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Register a new userUltra-deep Large Binocular Camera U-band Imaging of the GOODS-North Field: Depth vs. Resolution
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We present a study of the trade-off between depth and resolution using a large number of U-band imaging observations in the GOODS-North field (Giavalisco et al. 2004) from the Large Binocular Camera (LBC) on the Large Binocular Telescope (LBT). Having acquired over 30 hours of data (315 images with 5-6 mins exposures), we generated multiple image mosaics, starting with the best atmospheric seeing images (FWHM $lesssim$0.8), which constitute $sim$10% of the total data set. For subsequent mosaics, we added in data with larger seeing values until the final, deepest mosaic included all images with FWHM $lesssim$1.8 ($sim$94% of the total data set). From the mosaics, we made object catalogs to compare the optimal-resolution, yet shallower image to the lower-resolution but deeper image. We show that the number counts for both images are $sim$90% complete to $U_{AB}$ $lesssim26$. Fainter than $U_{AB}$$sim$ 27, the object counts from the optimal-resolution image start to drop-off dramatically (90% between $U_{AB}$ = 27 and 28 mag), while the deepest image with better surface-brightness sensitivity ($mu^{AB}_{U}$$lesssim$ 32 mag arcsec$^{-2}$) show a more gradual drop (10% between $U_{AB}$ $simeq$ 27 and 28 mag). For the brightest galaxies within the GOODS-N field, structure and clumpy features within the galaxies are more prominent in the optimal-resolution image compared to the deeper mosaics. Finally, we find - for 220 brighter galaxies with $U_{AB}$$lesssim$ 24 mag - only marginal differences in total flux between the optimal-resolution and lower-resolution light-profiles to $mu^{AB}_{U}$$lesssim$ 32 mag arcsec$^{-2}$. In only 10% of the cases are the total-flux differences larger than 0.5 mag. This helps constrain how much flux can be missed from galaxy outskirts, which is important for studies of the Extragalactic Background Light.
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This work presents dense stereo reconstruction using high-resolution images for infrastructure inspections. The state-of-the-art stereo reconstruction methods, both learning and non-learning ones, consume too much computational resource on high-resolution data. Recent learning-based methods achieve top ranks on most benchmarks. However, they suffer from the generalization issue due to lack of task-specific training data. We propose to use a less resource demanding non-learning method, guided by a learning-based model, to handle high-resolution images and achieve accurate stereo reconstruction. The deep-learning model produces an initial disparity prediction with uncertainty for each pixel of the down-sampled stereo image pair. The uncertainty serves as a self-measurement of its generalization ability and the per-pixel searching range around the initially predicted disparity. The downstream process performs a modified version of the Semi-Global Block Matching method with the up-sampled per-pixel searching range. The proposed deep-learning assisted method is evaluated on the Middlebury dataset and high-resolution stereo images collected by our customized binocular stereo camera. The combination of learning and non-learning methods achieves better performance on 12 out of 15 cases of the Middlebury dataset. In our infrastructure inspection experiments, the average 3D reconstruction error is less than 0.004m.
The CFHT Large Area U-band Deep Survey (CLAUDS) uses data taken with the MegaCam mosaic imager on CFHT to produce images of 18.60 deg2 with median seeing of FWHM=0.92 arcsec and to a median depth of U = 27.1 AB (5 sigma in 2 arcsec apertures), with selected areas that total 1.36 deg2 reaching a median depth of U=27.7 AB. These are the deepest U-band images assembled to date over this large an area. These data are located in four fields also imaged to comparably faint levels in grizy and several narrowband filters as part of the Hyper Suprime-Cam (HSC) Subaru Strategic Program (HSC-SSP). These CFHT and Subaru datasets will remain unmatched in their combination of area and depth until the advent of the Large Synoptic Survey Telescope (LSST). This paper provides an overview of the scientific motivation for CLAUDS and gives details of the observing strategy, observations, data reduction, and data merging with the HSC-SSP. Three early applications of these deep data are used to illustrate the potential of the dataset: deep U-band galaxy number counts, z~3 Lyman break galaxy (LBG) selection, and photometric redshifts improved by adding CLAUDS U to the Subaru HSC grizy photometry.
