DeepShadows：使用深度学习从伪影中分离低表面亮度Galerkin
DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning
摘要：
在星系巡天中对低表面亮度星系（LSBG）的探测受到大量伪影（例如，混合在恒星和星系、银河卷云、螺旋星系臂中的恒星形成区等漫射光中的物体。必须通过耗时的目视检查来拒绝。在未来的调查中，预计将收集数百PB的数据并检测数十亿个对象，这种方法将不可行。我们研究了卷积神经网络（CNN）的使用，用于将LSBG与调查图像中的伪影分离的问题。我们利用了这样一个事实，即我们第一次从暗能量调查中获得了大量标记的LSBG和工件，我们用它们来训练、验证和测试CNN模型。该模型，我们称之为DeepShadows，达到了$92.0 \%$的测试精度，相对于基于特征的机器学习模型，这是一个显著的改进。我们还研究了使用迁移学习来适应该模型对来自更深层次的Hyper-Suprime-Cam调查的对象进行分类的能力，并且我们表明，在新调查的非常小的样本上重新训练该模型后，它可以达到$87.6\%$的准确度。这些结果表明，CNN为研究低表面亮度宇宙提供了一条非常有前途的道路。
英文摘要：
Searches for low-surface-brightness galaxies (LSBGs) in galaxy surveys are plagued by the presence of a large number of artifacts (e.g., objects blended in the diffuse light from stars and galaxies, Galactic cirrus, star-forming regions in the arms of spiral galaxies, etc.) that have to be rejected through time consuming visual inspection. In future surveys, which are expected to collect hundreds of petabytes of data and detect billions of objects, such an approach will not be feasible. We investigate the use of convolutional neural networks (CNNs) for the problem of separating LSBGs from artifacts in survey images. We take advantage of the fact that, for the first time, we have available a large number of labeled LSBGs and artifacts from the Dark Energy Survey, that we use to train, validate, and test a CNN model. That model, which we call DeepShadows, achieves a test accuracy of $92.0 \%$, a significant improvement relative to feature-based machine learning models. We also study the ability to use transfer learning to adapt this model to classify objects from the deeper Hyper-Suprime-Cam survey, and we show that after the model is retrained on a very small sample from the new survey, it can reach an accuracy of $87.6\%$. These results demonstrate that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
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。
