206 research outputs found

    Deep GALEX Observations of the Coma Cluster: Source Catalog and Galaxy Counts

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    We present a source catalog from deep 26 ks GALEX observations of the Coma cluster in the far-UV (FUV; 1530 A) and near-UV (NUV; 2310 A) wavebands. The observed field is centered 0.9 deg (1.6 Mpc) south-west of the Coma core, and has full optical photometric coverage with SDSS. The catalog consists of 9700 galaxies with GALEX and SDSS photometry, including 242 spectroscopically-confirmed Coma member galaxies that range from giant spirals and elliptical galaxies to dwarf irregular and early-type galaxies. The full multi-wavelength catalog (cluster plus background galaxies) is ~80% complete to NUV=23 and FUV=23.5, and has a limiting depth at NUV=24.5 and FUV=25.0 which corresponds to a star formation rate of ~0.001 Msun/yr at the distance of Coma. Our deep GALEX observations required a two-fold approach to generating a source catalog: we used a Bayesian deblending algorithm to measure faint and compact sources (using SDSS coordinates as a position prior), and relied on the GALEX pipeline catalog for bright/extended objects. We performed simulations to assess the influence that systematic effects (e.g. object blends, source confusion, Eddington Bias) have on source detection and photometry when using both methods. The Bayesian deblending method roughly doubles the number of source detections and provides reliable photometry to a few magnitudes deeper than the GALEX pipeline catalog. This method is also free from source confusion over the UV magnitude range studied here; conversely, we estimate that the GALEX pipeline catalogs are confusion limited at magnitudes fainter than NUV~23 and FUV~24. We have measured the total UV galaxy counts using our catalog and report a ~50% excess of counts across FUV=22-23.5 and NUV=21.5-23 relative to previous GALEX measurements, which is not attributed to cluster member galaxies. Our galaxy counts are a better match to deeper UV counts measured with HST.Comment: 27 pages, 13 figures, 4 tables, accepted for publication in ApJ

    The HST/ACS Coma Cluster Survey. II. Data Description and Source Catalogs

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    The Coma cluster was the target of a HST-ACS Treasury program designed for deep imaging in the F475W and F814W passbands. Although our survey was interrupted by the ACS instrument failure in 2007, the partially completed survey still covers ~50% of the core high-density region in Coma. Observations were performed for 25 fields that extend over a wide range of cluster-centric radii (~1.75 Mpc) with a total coverage area of 274 arcmin^2. The majority of the fields are located near the core region of Coma (19/25 pointings) with six additional fields in the south-west region of the cluster. In this paper we present reprocessed images and SExtractor source catalogs for our survey fields, including a detailed description of the methodology used for object detection and photometry, the subtraction of bright galaxies to measure faint underlying objects, and the use of simulations to assess the photometric accuracy and completeness of our catalogs. We also use simulations to perform aperture corrections for the SExtractor Kron magnitudes based only on the measured source flux and half-light radius. We have performed photometry for ~73,000 unique objects; one-half of our detections are brighter than the 10-sigma point-source detection limit at F814W=25.8 mag (AB). The slight majority of objects (60%) are unresolved or only marginally resolved by ACS. We estimate that Coma members are 5-10% of all source detections, which consist of a large population of unresolved objects (primarily GCs but also UCDs) and a wide variety of extended galaxies from a cD galaxy to dwarf LSB galaxies. The red sequence of Coma member galaxies has a constant slope and dispersion across 9 magnitudes (-21<M_F814W<-13). The initial data release for the HST-ACS Coma Treasury program was made available to the public in 2008 August. The images and catalogs described in this study relate to our second data release.Comment: Accepted for publication in ApJS. A high-resolution version is available at http://archdev.stsci.edu/pub/hlsp/coma/release2/PaperII.pd

    UNBOUND

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    Featured here, are the extraordinary works of our graduating Fanshawe Design class. This accomplishment is truly a celebration of the three years of passion, hard work, and dedication put forth by our students. It is our greatest hope that family, friends and the fashion industry will enjoy the creative endeavors of these emerging designers from the Fashion Design program at Fanshawe College in London, Ontario.https://first.fanshawec.ca/famd_design_fashiondesign_unbound/1001/thumbnail.jp

    Many Labs 5:Testing pre-data collection peer review as an intervention to increase replicability

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    Replication studies in psychological science sometimes fail to reproduce prior findings. If these studies use methods that are unfaithful to the original study or ineffective in eliciting the phenomenon of interest, then a failure to replicate may be a failure of the protocol rather than a challenge to the original finding. Formal pre-data-collection peer review by experts may address shortcomings and increase replicability rates. We selected 10 replication studies from the Reproducibility Project: Psychology (RP:P; Open Science Collaboration, 2015) for which the original authors had expressed concerns about the replication designs before data collection; only one of these studies had yielded a statistically significant effect (p < .05). Commenters suggested that lack of adherence to expert review and low-powered tests were the reasons that most of these RP:P studies failed to replicate the original effects. We revised the replication protocols and received formal peer review prior to conducting new replication studies. We administered the RP:P and revised protocols in multiple laboratories (median number of laboratories per original study = 6.5, range = 3?9; median total sample = 1,279.5, range = 276?3,512) for high-powered tests of each original finding with both protocols. Overall, following the preregistered analysis plan, we found that the revised protocols produced effect sizes similar to those of the RP:P protocols (?r = .002 or .014, depending on analytic approach). The median effect size for the revised protocols (r = .05) was similar to that of the RP:P protocols (r = .04) and the original RP:P replications (r = .11), and smaller than that of the original studies (r = .37). Analysis of the cumulative evidence across the original studies and the corresponding three replication attempts provided very precise estimates of the 10 tested effects and indicated that their effect sizes (median r = .07, range = .00?.15) were 78% smaller, on average, than the original effect sizes (median r = .37, range = .19?.50)

    Act now against new NHS competition regulations: an open letter to the BMA and the Academy of Medical Royal Colleges calls on them to make a joint public statement of opposition to the amended section 75 regulations.

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    Integrated genomic characterization of pancreatic ductal adenocarcinoma

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    We performed integrated genomic, transcriptomic, and proteomic profiling of 150 pancreatic ductal adenocarcinoma (PDAC) specimens, including samples with characteristic low neoplastic cellularity. Deep whole-exome sequencing revealed recurrent somatic mutations in KRAS, TP53, CDKN2A, SMAD4, RNF43, ARID1A, TGFβR2, GNAS, RREB1, and PBRM1. KRAS wild-type tumors harbored alterations in other oncogenic drivers, including GNAS, BRAF, CTNNB1, and additional RAS pathway genes. A subset of tumors harbored multiple KRAS mutations, with some showing evidence of biallelic mutations. Protein profiling identified a favorable prognosis subset with low epithelial-mesenchymal transition and high MTOR pathway scores. Associations of non-coding RNAs with tumor-specific mRNA subtypes were also identified. Our integrated multi-platform analysis reveals a complex molecular landscape of PDAC and provides a roadmap for precision medicine

    The Psychological Science Accelerator's COVID-19 rapid-response dataset

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