Description
Within collisionless turbulent plasmas, intense thin current sheets can undergo magnetic reconnection, playing a crucial role in both turbulence dynamics and energy dissipation. The prevalence of magnetic reconnection may be influenced by the properties of the turbulent fluctuations in different environments. The Magnetospheric Multiscale mission (MMS) provides high-resolution, multi-point observations of Earth’s turbulent magnetosheath that are well suited for identifying turbulence-driven magnetic reconnection. However, identifying magnetic reconnection sites is challenging and time-consuming due to the range of scales and complex magnetic field topologies involved. We aim to systematically identify magnetic reconnection events in turbulent plasma observations using an unsupervised Machine Learning (ML) algorithm, Toeplitz Inverse Covariance-Based Clustering (TICC). This method requires key physical features that highlight magnetic reconnection sites as input. TICC clusters timeseries data by modelling each cluster as a time-invariant correlation network, enabling the detection of complex patterns within turbulence. The ability of the method to identify magnetic reconnection events is evaluated against existing datasets of turbulence-driven reconnection. Once an optimal model is established and the cluster corresponding to reconnection sites is identified, the occurrence rate of reconnection is quantified across different turbulent intervals and compared with bulk properties to understand factors controlling its prevalence. Then, the model will be applied to a broader range of turbulent datasets to support preliminary results that suggest a link between reconnection and turbulence parameters such as correlation length and energy dissipation. This study aims to provide key insight into how the role of turbulent plasmas may vary across different turbulent environments.
