arXiv 2004.027152020-04astro-ph.GA

Kilo Degree调查中具有深度学习的新高质量强镜头候选者

New high-quality strong lens candidates with deep learning in the Kilo Degree Survey

R. Li, N. R. Napolitano, C. Tortora, C. Spiniello, L. V. E. Koopmans, Z. Huang, N. Roy, G. Vernardos, S. Chatterjee, B. Giblin, F. Getman, M. Radovich, G. Covone, K. Kuijken

备注：: Accepted by APJ
DOI：: 10.3847/1538-4357/ab9dfa

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摘要：

我们报告了使用机器学习在Kilo Degree Survey数据发布4中发现的新的高质量星系级强透镜候选者。我们开发了一种新的卷积神经网络（CNN）分类器来搜索重力弧，遵循\cite{2019MNRAS.484.3879P}的处方，只使用$r-$波段图像。我们已经将CNN应用于两个“预测样本”：一个明亮的红色星系（LRG）和一个“明亮星系”（BG）样本（$r<21$）。我们发现了286个新的高概率候选者，133个来自LRG样本，153个来自BG样本。然后，我们根据结合了CNN成为晶状体的可能性和目视检查所得的人类评分（P值）的值对这些候选项进行了排名，我们在此提供了具有P值$\ge 0.5$的排名最高的82个候选项。所有这些高质量的候选人都有明显的弧形或点状特征周围的中央红色叛逃者。此外，我们定义了最好的26个对象，所有的得分P值$\ge 0.7$作为“黄金样本”的候选人。预计该样本包含极少的假阳性，因此适用于后续观察。新的候选镜片部分来自于本文采用的与先前分析相关的更广泛的足迹，部分来自于更大的预测样本（也包括BG样本）。这些结果表明，机器学习工具非常有希望在大型调查中找到强透镜，并且可以通过扩大预测样本来找到更多的候选者，超出LRG的标准假设。未来，我们计划将我们的CNN应用于下一代巡天数据，如大型综合巡天望远镜、欧几里得和中国空间站光学巡天。

英文摘要：

We report new high-quality galaxy scale strong lens candidates found in the Kilo Degree Survey data release 4 using Machine Learning. We have developed a new Convolutional Neural Network (CNN) classifier to search for gravitational arcs, following the prescription by \cite{2019MNRAS.484.3879P} and using only $r-$band images. We have applied the CNN to two "predictive samples": a Luminous red galaxy (LRG) and a "bright galaxy" (BG) sample ($r<21$). We have found 286 new high probability candidates, 133 from the LRG sample and 153 from the BG sample. We have then ranked these candidates based on a value that combines the CNN likelihood to be a lens and the human score resulting from visual inspection (P-value) and we present here the highest 82 ranked candidates with P-values $\ge 0.5$. All these high-quality candidates have obvious arc or point-like features around the central red defector. Moreover, we define the best 26 objects, all with scores P-values $\ge 0.7$ as a "golden sample" of candidates. This sample is expected to contain very few false positives and thus it is suitable for follow-up observations. The new lens candidates come partially from the the more extended footprint adopted here with respect to the previous analyses, partially from a larger predictive sample (also including the BG sample). These results show that machine learning tools are very promising to find strong lenses in large surveys and more candidates that can be found by enlarging the predictive samples beyond the standard assumption of LRGs. In the future, we plan to apply our CNN to the data from next-generation surveys such as the Large Synoptic Survey Telescope, Euclid, and the Chinese Space Station Optical Survey.

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