Exploring Machine Learning Regression Models for Advancing Foreground Mitigation and Global 21cm Signal Parameter Extraction

TL;DR

This study evaluates machine learning regression models for extracting parameters from the global 21cm signal, demonstrating that Artificial Neural Networks outperform others, especially with PCA preprocessing, in accuracy and efficiency.

Contribution

The paper compares four machine learning regression models for 21cm signal analysis, highlighting the superior performance of ANNs and the benefits of PCA preprocessing.

Findings

ANN achieves lowest RMSE and highest R^2 scores.

PCA preprocessing improves model accuracy, especially with foreground contamination.

GPR is accurate but computationally intensive.

Abstract

Extracting parameters from the global 21cm signal is crucial for understanding the early Universe. However, detecting the 21cm signal is challenging due to the brighter foreground and associated observational difficulties. In this study, we evaluate the performance of various machine-learning regression models to improve parameter extraction and foreground removal. This evaluation is essential for selecting the most suitable machine learning regression model based on computational efficiency and predictive accuracy. We compare four models: Random Forest Regressor (RFR), Gaussian Process Regressor (GPR), Support Vector Regressor (SVR), and Artificial Neural Networks (ANN). The comparison is based on metrics such as the root mean square error (RMSE) and $R^2$ scores. We examine their effectiveness across different dataset sizes and conditions, including scenarios with foreground contamination. Our results indicate that ANN consistently outperforms the other models, achieving the lowest RMSE and the highest $R^2$ scores across multiple cases. While GPR also performs well, it is computationally intensive, requiring significant RAM and longer execution times. SVR struggles with large datasets due to its high computational costs, and RFR demonstrates the weakest accuracy among the models tested. We also found that employing Principal Component Analysis (PCA) as a preprocessing step significantly enhances model performance, especially in the presence of foregrounds.