We present the J and H-band source catalog covering the AKARI North Ecliptic Pole field. Filling the gap between the optical data from other follow-up observations and mid-infrared (MIR) data from AKARI, our near-infrared (NIR) data provides contiguous wavelength coverage from optical to MIR. For the J and H-band imaging, we used the FLoridA Multi-object Imaging Near-ir Grism Observational Spectrometer (FLAMINGOS) on the Kitt Peak National Observatory 2.1m telescope covering a 5.1 deg2 area down to a 5 sigma depth of ~21.6 mag and ~21.3 mag (AB) for J and H-band with an astrometric accuracy of 0.14 and 0.17 for 1 sigma in R.A. and Decl. directions, respectively. We detected 208,020 sources for J-band and 203,832 sources for H-band. This NIR data is being used for studies including analysis of the physical properties of infrared sources such as stellar mass and photometric redshifts, and will be a valuable dataset for various future missions.
The $AKARI$ infrared (IR) space telescope conducted two surveys (Deep and Wide) in the North Ecliptic Pole (NEP) field to find more than 100,000 IR sources using its Infrared Camera (IRC). IRCs 9 filters, which cover wavebands from 2 to 24 $mu$m continuously, make $AKARI$ unique in comparison with other IR observatories such as $Spitzer$ or $WISE$. However, studies of the $AKARI$ NEP-Wide field sources had been limited due to the lack of follow-up observations in the ultraviolet (UV) and optical. In this work, we present the Canada-France-Hawaii Telescope (CFHT) MegaPrime/MegaCam $u$-band source catalogue of the $AKARI$ NEP-Wide field. The observations were taken in 7 nights in 2015 and 2016, resulting in 82 observed frames covering 3.6 deg$^2$. The data reduction, image processing and source extraction were performed in a standard procedure using the textsc{Elixir} pipeline and the textsc{AstrOmatic} software, and eventually 351,635 sources have been extracted. The data quality is discussed in two regions (shallow and deep) separately, due to the difference in the total integration time (4,520 and 13,910 seconds). The 5$sigma$ limiting magnitude, seeing FWHM, and the magnitude at 50 per cent completeness are 25.38 mag (25.79 mag in the deep region), 0.82 arcsec (0.94 arcsec) and 25.06 mag (25.45 mag), respectively. The u-band data provide us with critical improvements to photometric redshifts and UV estimates of the precious infrared sources from the $AKARI$ space telescope.
We present the characteristics and some early scientific results of the first instrument at the Large Binocular Telescope (LBT), the Large Binocular Camera (LBC). Each LBT telescope unit will be equipped with similar prime focus cameras. The blue channel is optimized for imaging in the UV-B bands and the red channel for imaging in the VRIz bands. The corrected field-of-view of each camera is approximately 30 arcminutes in diameter, and the chip area is equivalent to a 23x23 arcmin2 field. In this paper we also present the commissioning results of the blue channel. The scientific and technical performance of the blue channel was assessed by measurement of the astrometric distortion, flat fielding, ghosts, and photometric calibrations. These measurements were then used as input to a data reduction pipeline applied to science commissioning data. The measurements completed during commissioning show that the technical performance of the blue channel is in agreement with original expectations. Since the red camera is very similar to the blue one we expect similar performance from the commissioning that will be performed in the following months in binocular configuration. Using deep UV image, acquired during the commissioning of the blue camera, we derived faint UV galaxy-counts in a ~500 sq. arcmin. sky area to U(Vega)=26.5. These galaxy counts imply that the blue camera is the most powerful UV imager presently available and in the near future in terms of depth and extent of the field-of-view. We emphasize the potential of the blue camera to increase the robustness of the UGR multicolour selection of Lyman break galaxies at redshift z~3.
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