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arXiv 2109.09781astro-ph.CO
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用机器学习寻找四重成像的类星体。I.方法

Finding quadruply imaged quasars with machine learning. I. Methods

A. Akhazhanov, A. More, A. Amini, C. Hazlett, T. Treu, S. Birrer, A. Shajib, P. Schechter, C. Lemon, B. Nord, M. Aguena, S. Allam, F. Andrade-Oliveira, J. Annis… 展开作者

A. Akhazhanov, A. More, A. Amini, C. Hazlett, T. Treu, S. Birrer, A. Shajib, P. Schechter, C. Lemon, B. Nord, M. Aguena, S. Allam, F. Andrade-Oliveira, J. Annis, D. Brooks, E. Buckley-Geer, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, A. Choi, C. Conselice, M. Costanzi, L. N. da Costa, M. E. S. Pereira, J. De Vicente, S. Desai, J. P. Dietrich, P. Doel, S. Everett, I. Ferrero, D. A. Finley, B. Flaugher, J. Frieman, J. García-Bellido, D. W. Gerdes, D. Gruen, R. A. Gruendl, J. Gschwend, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, A. G. Kim, K. Kuehn, N. Kuropatkin, O. Lahav, M. Lima, H. Lin, M. A. G. Maia, M. March, F. Menanteau, R. Miquel, R. Morgan, A. Palmese, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, E. Sanchez, V. Scarpine, S. Serrano, I. Sevilla-Noarbe, M. Smith, M. Soares-Santos, E. Suchyta, M. E. C. Swanson, G. Tarle, C. To, T. N. Varga, J. Weller

摘要:

强透镜四重成像类星体是非常特殊的天体。它们在天空中非常罕见-迄今为止只知道几十个-但它们提供了关于广泛主题的独特信息,包括宇宙的膨胀历史和组成,恒星和暗物质在星系中的分布,类星体的宿主星系以及恒星初始质量函数。在天文图像中找到它们是一个经典的“大海捞针”问题,因为它们的数量要比其他(污染物)源多出许多数量级。为了解决这个问题,我们开发了最先进的深度学习方法,并在逼真的模拟四边形上进行训练,这些四边形基于从暗能量调查中拍摄的星系实像,具有逼真的源和偏转器模型,包括微透镜的色彩效应。最好的方法在模拟和真实物体的混合物上的性能非常好,接收器工作曲线下的面积在0.86到0.89之间。召回率接近100%下降到总幅度i~21,表明高完整性,而精确度从85%下降到70%,在范围i~17-21。这些方法非常快:在GPU机器上训练200万个样本需要20个小时,每GPU小时可以评估10^8个多波段剪切。该方法的速度和性能为将其应用于天文源的大样本铺平了道路,绕过了光度预选的需要,这可能是当前已知四边形样本不完整的主要原因。

英文摘要:

Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky -- only a few tens are known to date -- and yet they provide unique information about a wide range of topics, including the expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic "needle in a haystack" problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range 0.86 to 0.89. Recall is close to 100% down to total magnitude i~21 indicating high completeness, while precision declines from 85% to 70% in the range i~17-21. The methods are extremely fast: training on 2 million samples takes 20 hours on a GPU machine, and 10^8 multi-band cutouts can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads.

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。

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