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

Project Overview

This project explores the prediction of Star Formation Rates (SFRs) in large galaxies using only photometric data, leveraging advanced machine learning and deep learning techniques. By utilizing the extensive dataset collected by Delli Veneri et al. (2019) from the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7), we aim to demonstrate the efficacy of these methods as alternatives to traditional spectroscopy for estimating SFRs. Please go through the research paper for additional details.

Key Features

Data Source: Utilizes photometric data from over 27 million galaxies collected by SDSS-DR7.

Algorithms Implemented

Linear Regression

Long Short-Term Memory (LSTM) Networks

Support Vector Regression (SVR)

Random Forest Regressor

Decision Tree Regressor

Gradient Boosting Regressor

Classical Deep Learning Models

Performance Metrics: Evaluates model performance using Mean Absolute Error (MAE) and other relevant metrics. Comparative Analysis: Provides a comprehensive comparison of the performance of different algorithms in estimating SFRs from photometric data.

Results

The Linear Regression model achieved an impressive accuracy of 98.97% as measured by Mean Absolute Error (MAE). The study indicates that machine learning approaches can effectively estimate SFRs from photometric data, suggesting significant potential for further applications in astrophysics.

Contributing

Contributions are welcome! Please submit a pull request or open an issue if you have any suggestions or improvements.

I have implemented the LSTM, SVR, Linear Regression, Random Forest and Decion trees

Contact

For any questions or inquiries, please reach out to [[email protected]].

This work has been submitted to the Experimental Atronomy Springer Journal