Kazumi Murata

In a pinhole SPECT system, degradation of the spatial resolution occurs in reconstructed images depending on the pinhole aperture. Conventionally, blurring caused by such pinhole apertures has been corrected by obtaining a system matrix based on the detection probability or by increasing the number of projection rays in a ray driven method. However, the former has the drawback that the computational load increases when the pixel size is small or the number of pinholes increases, while the latter has the problem that the spatial resolution cannot be sufficiently improved when the pinhole diameter increases. Therefore, in this study, we proposed a method to reduce the deterioration of the spatial resolution due to pinholes using deep learning. Specifically, we implemented convolutional neural networks (U-net and U-net++) that learns projection data with reduced spatial resolution as input data and ideal infinitesimal pinhole projection data as training data to suppress the degradation of the spatial resolution. Simulation results showed that the peak signal-to-noise ratios of the corrected image with the U-net or U-net++ based method were 17.48 and 17.92 dB, respectively, and that with the conventional 21-rays method was 16.82 dB. It was clarified that the proposed methods can improve the spatial resolution compared with the conventional method.

Abstract We propose a regularization-based deblurring method that works efficiently for galaxy images. The spatial resolution of a ground-based telescope is generally limited by seeing conditions and is much worse than space-based telescopes. This circumstance has generated considerable research interest in the restoration of spatial resolution. Since image deblurring is a typical inverse problem and often ill-posed, solutions tend to be unstable. To obtain a stable solution, much research has adopted regularization-based methods for image deblurring, but the regularization term is not necessarily appropriate for galaxy images. Although galaxies have an exponential or Sérsic profile, the conventional regularization assumes the image profiles to behave linearly in space. The significant deviation between the assumption and real situations leads to blurring of the images and smoothing out the detailed structures. Clearly, regularization on logarithmic domain, i.e., magnitude domain, should provide a more appropriate assumption, which we explore in this study. We formulate a problem of deblurring galaxy images by an objective function with a Tikhonov regularization term on a magnitude domain. We introduce an iterative algorithm minimizing the objective function with a primal–dual splitting method. We investigate the feasibility of the proposed method using simulation and observation images. In the simulation, we blur galaxy images with a realistic point spread function and add both Gaussian and Poisson noise. For the evaluation with the observed images, we use galaxy images taken by the Subaru HSC-SSP. Both of these evaluations show that our method successfully recovers the spatial resolution of the deblurred images and significantly outperforms the conventional methods. The code is publicly available from the GitHub 〈https://github.com/kzmurata-astro/PSFdeconv_amag〉.

Abstract We present the launch of the Hyper Suprime-Cam Legacy Archive (HSCLA), a public archive of processed, science-ready data from the Hyper Suprime-Cam (HSC). HSC is an optical wide-field imager installed at the prime focus of the Subaru Telescope and has been in operation since 2014. While ∼1/3 of the total observing time of HSC has been used for the Subaru Strategic Program (SSP), the remainder of the time is used for Principal Investigator (PI)-based programs. We have processed the data from these PI-based programs and make the processed, high-quality data available to the community through HSCLA. The current version of HSCLA includes data taken in the first year of science operation, 2014. We provide both individual and coadd images as well as photometric catalogs. The photometric catalog from the coadd is loaded to the database, which offers a fast access to the large catalog. There are other online tools such as an image browser and an image cutout tool and they will be useful for science analyses. The coadd images reach 24–27th magnitudes at 5σ for point sources and cover approximately 580 square degrees in at least one filter with 150 million objects in total. We perform extensive quality assurance tests and verify that the photometric and astrometric quality of the data is good enough for most scientific explorations. However, the data are not without problems and users are referred to the list of known issues before exploiting the data for science. All the data and documentations can be found at the data release site, 〈https://hscla.mtk.nao.ac.jp/〉.

This study investigates radial age profiles of local barred and unbarred galaxies using Mapping Nearby Galaxies at APO (MaNGA) survey data. Galaxies with prominent bars were selected from the Galaxy Zoo catalogue as a barred sample, and those without bars as a control sample. These samples were divided into three stellar-mass bins, log M/Msun = 10.0-10.5, 10.5-11.0, and 11.0-11.5, respectively. The numbers of galaxies in each bin were 5, 24, and 19 for the barred sample, and 25, 48, and 38 for the mass-matched control sample, respectively. The luminosity-weighted age profiles of barred galaxies were flat at central 3 kpc regions and were 0.2-0.4 dex higher than the control sample at outer regions. This indicates bar-induced star-formation in the central regions and bar-quenching in the outer regions. On the other hand, the mass-weighted age profiles of both samples monotonically increased towards the central regions. This suggests that barred galaxies also follow inside-out quenching, regardless of bar-induced central star-formation and/or bar-quenching of disk regions.

The aim of this work is to create a new catalog of reliable AGN candidates selected from the AKARI NEP-Deep field. Selection of the AGN candidates was done by applying a fuzzy SVM algorithm, which allows to incorporate measurement uncertainties into the classification process. The training dataset was based on the spectroscopic data available for selected objects in the NEP-Deep and NEP-Wide fields. The generalization sample was based on the AKARI NEP-Deep field data including objects without optical counterparts and making use of the infrared information only. A high quality catalog of previously unclassified 275 AGN candidates was prepared.

Abstract The Japanese infrared astronomical satellite AKARI performed ∼4000 pointed observations for 16 months until the end of August 2007, when the telescope and instruments were cooled by liquid helium. Observation targets include solar system objects, Galactic objects, local galaxies, and galaxies at cosmological distances. We describe recent updates on calibration processes of near- and mid-infrared images taken by the Infrared Camera (IRC), which has nine photometric filters covering 2–27 μm continuously. Using the latest data reduction toolkit, we created calibrated and stacked images from each pointed observation. About 90% of the stacked images have a position accuracy better than 1${^{\prime\prime}_{. } }$5. Uncertainties in aperture photometry estimated from a typical standard sky deviation of stacked images are a factor of ∼2–4 smaller than those of AllWISE at similar wavelengths. The processed images, together with documents such as process logs, as well as the latest toolkit are available online.