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Searching for moving objects in HSC-SSP: Pipeline and preliminary results

机译：在HSC-SSP中搜索移动对象：管道和初步结果

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The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) is currently the deepest wide-field survey in progress. The 8.2 m aperture of the Subaru telescope is very powerful in detecting faint/small moving objects, including near-Earth objects, asteroids, centaurs and Tran-Neptunian objects (TNOs). However, the cadence and dithering pattern of the HSC-SSP are not designed for detecting moving objects, making it difficult to do so systematically. In this paper, we introduce a new pipeline for detecting moving objects (specifically TNOs) in a non-dedicated survey. The HSC-SSP catalogs are sliced into HEALPix partitions. Then, the stationary detections and false positives are removed with a machine-learning algorithm to produce a list of moving object candidates. An orbit linking algorithm and visual inspections are executed to generate the final list of detected TNOs. The preliminary results of a search for TNOs using this new pipeline on data from the first HSC-SSP data release (2014 March to 2015 November) present 231?TNO/Centaurs candidates. The bright candidates with Hr??7.7 and i??5 show that the best-fitting slope of a single power law to absolute magnitude distribution is 0.77. The g???r color distribution of hot HSC-SSP TNOs indicates a bluer peak at g???r?=?0.9, which is consistent with the bluer peak of the bimodal color distribution in literature.
机译：斯巴鲁超级Suprime-Cam战略计划（HSC-SSP）是目前正在进行的最深入的广域调查。斯巴鲁望远镜的8.2 m孔径对检测微弱/小的运动物体（包括近地物体，小行星，半人马和Tran-海王星物体（TNOs））非常有力。但是，HSC-SSP的节奏和抖动模式并非为检测运动物体而设计，因此很难系统地进行检测。在本文中，我们介绍了一种用于在非专用调查中检测运动对象（特别是TNO）的新管道。 HSC-SSP目录被切片为HEALPix分区。然后，使用机器学习算法去除静态检测和误报，以生成候选移动对象列表。执行轨道链接算法和目视检查以生成检测到的TNO的最终列表。使用此新管道搜索TNO的初步结果是，首次HSC-SSP数据发布（2014年3月至2015年11月）中的数据为231个TNO / Centaurs候选对象。 Hr <？7.7和i？>？5的明亮候选者表明，单个幂定律与绝对量级分布的最佳拟合斜率为0.77。热的HSC-SSP TNO的g-r颜色分布表明在g-r2 ＝α0.9处有一个蓝色峰，这与文献中的双峰颜色分布的蓝色峰一致。

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