Visual analytics system for understanding DeepRL-based charged particle tracking

doi:10.1007/s00371-024-03297-3

2024-03-18

Raju Ningappa Mulawade *

Christoph Garth

Alexander Wiebel

DOI：10.1007/s00371-024-03297-3

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摘要

En 中文

In this work, we propose a visual analytics system to analyze deep reinforcement learning (deepRL) models working on the track reconstruction of charged particles in the field of particle physics. The data of these charged particles are in the form of point clouds with high-dimensional features. We use one of the existing post hoc saliency methods of explainable artificial intelligence (XAI) and extend its adaptation to compute saliency attributions for the input data corresponding to the output of the model. Our proposed system helps users to explore these saliency attributions corresponding to the high-dimensional input data of the machine learning model and interpret the decision-making process of the model. In particular, we provide the users with multiple task-oriented components, different types of linked views and interactive tools to analyze the model. We explain how to use the system by outlining a typical user workflow and demonstrate the system's usefulness using several case studies which address specific analysis tasks.

Keyword:

Machine learning

Particle physics

Explainability

High-dimensional data

Visual analytics