一种高效的星系形态无监督分类模型：基于ConvNeXt大模型编码的投票聚类
An efficient unsupervised classification model for galaxy morphology: Voting clustering based on coding from ConvNeXt large model
AI总结：
本文提出一种基于ConvNeXt大模型编码、PCA降维和bagging投票聚类的无监督星系形态分类方法，在COSMOS场数据上将聚类组数从100降至20，分类约53%星系，显著提升效率并通过参数测试验证了有效性。
摘要：
在这项工作中，我们通过提出一种基于ConvNeXt大模型编码的算法来更新无监督机器学习（UML）步骤，以提高无标签星系形态分类的效率。该方法可以概括为以下三个关键方面：（1）使用卷积自编码器进行图像去噪和重建，并通过极坐标扩展提高模型的旋转不变性；（2）利用名为ConvNeXt的预训练卷积神经网络（CNN）对图像数据进行编码，特征通过主成分分析（PCA）降维进一步压缩；（3）采用基于bagging的多模型投票分类算法以增强鲁棒性。我们将该模型应用于COSMOS场中$I_{\ m mag}< 25$的星系样本的I波段图像。与原始无监督方法相比，新方法所需的聚类组数从100减少到20。最终，我们成功分类了约53%的星系，显著提高了分类效率。为验证形态分类的有效性，我们选取了$M(*)>10^{10}(M(sun))$的大质量星系进行形态参数测试。分类结果与星系物理性质在多个参数面上的对应关系与现有演化模型一致。我们的方法证明了大模型编码用于星系形态分类的可行性，不仅提高了星系形态分类的效率，而且节省了时间和人力。此外，与原始UML模型相比，增强的分类性能在定性分析中更加明显，并成功通过了更多数量的参数测试。
英文摘要：
In this work, we update the unsupervised machine learning (UML) step by proposing an algorithm based on ConvNeXt large model coding to improve the efficiency of unlabeled galaxy morphology classifications. The method can be summarized into three key aspects as follows: (1) a convolutional autoencoder is used for image denoising and reconstruction and the rotational invariance of the model is improved by polar coordinate extension; (2) utilizing a pre-trained convolutional neural network (CNN) named ConvNeXt for encoding the image data. The features were further compressed via a principal component analysis (PCA) dimensionality reduction; (3) adopting a bagging-based multi-model voting classification algorithm to enhance robustness. We applied this model to I-band images of a galaxy sample with $I_{\rm mag}< 25$ in the COSMOS field. Compared to the original unsupervised method, the number of clustering groups required by the new method is reduced from 100 to 20. Finally, we managed to classify about 53\% galaxies, significantly improving the classification efficiency. To verify the validity of the morphological classification, we selected massive galaxies with $M(*)>10^{10}(M(sun))$ for morphological parameter tests. The corresponding rules between the classification results and the physical properties of galaxies on multiple parameter surfaces are consistent with the existing evolution model. Our method has demonstrated the feasibility of using large model encoding to classify galaxy morphology, which not only improves the efficiency of galaxy morphology classification, but also saves time and manpower. Furthermore, in comparison to the original UML model, the enhanced classification performance is more evident in qualitative analysis and has successfully surpassed a greater number of parameter tests.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
