Multi-layer Perceptron for Predicting Galaxy Parameters (MLP-GaP): Stellar Masses and Star Formation Rates

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, 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 data set generated by the Code Investigating GALaxy Emission (CIGALE) for training and testing data sets. Subsequently, we used a multi-layer perceptron model to build MLP-GaP and effectively trained it with the training data set. The results of the test performed on the mock data set 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 spectral energy distribution 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.