Searching 13 Million Light Curves for Coronal Dimming

When coronal mass ejections (CMEs) depart the corona, they leave behind a transient void. Such a region evacuated of plasma is known as a coronal dimming and it contains information about the kinetics of the CME that produced it. The dimming can be so great in the extreme ultraviolet (EUV) that it reduces the overall energy output of the sun in particular emission lines, i.e., dimming is observable in spectral irradiance. The Solar Dynamics Observatory (SDO) EUV Variability Experiment (EVE) data provide an excellent opportunity to search for and parameterize dimming. We focus our search on the 39 extracted emission lines data product. We search these light curves for dimming around all of the >8,500 ≥C1 solar flares observed by the Geostationary Operational Environmental Satellite (GOES) X-ray Sensor (XRS) in the SDO era. In prior work, we have found that it is important to remove the gradual flare phase from dimming light curves in order to obtain slopes and magnitudes that are consistent with what can be obtained by spatially isolating flaring loops in spectral image data. To do this, we peak-match and subtract two different emission line light curves. In this exhaustive search for dimming, we therefore consider every permutation of the 39 emission lines as well as the “uncorrected” light curves, resulting in 1,521 light curves for every ≥C1 solar flare. Thus, we come to a total of ~13 million light curves in which to search for dimming.

Here, we describe the feature detection and characterization algorithms developed and applied to the 13 million EUV irradiance light curves. Machine learning techniques have been used for both this backend processing pipeline and to analyze the results. All of the code is open source python available on GitHub (https://github.com/jmason86/James-s-EVE-Dimming-Index-JEDI).