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Register a new userSearching for Moving Objects in HSC-SSP: Pipeline and Preliminary Results
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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 Subaru telescope is very powerful in detect- ing 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 re-arranged into the HEALPix architecture. Then, the stationary detections and false positive are removed with a machine learning al- gorithm 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 (Mar 2014 to Nov 2015) are also presented.
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Machine learning techniques are widely applied in many modern optical sky surveys, e.q. Pan-STARRS1, PTF/iPTF and Subaru/Hyper Suprime-Cam survey, to reduce human intervention for data verification. In this study, we have established a machine learning based real-bogus system to reject the false detections in the Subaru/Hyper-Suprime-Cam StrategicSurvey Program (HSC-SSP) source catalog. Therefore the HSC-SSP moving object detection pipeline can operate more effectively due to the reduction of false positives. To train the real-bogus system, we use the stationary sources as the real training set and the flagged data as the bogus set. The training set contains 47 features, most of which are photometric measurements and shape moments generated from the HSC image reduction pipeline (hscPipe). Our system can reach a true positive rate (tpr) ~96% with a false positive rate (fpr) ~ 1% or tpr ~99% at fpr ~5%. Therefore we conclude that the stationary sources are decent real training samples, and using photometry measurements and shape moments can reject the false positives effectively.
With the NEOWISE portion of the emph{Wide-field Infrared Survey Explorer} (WISE) project, we have carried out a highly uniform survey of the near-Earth object (NEO) population at thermal infrared wavelengths ranging from 3 to 22 $mu$m, allowing us to refine estimates of their numbers, sizes, and albedos. The NEOWISE survey detected NEOs the same way whether they were previously known or not, subject to the availability of ground-based follow-up observations, resulting in the discovery of more than 130 new NEOs. The surveys uniformity in sensitivity, observing cadence, and image quality have permitted extrapolation of the 428 near-Earth asteroids (NEAs) detected by NEOWISE during the fully cryogenic portion of the WISE mission to the larger population. We find that there are 981$pm$19 NEAs larger than 1 km and 20,500$pm$3000 NEAs larger than 100 m. We show that the Spaceguard goal of detecting 90% of all 1 km NEAs has been met, and that the cumulative size distribution is best represented by a broken power law with a slope of 1.32$pm$0.14 below 1.5 km. This power law slope produces $sim13,200pm$1,900 NEAs with $D>$140 m. Although previous studies predict another break in the cumulative size distribution below $Dsim$50-100 m, resulting in an increase in the number of NEOs in this size range and smaller, we did not detect enough objects to comment on this increase. The overall number for the NEA population between 100-1000 m are lower than previous estimates. The numbers of near-Earth comets will be the subject of future work.
We present the preliminary results of an analysis of the sub-populations within the near-Earth asteroids, including the Atens, Apollos, Amors, and those that are considered potentially hazardous using data from the Wide-field Infrared Survey Explorer (WISE). In order to extrapolate the sample of objects detected by WISE to the greater population, we determined the survey biases for asteroids detected by the projects automated moving object processing system (known as NEOWISE) as a function of diameter, visible albedo, and orbital elements. Using this technique, we are able to place constraints on the number of potentially hazardous asteroids (PHAs) larger than 100 m and find that there are $sim4700pm1450$ such objects. As expected, the Atens, Apollos, and Amors are revealed by WISE to have somewhat different albedo distributions, with the Atens being brighter than the Amors. The cumulative size distributions of the various near-Earth object (NEO) subgroups vary slightly between 100 m and 1 km. A comparison of the observed orbital elements of the various sub-populations of the NEOs with the current best model is shown.
We report the largest sample of candidate strong gravitational lenses belonging to the Survey of Gravitationally-lensed Objects in HSC Imaging for group-to-cluster scale (SuGOHI-c) systems. These candidates are compiled from the S18A data release of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) Survey. We visually inspect $sim39,500$ galaxy clusters, selected from several catalogs, overlapping with the Wide, Deep, and UltraDeep fields, spanning the cluster redshift range $0.05<z_{cl}<1.38$. We discover 641 candidate lens systems, of which 536 are new. From the full sample, 47 are almost certainly bonafide lenses, 181 of them are highly probable lenses and 413 are possible lens systems. Additionally, we present 131 lens candidates at galaxy-scale serendipitously discovered during the inspection. We obtained spectroscopic follow-up of 10 candidates using the X-shooter. With this follow-up, we confirm 8 systems as strong gravitational lenses. Of the remaining two, one of the sources is too faint to detect any emission, and the other has a tentative redshift close to the lens redshift, but additional arcs in this system are yet to be observed spectroscopically. Since the HSC-SSP is an ongoing survey, we expect to find $sim600$ definite or probable lenses using this procedure and even more if combined with other lens finding methods.
We present the procedure to build and validate the bright-star masks for the Hyper-Suprime-Cam Strategic Subaru Proposal (HSC-SSP) survey. To identify and mask the saturated stars in the full HSC-SSP footprint, we rely on the Gaia and Tycho-2 star catalogues. We first assemble a pure star catalogue down to $G_{rm Gaia} < 18$ after removing $sim1.5%$ of sources that appear extended in the Sloan Digital Sky Survey (SDSS). We perform visual inspection on the early data from the S16A internal release of HSC-SSP, finding that our star catalogue is $99.2%$ pure down to $G_{rm Gaia} < 18$. Second, we build the mask regions in an automated way using stacked detected source measurements around bright stars binned per $G_{rm Gaia}$ magnitude. Finally, we validate those masks from visual inspection and comparison with the literature of galaxy number counts and angular two-point correlation functions. This version (Arcturus) supersedes the previous version (Sirius) used in the S16A internal and DR1 public releases. We publicly release the full masks and tools to flag objects in the entire footprint of the planned HSC-SSP observations at this address: ftp://obsftp.unige.ch/pub/coupon/brightStarMasks/HSC-SSP/.
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