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arXiv 2102.12776astro-ph.GAastro-ph.IM
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Dwarfs from the Dark(Energy Survey):一种从多波段图像中分类矮星系的机器学习方法

Dwarfs from the Dark (Energy Survey): a machine learning approach to classify dwarf galaxies from multi-band images

Oliver Müller, Eva Schnider

摘要:

在最近的大型巡天中,已经探测到了无数的低表面亮度天体,包括螺旋星系、矮星系和噪音模式。传统上,天文学家通过目视检查这些探测结果来区分真实的低表面亮度星系和人工制品。利用暗能量调查(DES)和机器学习技术,Tanoglidis等人(2020)已经展示了计算机如何自动执行这项任务。在这里,我们建立在他们的开创性工作,并进一步分离检测到的低表面亮度星系螺旋,矮椭圆,矮不规则星系。为了这个目的,我们已经手动分类5567检测从DES的多波段图像和搜索的神经网络架构能够完成这项任务。采用超参数搜索,我们发现一系列卷积神经网络实现了与手动分类相似的结果,螺旋星系的准确率为85%,矮椭圆星系为94%,矮不规则星系为52%。对于矮小的不规则生物--由于它们形态上的多样性--这项任务对人类和机器来说都很困难。我们简单的架构表明,机器学习可以减少天文学家研究大型数据集的工作量,在不久的将来,Euclid等任务将提供这种服务。

英文摘要:

Countless low-surface brightness objects - including spiral galaxies, dwarf galaxies, and noise patterns - have been detected in recent large surveys. Classically, astronomers visually inspect those detections to distinguish between real low-surface brightness galaxies and artefacts. Employing the Dark Energy Survey (DES) and machine learning techniques, Tanoglidis et al. (2020) have shown how this task can be automatically performed by computers. Here, we build upon their pioneering work and further separate the detected low-surface brightness galaxies into spirals, dwarf ellipticals, and dwarf irregular galaxies. For this purpose, we have manually classified 5567 detections from multi-band images from DES and searched for a neural network architecture capable of this task. Employing a hyperparameter search, we find a family of convolutional neural networks achieving similar results as with the manual classification, with an accuracy of 85% for spiral galaxies, 94% for dwarf ellipticals, and 52% for dwarf irregulars. For dwarf irregulars - due to their diversity in morphology - the task is difficult for humans and machines alike. Our simple architecture shows that machine learning can reduce the workload of astronomers studying large data sets by orders of magnitudes, as will be available in the near future with missions such as Euclid.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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