SoFT: Detecting and tracking magnetic structures in the solar photosphere

¹ University of Trento, Via Calepina 14, 38122 Trento, Italy
² University of Rome Tor Vergata, Department of Physics, Via della Ricerca Scientifica 3, 00133 Rome, Italy
³ ASI Italian Space Agency, Via del Politecnico snc, 00133 Rome, Italy
⁴ University of Rome “La Sapienza”, Department of Physics, P.le A. Moro 5, 00185 Rome, Italy
⁵ Astrophysics Research Centre, School of Mathematics and Physics, Queen’s University Belfast, Belfast, BT7 1NN, Northern Ireland, UK
⁶ Department of Physics and Astronomy, California State University Northridge, Northridge, CA 91330, USA
⁷ Max Planck Institute for Solar System Research, Justus-von-Liebig-Weg 3, 37077 Göttingen, Germany

^★ Corresponding author; michele.berretti@unitn.it

Received: 18 October 2024
Accepted: 7 December 2024

Abstract

In this work, we present Solar Feature Tracking, a novel feature-tracking tool developed in Python and designed to detect, identify, and track magnetic elements in the solar atmosphere. It relies on a watershed segmentation algorithm to effectively detect magnetic clumps within magnetograms, which are then associated across successive frames to follow the motion of magnetic structures in the photosphere. Here, we study its reliability in detecting and tracking features under different noise conditions starting with real-world data observed with SDO/HMI and followed with simulation data obtained from the Bifrost numerical code to better replicate the movements and shape of actual magnetic structures observed in the Sun’s atmosphere within a controlled noise environment.