Abstract
Abstract 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.
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