标题： 一种广泛适用的星系团机器学习发现方法 显示英文标题

标题： A widely applicable Galaxy Group finder Using Machine Learning

摘要： 星系团对于研究红移巡天中大尺度物质分布以及解密星系特性与其相关晕之间的联系至关重要。 在本工作中，我们提出了一种广泛适用的方法，通过机器学习技术在真实空间中识别星系团，并考虑红移畸变的影响。 我们的方法涉及两个神经网络：一个是用于识别中心星系团的分类模型，另一个是用于预测这些星系团质量的回归模型。 两个模型都输入可观测的星系特性，使其能够适用于未来的实际巡天数据。 在模拟数据集上的测试表明，我们的方法能准确识别超过$92\%$的星系团，其中$\mathrm{M}_{vir} \geq 10^{11}h^{-1}\mathrm{M}_\odot$的识别准确率达到了$80\%$，成员完整性至少达到$80\%$。 即使在没有先验数据的情况下，预测的星系团质量在不同质量尺度上的变化也小于 0.3 dex。 我们的网络可以无缝适应扩展到通量限制为$m_{r} < 14$的稀疏样本、红移为$z=1.08$的高红移样本以及来自 TNG300 流体动力学模拟的星系样本，而无需进一步训练。 此外，该框架可以通过在红移畸变样本上进行训练，轻松调整以适应实际巡天，而无需更改参数。 仔细考虑红移空间中的不同观测效应，使得该方法有望应用于实际的星系巡天。 显示英文摘要

摘要： Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for identifying groups through machine learning techniques in real space taking into account the impact of redshift distortion. Our methodology involves two neural networks: one is a classification model for identifying central galaxy groups, and the other is a regression model for predicting the mass of these groups. Both models input observable galaxy traits, allowing future applicability to real survey data. Testing on simulated datasets indicates our method accurately identifies over $92\%$ of groups with $\mathrm{M}_{vir} \geq 10^{11}h^{-1}\mathrm{M}_\odot$, with $80\%$ achieving a membership completeness of at least $80\%$. The predicted group masses vary by less than 0.3 dex across different mass scales, even in the absence of a priori data. Our network adapts seamlessly to expand to sparse samples with a flux limit of $m_{r} < 14$, to high redshift samples at $z=1.08$, and to galaxy samples from the TNG300 hydrodynamical simulation without further training. Furthermore, the framework can easily adjust to real surveys by training on redshift distorted samples without needing parameter changes. Careful consideration of different observational effects in redshift space makes it promising that this method will be applicable to real galaxy surveys.