银河光谱神经网络（GaSNets）。I.使用深度学习在eBOSS光谱中搜索强透镜候选者
Galaxy Spectra neural Networks (GaSNets). I. Searching for strong lens candidates in eBOSS spectra using Deep Learning
摘要：
随着新的地面和太空光谱测量的出现，观测到数亿个星系，光谱分类将成为标准分析技术的压倒性优势。为了应对这一挑战，我们引入了一系列深度学习工具来对一维光谱中的特征进行分类。作为这些星系光谱神经网络（GaSNets）的第一个应用程序，我们专注于专门用于识别eBOSS光谱中强透镜恒星形成星系的发射线的工具。我们首先讨论了这些网络的训练和测试，并定义了一个阈值概率，PL，为95%的高质量事件检测。然后，使用先前一组从eBOSS中光谱选择的强透镜，用HST确认，我们估计完整性约为80%，因为透镜的回收率高于所采用的PL。最后，我们将GaSNets应用于约1.3M光谱，以收集第一个约430个新的高质量候选者的列表，这些候选者通过应用于光谱学的深度学习进行识别，并在视觉上被分级为高度可能的真实事件。对地面观测的初步检查初步显示，该样本的确认率为38%，与之前使用标准（无深度学习）分类工具选择的样本以及哈勃太空望远镜的后续样本一致。第一次测试表明，机器学习可以有效地扩展到波长空间中的特征识别，这对于未来的调查，如4MOST，DESI，Euclid和中国空间站望远镜（CSST）至关重要。
英文摘要：
With the advent of new spectroscopic surveys from ground and space, observing up to hundreds of millions of galaxies, spectra classification will become overwhelming for standard analysis techniques. To prepare for this challenge, we introduce a family of deep learning tools to classify features in one-dimensional spectra. As the first application of these Galaxy Spectra neural Networks (GaSNets), we focus on tools specialized at identifying emission lines from strongly lensed star-forming galaxies in the eBOSS spectra. We first discuss the training and testing of these networks and define a threshold probability, PL, of 95% for the high quality event detection. Then, using a previous set of spectroscopically selected strong lenses from eBOSS, confirmed with HST, we estimate a completeness of ~80% as the fraction of lenses recovered above the adopted PL. We finally apply the GaSNets to ~1.3M spectra to collect a first list of ~430 new high quality candidates identified with deep learning applied to spectroscopy and visually graded as highly probable real events. A preliminary check against ground-based observations tentatively shows that this sample has a confirmation rate of 38%, in line with previous samples selected with standard (no deep learning) classification tools and follow-up by Hubble Space Telescope. This first test shows that machine learning can be efficiently extended to feature recognition in the wavelength space, which will be crucial for future surveys like 4MOST, DESI, Euclid, and the Chinese Space Station Telescope (CSST).
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。
