Deep-CEE I：用深度神经网络寻找星系团
Deep-CEE I: Fishing for Galaxy Clusters with Deep Neural Nets
摘要：
我们介绍Deep-CEE（用于星系团提取和评估的深度学习），这是一种新型深度学习技术的概念证明，直接应用于宽视场彩色成像以搜索星系团，而不需要光度目录。这项技术是对传统方法的补充，也可以与它们结合使用，以确认星系团的候选者。我们使用一个国家的最先进的概率算法，适用于本地化和分类的星系团从其他天体在SDSS成像。由于在公开的星表中有大量的星系团的标记数据，我们不需要依赖于模拟数据。这意味着我们尽可能保持训练数据的真实性，这在训练深度学习算法时是有利的。最终，我们将把我们的模型应用于LSST和Euclid等调查，以更广泛、更深入地探索宇宙中未被探索的区域。这将产生大量的高红移和低质量星系团的样本，这些样本可以用来约束环境驱动的星系演化和宇宙学。
英文摘要：
We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for photometric catalogues. This technique is complementary to traditional methods and could also be used in combination with them to confirm galaxy cluster candidates. We use a state-of-the-art probabilistic algorithm, adapted to localize and classify galaxy clusters from other astronomical objects in SDSS imaging. As there is an abundance of labelled data for galaxy clusters from previous classifications in publicly available catalogues, we do not need to rely on simulated data. This means we keep our training data as realistic as possible, which is advantageous when training a deep learning algorithm. Ultimately, we will apply our model to surveys such as LSST and Euclid to probe wider and deeper into unexplored regions of the Universe. This will produce large samples of both high redshift and low mass clusters, which can be utilized to constrain both environment-driven galaxy evolution and cosmology.
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。
