A computational framework for detection, classification, and visualization of magnetic nulls in multi-spacecraft observations

Abstract

Magnetic nulls, defined as locations where the magnetic field magnitude be comes zero, are theoretically well defined however practically difficult to lo cate, validate, and interpret. To address these challenges, this paper introduces a novel, modular, and fully reproducible automated framework for magnetic null detection, classification, and visualization based on multi-spacecraft observations. The proposed framework consists of two open-source modules: an automated data ingestion and null detection module, and a topological classification and three-dimensional visualization module. Magnetic nulls are detected by combining eigenvalue analysis of the magnetic field gradient tensor with tetrahedron-based geometric validation, enabling both numerical stability assessment and physical consistency checks. Meanwhile, detected nulls are classified into radial (Type A, B) and spiral (Type As, Bs) topologies, and their lo cal magnetic structures are visualized through reconstructed three-dimensional magnetic field lines. The main contribution of this work is the tight integration of detection, numerical validation, classification, and visualization within a single end-to-end pipeline, ensuring consistency between computational output and physical interpretation. The framework is validated using four previously reported electron diffusion region (EDR) events and one storm-time substorm event. The detected null times closely agree with the reported EDR intervals, with several events showing sub-second differences. Among all detected can didates, three nulls satisfy strict numerical validity criteria, including a Type A null, a Type As null, and a Type Bs null.