TY - GEN
T1 - Single-Epoch Supernova Classification with Deep Convolutional Neural Networks
AU - Kimura, Akisato
AU - Takahashi, Ichiro
AU - Tanaka, Masaomi
AU - Yasuda, Naoki
AU - Ueda, Naonori
AU - Yoshida, Naoki
N1 - Publisher Copyright:
© 2017 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/7/13
Y1 - 2017/7/13
N2 - 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. The current photometric supernova surveys produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods. 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 luminances 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.
AB - 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. The current photometric supernova surveys produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods. 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 luminances 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.
KW - Supernova
KW - convolutional neural network
KW - deep learning
KW - image classification
KW - redshift
UR - http://www.scopus.com/inward/record.url?scp=85027493531&partnerID=8YFLogxK
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U2 - 10.1109/ICDCSW.2017.47
DO - 10.1109/ICDCSW.2017.47
M3 - Conference contribution
AN - SCOPUS:85027493531
T3 - Proceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017
SP - 354
EP - 359
BT - Proceedings - IEEE 37th International Conference on Distributed Computing Systems Workshops, ICDCSW 2017
A2 - Ferreira, Joao E.
A2 - Higashino, Teruo
A2 - Musaev, Aibek
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2017
Y2 - 5 June 2017 through 8 June 2017
ER -