  Supernovae Type-Ia (SNeIa) play a significant role in exploring the history
of the expansion of the Universe, since they are the best-known standard
candles with which we can accurately measure the distance to the objects.
Finding large samples of SNeIa and investigating their detailed characteristics
have become an important issue in cosmology and astronomy. Existing methods
relied on a photometric approach that first measures the luminance of supernova
candidates precisely and then fits the results to a parametric function of
temporal changes in luminance. However, it inevitably requires multi-epoch
observations and complex luminance measurements. In this work, we present a
novel method for classifying SNeIa simply from single-epoch observation images
without any complex measurements, by effectively integrating the
state-of-the-art computer vision methodology into the standard photometric
approach. Our method first builds a convolutional neural network for estimating
the luminance of supernovae from telescope images, and then constructs another
neural network for the classification, where the estimated luminance and
observation dates are used as features for classification. Both of the neural
networks are integrated into a single deep neural network to classify SNeIa
directly from observation images. Experimental results show the effectiveness
of the proposed method and reveal classification performance comparable to
existing photometric methods with multi-epoch observations.
