• 한국우주과학회:학술대회논문집(한국우주과학회보)
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• 한국우주과학회 2009년도 한국우주과학회보 제18권2호
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• pp.30.2-30.2
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• 2009

There is a growing body of evidence for the presence of multiple stellar populations in some globular clusters, including NGC 1851. For most of these peculiar globular clusters, however, the evidence for the multiple red giant-branches (RGBs) having different heavy elemental abundances as observed in $\omega$ Centauri is hitherto lacking, although spreads in some lighter elements are reported. It is therefore not clear whether they also share the suggested dwarf galaxy origin of $\omega$ Cen or not. Here we show from the CTIO 4m UVI photometry of the globular cluster NGC 1851 that its RGB is clearly split into two in the U - I color. The two distinct RGB populations are also clearly separated in the abundance of heavy elements as traced by Calcium, suggesting that the type II supernovae enrichment is also responsible, in addition to the pollutions of lighter elements by intermediate mass asymptotic giant branch stars or fast-rotating massive stars. The RGB split, however, is not shown in the V - I color, as indicated by previous observations. Our stellar population models show that this and the presence of bimodal horizontal-branch distribution in NGC 1851 can be naturally reproduced if the metal-rich second generation stars are also enhanced in helium.

• 천문학회보
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• 제44권1호
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• pp.54.2-54.2
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• 2019

Removing noise which occurs inevitably when taking image data has been a big concern. There is a way to raise signal-to-noise ratio and it is regarded as the only way, image stacking. Image stacking is averaging or just adding all pixel values of multiple pictures taken of a specific area. Its performance and reliability are unquestioned, but its weaknesses are also evident. Object with fast proper motion can be vanished, and most of all, it takes too long time. So if we can handle single shot image well and achieve similar performance, we can overcome those weaknesses. Recent developments in deep learning have enabled things that were not possible with former algorithm-based programming. One of the things is generating data with more information from data with less information. As a part of that, we reproduced stacked image from single shot image using a kind of deep learning, conditional generative adversarial network (cGAN). r-band camcol2 south data were used from SDSS Stripe 82 data. From all fields, image data which is stacked with only 22 individual images and, as a pair of stacked image, single pass data which were included in all stacked image were used. All used fields are cut in $128{\times}128$ pixel size, so total number of image is 17930. 14234 pairs of all images were used for training cGAN and 3696 pairs were used for verify the result. As a result, RMS error of pixel values between generated data from the best condition and target data were $7.67{\times}10^{-4}$ compared to original input data, $1.24{\times}10^{-3}$. We also applied to a few test galaxy images and generated images were similar to stacked images qualitatively compared to other de-noising methods. In addition, with photometry, The number count of stacked-cGAN matched sources is larger than that of single pass-stacked one, especially for fainter objects. Also, magnitude completeness became better in fainter objects. With this work, it is possible to observe reliably 1 magnitude fainter object.

• 천문학회보
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• 제37권2호
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• pp.81-81
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• 2012

Since the ultraviolet (UV) flux of an integrated population is a good tracer of recent star formation activities, UV observations provide an important constraint on star formation history (SFH) in galaxies. We present UV color-magnitude relations (CMRs) of early-type dwarf galaxies in the Virgo cluster, based on Galaxy Evolution Explorer (GALEX) UV data and the Extended Virgo Cluster Catalog (EVCC, Kim, S. in prep.). The EVCC covers an area 5.4 times larger (750 deg2) than the footprint of the classical Virgo cluster catalog by Binggeli and collaborators. We secure 1304 galaxies as members of the Virgo cluster and 526 galaxies of them are new objects not contained in the VCC. Morphological classification of galaxies in the EVCC is based on the optical image ("Primary Classification") and spectral feature ("Secondary Classification") of the SDSS data. We find that dwarf lenticular galaxies (dS0s) show a surprisingly distinct and tight locus separated from that of ordinary dwarf elliptical galaxies (dEs), which is not clearly seen in previous CMRs. The dS0s in UV CMRs follow a steeper sequence than dEs and show bluer UV-optical color at a given magnitude. Most early type dwarf galaxies with blue UV colors (FUV-r < 6 and NUV-r < 4) are identified as those showing spectroscopic hints of recent or ongoing star formation activities. We explore the observed CMRs with population models of a luminosity-dependent delayed exponential star formation history. The observed CMR of dS0s is well matched with models with relatively long delayed star formation. Our results suggest that dS0s are most likely transitional objects at the stage of subsequent transformation of late-type progenitors to ordinary red dEs in the cluster environment. In any case, UV photometry provides a powerful tool to disentangle the diverse subpopulations of early-type dwarf galaxies and uncover their evolutionary histories.