Practical Galaxy Morphology Tools from Deep Supervised Representation Learning-猫眼课题宝

DOI：

10.1093/mnras/stac525

发表时间：

2021-10

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Mike Walmsley;A. Scaife;C. Lintott;M. Lochner;Verlon Etsebeth;Tobias G'eron;H. Dickinson;L. Fortson;S. Kruk;K. Masters;K. Mantha;B. Simmons

天文学家通常通过从头开始创建自己的表示来解决监督机器学习问题。我们表明，经过训练来回答每个 Galaxy Zoo DECaLS 问题的深度学习模型可以学习有意义的星系语义表示，这些表示对于模型从未接受过训练的新任务非常有用。在研究大型星系样本至关重要的实际任务中，我们利用这些表示来超越最近的几种方法。第一个任务是识别与查询星系具有相似形态的星系。给定一个由人类分配自由文本标签的星系（例如“#diffuse”），我们可以找到与大多数标签匹配的星系。第二个任务是识别特定研究人员最感兴趣的异常现象。我们的方法在识别最有趣的 100 个异常情况方面 100% 准确（由 Galaxy Zoo 2 志愿者判断）。第三项任务是调整模型以仅使用少量新标记的星系来解决新任务。根据我们的表示进行微调的模型比根据地面图像（ImageNet）微调或从头开始训练的模型能够更好地识别环形星系。我们用很少的新标签来解决每项任务；一个（用于相似性搜索）或数百个（用于异常检测或微调）。这挑战了长期以来的观点，即深度监督方法需要新的大型标记数据集才能在天文学中实际使用。为了帮助社区从我们的预训练模型中受益，我们发布了微调代码 Zoobot。 Zoobot 可供没有深度学习经验的研究人员使用。

Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g."#diffuse"), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100% accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly-labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled datasets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code Zoobot. Zoobot is accessible to researchers with no prior experience in deep learning.

Practical Galaxy Morphology Tools from Deep Supervised Representation Learning

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