicule activity on-disk can be identified by observing temporary excursions in the red and blue wings of chromospheric emission lines. Researchers have demonstrated this in Hydrogen~Alpha (H{\alpha}, 6563 {\AA}), Ca II (8542 {\AA}, k 3934 {\AA}), Mg II (h 2803 {\AA}, k 2796 {\AA}), and Si IV (1394 {\AA}, 1405 {\AA}) spectral observations, with the vast majority of identification efforts focused on lower chromospheric observations of H$\alpha$ and Ca II. Because any spicules which deposit mass and energy into the transition region must necessarily pass through the upper chromosphere, observations from this region such as Mg II or Hydrogen Lyman Alpha (Ly$\alpha$ 1216 {\AA}) in enough quantity to perform proper statistics will be critical to fully characterizing spicules' impact on mass and energy transfer in the Sun. This research proposes a definition with numerical limits for how spicules appear in Mg II wavelengths, tunes an algorithm for automatically detecting spicules in Mg II spectral observations, and uses K Means Clustering to identify and display the full range of spicule spectrum shapes. This work will help allow statistical studies on spicules in the upper chromosphere to be as thorough as those of the lower chromosphere, allowing researchers to better understand the physical nature of spicules and their role in energy transfer and deposition in the solar atmosphere.