Outlier classification using Autoencoders: application for fluctuation driven flows in fusion plasmas

TL;DR

This paper presents a novel autoencoder-based method for classifying outliers in plasma fluctuation data, improving the accuracy of heat flux measurements in fusion plasma boundary studies.

Contribution

It introduces an autoencoder approach to identify outliers in plasma data without relying on predefined thresholds, enhancing data analysis robustness.

Findings

Outlier removal reduces heat flux estimates by 5-15%.

Triple correlation contributions decrease by up to 40%.

The method improves classification accuracy over threshold-based approaches.

Abstract

Understanding the statistics of fluctuation driven flows in the boundary layer of magnetically confined plasmas is desired to accurately model the lifetime of the vacuum vessel components. Mirror Langmuir probes (MLPs) are a novel diagnostic that uniquely allow to sample the plasma parameters on a time scale shorter than the characteristic time scale of their fluctuations. Sudden large-amplitude fluctuations in the plasma degrade the precision and accuracy of the plasma parameters reported by MLPs for cases in which the probe bias range is of insufficient amplitude. While some data samples can readily be classified as valid and invalid, we find that such a classification may be ambiguous for up to 40% of data sampled for the plasma parameters and bias voltages considered in this study. In this contribution we employ an autoencoder (AE) to learn a low-dimensional representation of valid data samples. By definition, the coordinates in this space are the features that mostly characterize valid data. Ambiguous data samples are classified in this space using standard classifiers for vectorial data. This way, we avoid to define complicate threshold rules to identify outliers, which requires strong assumptions and introduce biases in the analysis. Instead, these rules are learned from the data by statistical inference By removing the outliers that are identified in the latent low-dimensional space of the AE, we find that the average conductive and convective radial heat flux are between approximately 5 and 15% lower as when removing outliers identified by threshold values. For contributions to the radial heat flux due to triple correlations, the difference is up to 40%.