用模糊推理+深度学习提升射电天文数据处理的可解释性
Explainable machine learning workflows for radio astronomical data processing
- 结合TSK模糊系统与深度学习实现可解释的自动化处理
- 仿真结果表明准确率不降，决策过程清晰可读
- 适合需理解算法逻辑的天文学家和数据工程师
射电天文学依赖高效精准的数据处理流程来产出科学可用数据。随着现代射电望远镜数据量激增，人工配置处理流程已不可行。机器学习（ML）正成为自动化处理流程的可行方案。然而，现有大多数基于ML的流程均为黑箱模式，其决策难以被天文学家理解。为提升射电天文领域ML辅助流程的可解释性，我们提出联合使用模糊规则推理与深度学习的方法。以射电天文中的校准任务为例，采用Takagi-Sugeno-Kang（TSK）模糊系统展示该方法在ML辅助决策中的应用。基于仿真结果表明，该方法在保持质量与准确性的同时，显著提升了决策过程的可解释性。
原文摘要 · Abstract (English)
Radio astronomy relies heavily on efficient and accurate processing pipelines to deliver science ready data. With the increasing data flow of modern radio telescopes, manual configuration of such data processing pipelines is infeasible. Machine learning (ML) is already emerging as a viable solution for automating data processing pipelines. However, almost all existing ML enabled pipelines are of black-box type, where the decisions made by the automating agents are not easily deciphered by astronomers. In order to improve the explainability of the ML aided data processing pipelines in radio astronomy, we propose the joint use of fuzzy rule based inference and deep learning. We consider one application in radio astronomy, i.e., calibration, to showcase the proposed approach of ML aided decision making using a Takagi-Sugeno-Kang (TSK) fuzzy system. We provide results based on simulations to illustrate the increased explainability of the proposed approach, not compromising on the quality or accuracy.
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