The classification of galaxies as spirals or ellipticals is a crucial task in understanding their formation and evolution. With the arrival of large-scale astronomical surveys, such as the Sloan Digital Sky Survey (SDSS), astronomers now have access to images of a vast number of galaxies. However, the visual inspection of these images is an impossible task for humans due to the sheer number of galaxies to be analyzed. To solve this problem, the Galaxy Zoo project was created to engage thousands of citizen scientists to classify the galaxies based on their visual features. In this paper, we present a machine learning model for galaxy classification using numerical data from the Galaxy Zoo[5] project. Our model utilizes a convolutional neural network architecture to extract features from galaxy images and classify them into spirals or ellipticals. We demonstrate the effectiveness of our model by comparing its performance with that of human classifiers using a subset of the Galaxy Zoo dataset. Our results show that our model achieves high accuracy in classifying galaxies and has the potential to significantly enhance our understanding of the formation and evolution of galaxies.

翻译：星系被分类为旋涡星系或椭圆星系，是理解其形成与演化的关键任务。随着大规模天文巡天项目（如斯隆数字巡天，SDSS）的出现，天文学家如今能够获取海量星系的图像数据。然而，由于需分析的星系数量庞大，人工对这些图像进行目视检查几乎不可行。为此，星系动物园（Galaxy Zoo）项目应运而生，号召数千名公民科学家根据星系视觉特征进行分类。本文提出一种基于星系动物园[5]项目数值数据的机器学习模型，用于星系分类。该模型采用卷积神经网络架构，从星系图像中提取特征，并将其分为旋涡星系或椭圆星系。我们通过使用星系动物园数据子集，将模型性能与人工分类结果进行对比，验证了模型的有效性。结果表明，我们的模型在星系分类中达到了高准确率，并有望显著加深我们对星系形成与演化的理解。