Galaxy morphologies play an essential role in the study of the evolution of galaxies. The determination of morphologies is laborious for a large amount of data giving rise to machine learning-based approaches. Unfortunately, most of these approaches offer no insight into how the model works and make the results difficult to understand and explain. We here propose to extend a classical encoder-decoder architecture with invertible flow, allowing us to not only obtain a good predictive performance but also provide additional information about the decision process with counterfactual explanations.

翻译：星系形态在星系演化研究中起着至关重要的作用。面对海量数据，形态的确定工作繁重，这催生了基于机器学习的方法。然而，这些方法大多无法揭示模型的工作原理，导致结果难以理解和解释。本文提出在经典的编码器-解码器架构中引入可逆流模型进行扩展，该方案不仅能获得良好的预测性能，还能通过反事实解释为决策过程提供额外信息。