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

TL;DR

This paper introduces MLP-GaP, a machine learning tool that efficiently predicts galaxy stellar masses and star formation rates from multi-band photometric data, demonstrating high accuracy and speed compared to traditional methods.

Contribution

The paper presents a novel multi-layer perceptron model that accurately and rapidly estimates galaxy parameters, outperforming existing SED fitting techniques in speed while maintaining high accuracy.

Findings

MLP-GaP predicts galaxy parameters with high accuracy.

MLP-GaP is significantly faster than traditional SED fitting methods.

Good consistency between MLP-GaP predictions and SED fitting results.

Abstract

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.