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Register a new userPhotometric redshifts for galaxies in the Subaru Hyper Suprime-Cam and unWISE and a catalogue of identified clusters of galaxies
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We first present a catalogue of photometric redshifts for 14.68 million galaxies derived from the 7-band photometric data of Hyper Suprime-Cam Subaru Strategic Program and the Wide-field Infrared Survey Explorer using the nearest-neighbour algorithm. The redshift uncertainty is about 0.024 for galaxies of z<0.7, and steadily increases with redshift to about 0.11 at z~2. From such a large data set, we identify 21,661 clusters of galaxies, among which 5537 clusters have redshifts z>1 and 642 clusters have z>1.5, significantly enlarging the high redshift sample of galaxy clusters. Cluster richness and mass are estimated, and these clusters have an equivalent mass of M_{500} > 0.7*10^{14} Msun. We find that the stellar mass of the brightest cluster galaxies (BCGs) in each richness bin does not significantly evolve with redshift. The fraction of star-forming BCGs increases with redshift, but does not depend on cluster mass.
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Photometric redshifts are a key component of many science objectives in the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). In this paper, we describe and compare the codes used to compute photometric redshifts for HSC-SSP, how we calibrate them, and the typical accuracy we achieve with the HSC five-band photometry (grizy). We introduce a new point estimator based on an improved loss function and demonstrate that it works better than other commonly used estimators. We find that our photo-zs are most accurate at 0.2<~zphot<~1.5, where we can straddle the 4000A break. We achieve sigma(d_zphot/(1+zphot))~0.05 and an outlier rate of about 15% for galaxies down to i=25 within this redshift range. If we limit to a brighter sample of i<24, we achieve sigma~0.04 and ~8% outliers. Our photo-zs should thus enable many science cases for HSC-SSP. We also characterize the accuracy of our redshift probability distribution function (PDF) and discover that some codes over/under-estimate the redshift uncertainties, which have implications for N(z) reconstruction. Our photo-z products for the entire area in the Public Data Release 1 are publicly available, and both our catalog products (such as point estimates) and full PDFs can be retrieved from the data release site, https://hsc-release.mtk.nao.ac.jp/.
We present a description of the second data release for the photometric redshift (photo-$z$) of the Subaru Strategic Program for the Hyper-Suprime Cam survey. Our photo-$z$ products for the entire area in the Data Release 2 are publicly available, and both our point estimate catalog products and full PDFs can be retrieved from the data release site, url{https://hsc-release.mtk.nao.ac.jp/}.
We present the photometric properties of a sample of infrared (IR) bright dust obscured galaxies (DOGs). Combining wide and deep optical images obtained with the Hyper Suprime-Cam (HSC) on the Subaru Telescope and all-sky mid-IR (MIR) images taken with Wide-Field Infrared Survey Explorer (WISE), we discovered 48 DOGs with $i - K_mathrm{s} > 1.2$ and $i - [22] > 7.0$, where $i$, $K_mathrm{s}$, and [22] represent AB magnitude in the $i$-band, $K_mathrm{s}$-band, and 22 $mu$m, respectively, in the GAMA 14hr field ($sim$ 9 deg$^2$). Among these objects, 31 ($sim$ 65 %) show power-law spectral energy distributions (SEDs) in the near-IR (NIR) and MIR regime, while the remainder show a NIR bump in their SEDs. Assuming that the redshift distribution for our DOGs sample is Gaussian, with mean and sigma $z$ = 1.99 $pm$ 0.45, we calculated their total IR luminosity using an empirical relation between 22 $mu$m luminosity and total IR luminosity. The average value of the total IR luminosity is (3.5 $pm$ 1.1) $times$ $10^{13}$ L$_{odot}$, which classifies them as hyper-luminous infrared galaxies (HyLIRGs). We also derived the total IR luminosity function (LF) and IR luminosity density (LD) for a flux-limited subsample of 18 DOGs with 22 $mu$m flux greater than 3.0 mJy and with $i$-band magnitude brighter than 24 AB magnitude. The derived space density for this subsample is log $phi$ = -6.59 $pm$ 0.11 [Mpc$^{-3}$]. The IR LF for DOGs including data obtained from the literature is well fitted by a double-power law. The derived lower limit for the IR LD for our sample is $rho_{mathrm{IR}}$ $sim$ 3.8 $times$ 10$^7$ [L$_{odot}$ Mpc$^{-3}$] and its contributions to the total IR LD, IR LD of all ultra-luminous infrared galaxies (ULIRGs), and that of all DOGs are $>$ 3 %, $>$ 9 %, and $>$ 15 %, respectively.
We report an automated morphological classification of galaxies into S-wise spirals, Z-wise spirals, and non-spirals using big image data taken from Subaru/Hyper Suprime-Cam (HSC) Survey and a convolutional neural network(CNN)-based deep learning technique. The HSC i-band images are about 25 times deeper than those from the Sloan Digital Sky Survey (SDSS) and have a two times higher spatial resolution, allowing us to identify substructures such as spiral arms and bars in galaxies at z>0.1. We train CNN classifiers by using HSC images of 1447 S-spirals, 1382 Z-spirals, and 51,650 non-spirals. As the number of images in each class is unbalanced, we augment the data of spiral galaxies by horizontal flipping, rotation, and rescaling of images to make the numbers of three classes similar. The trained CNN models correctly classify 97.5% of the validation data, which is not used for training. We apply the CNNs to HSC images of a half million galaxies with an i-band magnitude of i<20 over an area of 320 deg^2. 37,917 S-spirals and 38,718 Z-spirals are identified, indicating no significant difference between the numbers of two classes. Among a total of 76,635 spiral galaxies, 48,576 are located at z>0.2, where we are hardly able to identify spiral arms in the SDSS images. Our attempt demonstrates that a combination of the HSC big data and CNNs has a large potential to classify various types of morphology such as bars, mergers and strongly-lensed objects.
The relationship between quasars and their host galaxies provides clues on how supermassive black holes (SMBHs) and massive galaxies are jointly assembled. To elucidate this connection, we measure the structural and photometric properties of the host galaxies of ~5000 SDSS quasars at 0.2<z<1 using five-band (grizy) optical imaging from the Hyper Suprime-Cam Subaru Strategic Program. An automated analysis tool is used to forward-model the blended emission of the quasar as characterized by the point spread function and the underlying host galaxy as a two-dimensional Sersic profile. In agreement with previous studies, quasars are preferentially hosted by massive star-forming galaxies with disk-like light profiles. Furthermore, we find that the size distribution of quasar hosts is broad at a given stellar mass and the average values exhibit a size-stellar mass relation as seen with inactive galaxies. In contrast, the sizes of quasar hosts are more compact than inactive star-forming galaxies on average, but not as compact as quiescent galaxies of similar stellar masses. This is true irrespective of quasar properties including bolometric luminosity, Eddington ratio, and black hole mass. These results are consistent with a scenario in which galaxies are concurrently fueling a SMBH and building their stellar bulge from a centrally-concentrated gas reservoir. Alternatively, quasar hosts may be experiencing a compaction process in which stars from the disk and inflowing gas are responsible for growing the bulge. In addition, we confirm that the host galaxies of type-1 quasars have a bias of being closer towards face-on, suggesting that galactic-scale dust can contribute to obscuring the broad-line region.
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