Using Random Forest Machine Learning Algorithms in Binary Supernovae Classification
arXiv:1907.00088
Abstract
In the era of large all-sky surveys, there will be a need for rapid, automatic classifications of newly discovered transient objects. Our focus here is the classification of supernovae (SNe). We consider random forest machine learning algorithm 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 study 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, but with several biases in the data that will require addressing in future work.
7 figures, withdrawn from ApJ, see note at end of paper
References in corpus (13)
- Scikit-learn: Machine Learning in Python
- Photometric Supernova Classification With Machine Learning
- Late-Time Spectroscopy of SN 2002cx: The Prototype of a New Subclass of Type Ia Supernovae
- Results from the Supernova Photometric Classification Challenge
- The Core-collapse rate from the Supernova Legacy Survey
- The Rate of Core Collapse Supernovae to Redshift 2.5 From The CANDELS and CLASH Supernova Surveys
- Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning
- A simple and robust method for automated photometric classification of supernovae using neural networks
- Bolometric and UV Light Curves of Core-Collapse Supernovae
- STACCATO: A Novel Solution to Supernova Photometric Classification with Biased Training Sets
- Supernova Photometric Classification Challenge
- Photometric classification and redshift estimation of LSST Supernovae
- The Supernovae Analysis Application (SNAP)