标题： 用于预测星系参数的多层感知器（MLP-GaP）：恒星质量和恒星形成率 显示英文标题

标题： Multi-Layer Perceptron for Predicting Galaxy Parameters (MLP-GaP): stellar masses and star formation rates

摘要： 大规模成像巡天将为数十亿个星系产生多波段的海量测光数据。必须制定策略以快速高效地从这些数据中提取有用的物理信息。在星系的恒星群体参数中，其恒星质量和恒星形成率（SFR）是最基本的。我们开发了一种新颖的工具，即\textit{用于预测星系参数的多层感知机}（MLP-GaP），它利用机器学习（ML）算法准确且高效地从多波段目录中推导出恒星质量和SFR。我们首先采用由\textit{代码研究星系发射}（CIGALE）生成的模拟数据集作为训练和测试数据集。随后，我们使用多层感知器模型构建了MLP-GaP，并用训练数据集有效训练了它。在模拟数据集上进行的测试结果显示，MLP-GaP能够准确预测参考值。此外，MLP-GaP的处理速度比CIGALE显著更快。为了证明MLP-GaP的科学适用性，我们还将其应用于真实数据样本，并将其恒星质量和SFR与CIGALE进行了比较。总体而言，MLP-GaP预测的值与通过SED拟合得出的估计值具有非常好的一致性。因此，MLP-GaP快速且准确地预测恒星质量和SFR的能力使其特别适合于大规模巡天时代的大量星系分析。 显示英文摘要

摘要： The large-scale imaging survey will produce massive photometric data in multi-bands for billions of galaxies. Defining strategies to quickly and efficiently extract useful physical information from this data is mandatory. Among the stellar population parameters for galaxies, their stellar masses and star formation rates (SFRs) are the most fundamental. We develop a novel tool, \textit{Multi-Layer Perceptron for Predicting Galaxy Parameters} (MLP-GaP), that uses a machine-learning (ML) algorithm to accurately and efficiently derive the stellar masses and SFRs from multi-band catalogs. We first adopt a mock dataset generated by the \textit{Code Investigating GALaxy Emission} (CIGALE) for training and testing datasets. Subsequently, we used a multi-layer perceptron model to build MLP-GaP and effectively trained it with the training dataset. The results of the test performed on the mock dataset show that MLP-GaP can accurately predict the reference values. Besides MLP-GaP has a significantly faster processing speed than CIGALE. To demonstrate the science-readiness of the MLP-GaP, we also apply it to a real data sample and compare the stellar masses and SFRs with CIGALE. Overall, the predicted values of MLP-GaP show a very good consistency with the estimated values derived from SED fitting. Therefore, the capability of MLP-GaP to rapidly and accurately predict stellar masses and SFRs makes it particularly well-suited for analyzing huge amounts of galaxies in the era of large sky surveys.