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Studying coronal mass ejections (CMEs) in coronagraph data can be challengingdue to their diffuse structure and transient nature, compounded by thevariations in their dynamics, morphology, and frequency of occurrence. Thelarge amounts of data available from missions like the Solar and HeliosphericObservatory (SOHO) make manual cataloging of CMEs tedious and prone to humanerror, and so a robust method of detection and analysis is required and oftenpreferred. A new coronal image processing catalog called CORIMP has beendeveloped in an effort to achieve this, through the implementation of a dynamicbackground separation technique and multiscale edge detection. These algorithmstogether isolate and characterise CME structure in the field-of-view of theLarge Angle Spectrometric Coronagraph (LASCO) onboard SOHO. CORIMP also appliesa Savitzky-Golay filter, along with quadratic and linear fits, to theheight-time measurements for better revealing the true CME speed andacceleration profiles across the plane-of-sky. Here we present a sample of newresults from the CORIMP CME catalog, and directly compare them with the otherautomated catalogs of Computer Aided CME Tracking (CACTus) and Solar EruptiveEvents Detection System (SEEDS), as well as the manual CME catalog at theCoordinated Data Analysis Workshop (CDAW) Data Center and a previouslypublished study of the sample events. We further investigate a form ofunsupervised machine learning by using a k-means clustering algorithm todistinguish detections of multiple CMEs that occur close together in space andtime. While challenges still exist, this investigation and comparison ofresults demonstrates the reliability and robustness of the CORIMP catalog,proving its effectiveness at detecting and tracking CMEs throughout the LASCOdataset.

Investigating the Kinematics of Coronal Mass Ejections with the Automated CORIMP Catalog