Photometric classification of HSC transients using machine learning

Understand

The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries.

• The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through spring 2017, yielded 1824 SN candidates.
• This gave rise to the need for fast type classification for spectroscopic follow-up and prompted us to develop a machine learning algorithm using a deep neural network (DNN) with highway layers.
• This machine is trained by actual observed cadence and filter combinations such that we can directly input the observed data array into the machine without any interpretation.