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

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.