SoFTg: A hybrid segmentation framework for solar granulation revealing turbulent convection statistics at DKIST scales

In this work, we present Solar Feature Tracking: granules (SoFTg), a new hybrid algorithm within the SoFT library designed for the segmentation and tracking of solar granulation. By combining a heuristic approach based on a custom adaptive-thresholding technique with the speed and robustness of deep-learning methods, SoFTg provides a fast, reliable and user-friendly solution for granule segmentation, followed by the standard SoFT approach for tracking. We present a comprehensive evaluation of the algorithm's performance using multiple metrics and analyse the different stages of the processing pipeline. The method is tested on three datasets acquired with the Visible Broadband Imager operating in the red continuum at the Daniel K. Inouye Solar Telescope in Maui, Hawaii. We present distributions of both the lifetime and equivalent diameter of the thousands of granules tracked, providing, for the first time, statistically robust evidence of turbulent convection at the spatial scales now accessible through the high-resolution imaging capabilities of DKIST.