Uncovering Solar Wind Phenomena with iSAX, HDBSCAN, Human-in-the-loop and PSP Observations

TL;DR

This paper introduces a scalable, interpretable pipeline combining symbolic compression, clustering, and human validation to analyze high-volume Parker Solar Probe data, uncovering known and new solar wind phenomena for improved space weather understanding.

Contribution

It presents a novel integrated framework that efficiently processes large-scale PSP data, systematically uses magnetic deflection angle as a unifying metric, and enhances solar wind event detection and cataloging.

Findings

Successfully processed over 150 GB of data

Recovered known structures and detected new events

Demonstrated robustness across multiple time scales

Abstract

The solar wind is a dynamic plasma outflow that shapes heliospheric conditions and drives space weather. Identifying its large-scale phenomena is crucial, yet the increasing volume of high-cadence Parker Solar Probe (PSP) observations poses challenges for scalable, interpretable analysis. We present a pipeline combining symbolic compression, density-based clustering, and human-in-the-loop validation. Applied to 2018-2024 PSP data, it efficiently processes over 150 GB of magnetic and plasma measurements, recovering known structures, detecting uncatalogued CMEs and transient events, and demonstrating robustness across multiple time scales. A key outcome is the systematic use of the magnetic deflection angle ($\theta_B$) as a unifying metric across solar wind phenomena. This framework provides a scalable, interpretable, expert-validated approach to solar wind analysis, producing expanded event catalogs and supporting improved space weather forecasting. The code and configuration files used in this study are publicly available to support reproducibility.