村田 一心

ピンホールSPECT システムでは，ピンホール径に依存して再構成画像に空間分解能の低下が発生する．従来，このようなピンホール開口に起因するボケは，検出確率に基づくシステムマトリックスを求め補正する，あるいは投影線の数を増大させて改善するなどの方法がとられてきた．しかし，前者は画素が小さいときやピンホール数が増大すると計算負荷が大きくなるという欠点があり，後者はピンホール径が大きくなると空間分解能が十分に改善できないという問題があった．そこで本研究では，深層学習（U-net, U-net++）を用いてピンホールによる空間分解能の低下を低減する方法を考案した．具体的には，空間分解能が低下した投影データを入力データとし，理想的な無限小ピンホールの投影データを教師データとして学習させる畳み込みネットワークを実装し，空間分解能の低下を抑制した．シミュレーション結果からPSNR 値で画質を評価すると，提案手法（U-net, U-net++）はそれぞれ17.48, 17.92 dB となり，従来法である21-rays 法の16.82 dB と比較して，空間分解能を改善できることが明らかとなった．

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/〉.

本研究では可視面分光サーベイMaNGAデータを用いて、近傍棒渦巻銀河の星種族年齢分布を調査した。サンプルセレクションにはGalaxy Zooカタログを利用し、顕著な棒構造を持つ棒銀河サンプルと、棒構造を持たないコントロールサンプルを作成した。星質量ごとの傾向を調べるため、これらのサンプルをlogM/Msun = 10.0-10.5, 10.5-11.0, 11.0-11.5の3グループに分類した。各グループにおける天体数は、棒銀河サンプルが5, 24, 19天体、コントロールサンプルが25, 48, 38天体となった。棒銀河サンプルの光度加重平均年齢分布は、中心の3 kpcで平坦となり、外側の領域ではコントロールサンプルより0.2-0.4桁高かった。これは、棒状構造によって中心では星形成活動が誘発され、外側では低下したことを示す。一方、質量加重平均年齢の分布は両サンプルとも全グループにおいて中心ほど増加した。この傾向は、棒状構造を持っていても、他の銀河と同様にインサイド-アウトの進化をすることを示す。

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.