9,339 research outputs found

    New Techniques for High-Contrast Imaging with ADI: the ACORNS-ADI SEEDS Data Reduction Pipeline

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    We describe Algorithms for Calibration, Optimized Registration, and Nulling the Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelized software package to reduce high-contrast imaging data, and its application to data from the SEEDS survey. We implement several new algorithms, including a method to register saturated images, a trimmed mean for combining an image sequence that reduces noise by up to ~20%, and a robust and computationally fast method to compute the sensitivity of a high-contrast observation everywhere on the field-of-view without introducing artificial sources. We also include a description of image processing steps to remove electronic artifacts specific to Hawaii2-RG detectors like the one used for SEEDS, and a detailed analysis of the Locally Optimized Combination of Images (LOCI) algorithm commonly used to reduce high-contrast imaging data. ACORNS-ADI is written in python. It is efficient and open-source, and includes several optional features which may improve performance on data from other instruments. ACORNS-ADI requires minimal modification to reduce data from instruments other than HiCIAO. It is freely available for download at www.github.com/t-brandt/acorns-adi under a BSD license.Comment: 15 pages, 9 figures, accepted to ApJ. Replaced with accepted version; mostly minor changes. Software update

    Segmentation and intensity estimation for microarray images with saturated pixels

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    <p>Abstract</p> <p>Background</p> <p>Microarray image analysis processes scanned digital images of hybridized arrays to produce the input spot-level data for downstream analysis, so it can have a potentially large impact on those and subsequent analysis. Signal saturation is an optical effect that occurs when some pixel values for highly expressed genes or peptides exceed the upper detection threshold of the scanner software (2<sup>16 </sup>- 1 = 65, 535 for 16-bit images). In practice, spots with a sizable number of saturated pixels are often flagged and discarded. Alternatively, the saturated values are used without adjustments for estimating spot intensities. The resulting expression data tend to be biased downwards and can distort high-level analysis that relies on these data. Hence, it is crucial to effectively correct for signal saturation.</p> <p>Results</p> <p>We developed a flexible mixture model-based segmentation and spot intensity estimation procedure that accounts for saturated pixels by incorporating a censored component in the mixture model. As demonstrated with biological data and simulation, our method extends the dynamic range of expression data beyond the saturation threshold and is effective in correcting saturation-induced bias when the lost information is not tremendous. We further illustrate the impact of image processing on downstream classification, showing that the proposed method can increase diagnostic accuracy using data from a lymphoma cancer diagnosis study.</p> <p>Conclusions</p> <p>The presented method adjusts for signal saturation at the segmentation stage that identifies a pixel as part of the foreground, background or other. The cluster membership of a pixel can be altered versus treating saturated values as truly observed. Thus, the resulting spot intensity estimates may be more accurate than those obtained from existing methods that correct for saturation based on already segmented data. As a model-based segmentation method, our procedure is able to identify inner holes, fuzzy edges and blank spots that are common in microarray images. The approach is independent of microarray platform and applicable to both single- and dual-channel microarrays.</p

    The stellar halo of isolated central galaxies in the Hyper Suprime-Cam imaging survey

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    We study the faint stellar halo of isolated central galaxies, by stacking galaxy images in the HSC survey and accounting for the residual sky background sampled with random points. The surface brightness profiles in HSC rr-band are measured for a wide range of galaxy stellar masses (9.2<log10M/M<11.49.2<\log_{10}M_\ast/M_\odot<11.4) and out to 120 kpc. Failing to account for the stellar halo below the noise level of individual images will lead to underestimates of the total luminosity by 15%\leq 15\%. Splitting galaxies according to the concentration parameter of their light distributions, we find that the surface brightness profiles of low concentration galaxies drop faster between 20 and 100 kpc than those of high concentration galaxies. Albeit the large galaxy-to-galaxy scatter, we find a strong self-similarity of the stellar halo profiles. They show unified forms once the projected distance is scaled by the halo virial radius. The colour of galaxies is redder in the centre and bluer outside, with high concentration galaxies having redder and more flattened colour profiles. There are indications of a colour minimum, beyond which the colour of the outer stellar halo turns red again. This colour minimum, however, is very sensitive to the completeness in masking satellite galaxies. We also examine the effect of the extended PSF in the measurement of the stellar halo, which is particularly important for low mass or low concentration galaxies. The PSF-corrected surface brightness profile can be measured down to \sim31 mag/arcsec2\mathrm{mag}/\mathrm{arcsec}^2 at 3-σ\sigma significance. PSF also slightly flattens the measured colour profiles.Comment: accepted by MNRAS - Significant changes have been made compared with the first version, including discussions on the extended PSF wings, robustness of our results to source detection and masking thresholds and more detailed investigations on the indications of positive colour gradient

    Star Cluster Candidates in M81

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    We present a catalog of extended objects in the vicinity of M81 based a set of 24 Hubble Space Telescope Advanced Camera for Surveys (ACS) Wide Field Camera (WFC) F814W (I-band) images. We have found 233 good globular cluster candidates; 92 candidate HII regions, OB associations, or diffuse open clusters; 489 probable background galaxies; and 1719 unclassified objects. We have color data from ground-based g- and r-band MMT Megacam images for 79 galaxies, 125 globular cluster candidates, 7 HII regions, and 184 unclassified objects. The color-color diagram of globular cluster candidates shows that most fall into the range 0.25 < g-r < 1.25 and 0.5 < r-I < 1.25, similar to the color range of Milky Way globular clusters. Unclassified objects are often blue, suggesting that many of them are likely to be HII regions and open clusters, although a few galaxies and globular clusters may be among them.Comment: 35 pages, 11 figures, submitted to A
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