用可解释的稀疏特征网络分析星系图像，精准预测演化物理参数
Insights on Galaxy Evolution from Interpretable Sparse Feature Networks
- 设计稀疏特征网络，将像素特征线性组合以推断星系属性
- 在星系光谱比值与气体金属丰度预测上性能媲美顶尖模型
- 适合需要物理可解释性的天文机器学习研究者使用
星系外观揭示了其形成与演化过程中的物理机制。如今机器学习模型能利用星系丰富的形态信息，直接从图像切片中预测物理属性。理解像素级特征与星系属性之间的关系，对建立星系演化物理认知至关重要，但现有深度神经网络的特征表示仍缺乏可解释性。为解决此问题，本文提出新型神经网络架构——稀疏特征网络（SFNet）。SFNets生成可解释的特征，这些特征可通过线性组合估计星系性质，如光学发射线比或气体相金属丰度。实验表明，SFNets在保持可解释性的同时未牺牲精度，在天文学机器学习任务中表现与前沿模型相当。该方法有助于在大规模数据集中发现物理规律，并帮助天文学家解读机器学习结果。
原文摘要 · Abstract (English)
Galaxy appearances reveal the physics of how they formed and evolved. Machine learning models can now exploit galaxies' information-rich morphologies to predict physical properties directly from image cutouts. Learning the relationship between pixel-level features and galaxy properties is essential for building a physical understanding of galaxy evolution, but we are still unable to explicate the details of how deep neural networks represent image features. To address this lack of interpretability, we present a novel neural network architecture called a Sparse Feature Network (SFNet). SFNets produce interpretable features that can be linearly combined in order to estimate galaxy properties like optical emission line ratios or gas-phase metallicity. We find that SFNets do not sacrifice accuracy in order to gain interpretability, and that they perform comparably well to cutting-edge models on astronomical machine learning tasks. Our novel approach is valuable for finding physical patterns in large datasets and helping astronomers interpret machine learning results.
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