Photometric Analysis for Predicting Star Formation Rates in Large Galaxies Using Machine Learning and Deep Learning Techniques

Abstract

Star formation rates (SFRs) are a crucial observational tracer of galaxy formation and evolution. Spectroscopy, which is expensive, is traditionally used to estimate SFRs. This study tests the possibility of inferring SFRs of large samples of galaxies from only photometric data, using state-of-the-art machine learning and deep learning algorithms. The dataset adopted in this work is the one collected by Delli Veneri et al. (2019): it includes photometric data of more than 27 million galaxies coming from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). The algorithms we implemented and tested for comparing the performances include Linear Regression, Long Short-Term Memory (LSTM) networks, Support Vector Regression (SVR), Random Forest Regressor, Decision Tree Regressor, Gradient Boosting Regressor, and classical deep learning models. Our results mention that the Linear Regression model predicted an impressive accuracy of 98.97 percent as measured by the Mean Absolute Error (MAE), demonstrating that machine-learning approaches can be effective when it comes to photometric SFR estimation. Besides, the paper also reported the results for other intelligent algorithms, which predicted the SFRs, providing a detailed comparison of the performance of different machine learning algorithms in the photometric SFR estimation. This study not only shows the estimated SFR from photometric data is promising but also opens a door toward the application of machine learning and deep learning in astrophysics.