SoFT: Detecting and Tracking Magnetic Structures in the Solar Photosphere

TL;DR

This paper introduces SoFT, a Python-based tool for detecting and tracking magnetic structures in the solar photosphere, validated on real and simulated data to ensure robustness under various noise conditions.

Contribution

It presents a novel feature-tracking method using watershed segmentation for magnetic elements in solar magnetograms, applicable to real and simulated data.

Findings

Effective detection of magnetic clumps in magnetograms.

Reliable tracking of magnetic structures across frames.

Robust performance under different noise levels.

Abstract

In this work, we present SoFT: 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.