Coronal jet Identification with machine learning

Coronal jets are narrow eruptions observable across various wavelengths, primarily driven by magnetic activity. These phenomena may play a pivotal role in solar activity, which significantly impacts the dynamics of the solar system, however they have not been studied in depth thus far. This work employs machine learning, specifically, via a random forest model, to enhance the assembly of the dataset of coronal jets. By combining data from two segmentation methods, semi-automated jet identification algorithm (SAJIA) and mathematical morphology (MM), we strove to develop a more comprehensive dataset. Our model was trained and validated initially on a robust dataset and subsequently applied to classify unlabelled data. To ensure a higher level of confidence for positive identifications, the classification threshold was increased to 0.95. This adjustment led to the identification of 3452 new jet candidates. The new candidates were then validated through visual inspection. The validation resulted in the identification of 3268 true jets and 184 false positives. Our findings highlight the e ectiveness of integrating machine learning with traditional analysis techniques to enhance the accuracy and reliability of solar jet identification. These results contribute to a deeper understanding of coronal jets and their role in solar dynamics, demonstrating the potential of machine learning in advancing solar physics research.