[Paper Review] Performance of Random Forest Machine Learning Algorithms in Binary Supernovae Classification

Jonathan Markel, Amanda J. Bayless|arXiv (Cornell University)|Jun 28, 2019

Gamma-ray bursts and supernovae30 references4 citations

TL;DR

This study applies random forest machine learning to classify Type Ia and core-collapse supernovae using light curve data, achieving over 80% accuracy with as few as five early-peak data points. It demonstrates the feasibility of rapid transient classification in upcoming all-sky surveys, offering a practical framework for early decision-making in follow-up observations.

ABSTRACT

We consider random forest machine learning algorithms applied to the classification of Type Ia and core-collapse supernovae (CCSNe) by full light curves. We also quantitatively show the potential of early-epoch classification using the same machine learning techniques. The algorithm uses the shape and magnitude of the light curve peak to determine the classification. This an initial study to essentially determine SN type with as few data points as possible. New all-sky surveys will discover new transients at a rapid rate and decisions will have to be made on very little data which transients to follow-up. Here we present an initial method where as few as five data points near peak, we an identify whether it is a Type Ia or CCSNe with better than 80% accuracy. Furthermore, we provide an introduction into the use of machine learning for classification of astrophysical transients, including potential pitfalls, best practices, and areas in need of future research.

Motivation & Objective

• To develop a machine learning method for rapid classification of supernovae using minimal observational data.
• To evaluate the performance of random forest algorithms in distinguishing Type Ia from core-collapse supernovae based on light curve morphology.
• To assess the feasibility of early classification—using data from just five points near peak brightness.
• To provide guidance on best practices and pitfalls in applying machine learning to astrophysical transient classification.

Proposed method

• Training random forest models on full light curves to learn distinguishing features between Type Ia and core-collapse supernovae.
• Using the shape and magnitude of the light curve peak as primary input features for classification.
• Evaluating model performance with progressively reduced data, starting from five early-epoch points near maximum brightness.
• Applying standard machine learning evaluation metrics (e.g., accuracy, precision, recall) to quantify classification performance.
• Validating results on a representative sample of supernova light curves from existing datasets.
• Integrating best practices in model training, feature engineering, and hyperparameter tuning for astrophysical applications.

Experimental results

Research questions

• RQ1 Can random forest models classify Type Ia and core-collapse supernovae with high accuracy using only five data points near peak brightness?
• RQ2 How does the performance of the model vary when trained on full light curves versus early-epoch data?
• RQ3 What are the key light curve features that most strongly differentiate Type Ia from core-collapse supernovae in the early phase?
• RQ4 What are the main challenges and pitfalls in applying machine learning to transient classification in astronomy?
• RQ5 What future research directions are needed to improve early transient classification in large-scale survey environments?

Key findings

• The random forest model achieves over 80% classification accuracy using only five data points near the light curve peak.
• The model effectively leverages the shape and magnitude of the light curve peak to distinguish between Type Ia and core-collapse supernovae.
• Early classification is feasible with minimal data, supporting rapid decision-making in high-cadence transient surveys.
• The study identifies key features in early light curves that are most informative for classification.
• The authors highlight critical pitfalls and best practices for applying machine learning to astrophysical transient data.
• The work establishes a foundation for scalable, automated classification in upcoming all-sky transient surveys.