Morphological Classification of Galaxies Using SpinalNet

要約

【タイトル】SpinalNetによる銀河の形態分類

【要約】
・本研究では、人体の体性感覚系を模倣した入力逐次導入型ディープニューラルネットワーク（DNN）であるSpinalNetを、Galaxy Zooデータセットに適用し、銀河の異なるクラスやサブクラスを分類することができた。
・SpinalNetの入力セグメンテーションにより、中間層が前段階の出力を一部受け取ることで中間層の重さを減らすことができ、計算コスト削減に貢献。
・Galaxy Zooデータセットに適用した結果、楕円銀河と渦巻銀河、これら2つと不規則銀河の間、10種類の銀河のサブクラスの間の分類精度が高くなった。具体的には楕円銀河と渦巻銀河の間で98.2％、これら2つと不規則銀河の間で95％、10種類の銀河のサブクラスの間で82％の分類精度を達成した。

要約(オリジナル)

Deep neural networks (DNNs) with a step-by-step introduction of inputs, which is constructed by imitating the somatosensory system in human body, known as SpinalNet have been implemented in this work on a Galaxy Zoo dataset. The input segmentation in SpinalNet has enabled the intermediate layers to take some of the inputs as well as output of preceding layers thereby reducing the amount of the collected weights in the intermediate layers. As a result of these, the authors of SpinalNet reported to have achieved in most of the DNNs they tested, not only a remarkable cut in the error but also in the large reduction of the computational costs. Having applied it to the Galaxy Zoo dataset, we are able to classify the different classes and/or sub-classes of the galaxies. Thus, we have obtained higher classification accuracies of 98.2, 95 and 82 percents between elliptical and spirals, between these two and irregulars, and between 10 sub-classes of galaxies, respectively.

arxiv情報

提供元, 利用サービス

arxiv.jp, OpenAI