Machine learning for the early classification of broad-lined Ic supernovae

TL;DR

This paper presents a machine learning approach using random forests and new parameters to improve early classification of rare broad-lined Ic supernovae, enhancing detection rates and data quality.

Contribution

It introduces a novel ML methodology with magnitude rates for early supernova classification, demonstrating improved detection of SN Ic-BL and SN Ia.

Findings

Random forest models identified over 13.6% of true SN Ic-BL population.

The ML approach outperforms current classification methods.

Implementation of this model can significantly increase early-time supernova data collection.

Abstract

Science is currently at an age where there is more data than we know how to deal with. Machine learning (ML) is an emerging tool that is useful for drawing valuable science out of incomprehensibly large datasets and identifying complex trends in data that may otherwise be overlooked. Moreover, ML can potentially enhance the quality and quantity of scientific data as they are collected. This paper explores how a new ML method can improve the rate of classification of rare broad-lined Ic (Ic-BL) supernovae (SNe). We introduce new parameters called magnitude rates to train ML models to identify SNe Ic-BL in large datasets and apply this same methodology to a population of SN Ia to test if our ML approach is reproducible. The information we required to train each ML model included three magnitudes, three time differences, two magnitude rates, and the second derivative of these rates using the first three available photometric data points in a single filter. Our initial investigations showed that the random forest algorithm provides a strong foundation for the early classifications SNe Ic-BL and SNe Ia. Testing this model again on an unseen dataset showed that the model can identify upward of 13.6\% of the total true SN Ic-BL population, significantly improving on current methods. By implementing a dedicated observation campaign using this model, the number of SN Ic-BL classified and the quality of early-time data collected each year will see considerable growth in the near future.