Most existing star-galaxy classifiers use the reduced summary information
from catalogs, requiring careful feature extraction and selection. The latest
advances in machine learning that use deep convolutional neural networks allow
a machine to automatically learn the features directly from data, minimizing
the need for input from human experts. We present a star-galaxy classification
framework that uses deep convolutional neural networks (ConvNets) directly on
the reduced, calibrated pixel values. Using data from the Sloan Digital Sky
Survey (SDSS) and the Canada-France-Hawaii Telescope Lensing Survey (CFHTLenS),
we demonstrate that ConvNets are able to produce accurate and well-calibrated
probabilistic classifications that are competitive with conventional machine
learning techniques. Future advances in deep learning may bring more success
with current and forthcoming photometric surveys, such as the Dark Energy
Survey (DES) and the Large Synoptic Survey Telescope (LSST), because deep
neural networks require very little, manual feature engineering.

本研究提出了一种使用深度卷积神经网络的恒星 - 星系分类框架，可直接基于像素值进行学习，避免了人工特征工程需求，并且能在 SDSS 和 CFHTLenS 数据集上取得与传统机器学习技术相竞争的准确和可靠的概率分类结果。