Prediction of Star Formation Rates Using an Artificial Neural Network

TL;DR

This paper presents an artificial neural network model to estimate galaxy star formation rates and IR luminosity using multi-wavelength photometric data, showing high accuracy for star-forming galaxies.

Contribution

The study introduces a neural network approach that effectively predicts IR luminosity and SFR from diverse photometric features, improving accuracy over previous methods.

Findings

High accuracy in predicting SFR for star-forming galaxies.

Slight performance improvement when using comprehensive photometric data.

Discrepancies observed in composite and AGN galaxy predictions.

Abstract

In this study, we develop an artificial neural network to estimate the infrared (IR) luminosity and star formation rates (SFR) of galaxies. Our network is trained using 'true' IR luminosity values derived from modeling the IR spectral energy distributions (SEDs) of FIR-detected galaxies. We explore five different sets of input features, each incorporating optical, mid-infrared (MIR), near-infrared (NIR), ultraviolet (UV), and emission line data, along with spectroscopic redshifts and uncertainties. All feature sets yield similar IR luminosity predictions, but including all photometric data leads to slightly improved performance. This suggests that comprehensive photometric information enhances the accuracy of our predictions. Our network is applied to a sample of SDSS galaxies defined as unseen data, and the results are compared with three published catalogs of SFRs. Overall, our network demonstrates excellent performance for star-forming galaxies while we observe discrepancies in composite and AGN samples. These inconsistencies may stem from uncertainties inherent in the compared catalogs or potential limitations in the performance of our network.