15 research outputs found

    DenseLens -- Using DenseNet ensembles and information criteria for finding and rank-ordering strong gravitational lenses,

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    Convolutional neural networks (CNNs) are the state-of-the-art technique for identifying strong gravitational lenses. Although they are highly successful in recovering genuine lens systems with a high true-positive rate, the unbalanced nature of the data set (lens systems are rare), still leads to a high false positive rate. For these techniques to be successful in upcoming surveys (e.g. with Euclid) most emphasis should be set on reducing false positives, rather than on reducing false negatives. In this paper, we introduce densely connected neural networks (DenseNets) as the CNN architecture in a new pipeline-ensemble model containing an ensemble of classification CNNs and regression CNNs to classify and rank-order lenses, respectively. We show that DenseNets achieve comparable true positive rates but considerably lower false positive rates (when compared to residual networks; ResNets). Thus, we recommend DenseNets for future missions involving large data sets, such as Euclid, where low false positive rates play a key role in the automated follow-up and analysis of large numbers of strong gravitational lens candidates when human vetting is no longer feasibl

    The Fornax Deep Survey (FDS) with the VST. XI. The search for signs of preprocessing between the Fornax main cluster and Fornax A group

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    Context. Galaxies either live in a cluster, a group, or in a field environment. In the hierarchical framework, the group environment bridges the field to the cluster environment, as field galaxies form groups before aggregating into clusters. In principle, environmental mechanisms, such as galaxy-galaxy interactions, can be more efficient in groups than in clusters due to lower velocity dispersion, which lead to changes in the properties of galaxies. This change in properties for group galaxies before entering the cluster environment is known as preprocessing. Whilst cluster and field galaxies are well studied, the extent to which galaxies become preprocessed in the group environment is unclear. Aims: We investigate the structural properties of cluster and group galaxies by studying the Fornax main cluster and the infalling Fornax A group, exploring the effects of galaxy preprocessing in this showcase example. Additionally, we compare the structural complexity of Fornax galaxies to those in the Virgo cluster and in the field. Methods: Our sample consists of 582 galaxies from the Fornax main cluster and Fornax A group. We quantified the light distributions of each galaxy based on a combination of aperture photometry, Sérsic+PSF (point spread function) and multi-component decompositions, and non-parametric measures of morphology. From these analyses, we derived the galaxy colours, structural parameters, non-parametric morphological indices (Concentration C; Asymmetry A, Clumpiness S; Gini G; second order moment of light M20), and structural complexity based on multi-component decompositions. These quantities were then compared between the Fornax main cluster and Fornax A group. The structural complexity of Fornax galaxies were also compared to those in Virgo and in the field. Results: We find significant (Kolmogorov-Smirnov test p-value < α = 0.05) differences in the distributions of quantities derived from Sérsic profiles (g′‒r′, r′‒i′, Re, and μ̄e,r′), and non-parametric indices (A and S) between the Fornax main cluster and Fornax A group. Fornax A group galaxies are typically bluer, smaller, brighter, and more asymmetric and clumpy. Moreover, we find significant cluster-centric trends with r′‒i′, Re, and μ̄e,r′, as well as A, S, G, and M20 for galaxies in the Fornax main cluster. This implies that galaxies falling towards the centre of the Fornax main cluster become fainter, more extended, and generally smoother in their light distribution. Conversely, we do not find significant group-centric trends for Fornax A group galaxies. We find the structural complexity of galaxies (in terms of the number of components required to fit a galaxy) to increase as a function of the absolute r′-band magnitude (and stellar mass), with the largest change occurring between ‒14 mag ≲Mr′ ≲ ‒19 mag (7.5 ≲ log10(M*/M⊙) ≲ 9.7). This same trend was found in galaxy samples from the Virgo cluster and in the field, which suggests that the formation or maintenance of morphological structures (e.g., bulges, bar) are largely due to the stellar mass of the galaxies, rather than the environment they reside in. Full Tables 2, 3, and I.1 are only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/cat/J/A+A/647/A10

    Radio emission in cooling flows

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    It is reviewed whether cooling flows could fuel central radio sources in spheroidal galaxies as a general, universal mechanism. The radio luminosity functions of S0, E, gE and cD galaxies show a similar scaled dependence on Lopt, demanding a non-exclusive radio powering mechanism for cD's. If cooling flows generate the central radio sources in these objects, then a non-exclusive X-ray halo formation process should occur. Gravitational accretion of gas from the intergalactic medium is identified as such a mechanism for both E+S0's and cD's. From studying a sample of 110 objects the author finds strong evidence for gravitationally driven cooling flows fuelling central radio sources in spheroidal galaxies

    Opaque spiral galaxies

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    The photographic images of a carefully selected sample of about 16,000 galaxies have been digitized, and an extensive set of photometric parameters has been determined. These data allow a reanalysis of the effective transparency using the measured surface brightness profiles for much larger, more strictly defined samples. Several tests provide evidence that the major parts of many spiral disks are opaque and that in many cases perhaps only the outer layer of stars is observable. This invalidates most determinations of mass-to-light ratios. Various photometric properties point to an obscuring component with a larger exponential scale length than obscuring component with a larger exponential scale length than that of the stars, possibly composed of cool compact opaque clouds. This result diminishes the evidence for weak haloes around spirals, as inferred from rotation curves

    Astro-WISE and Grid

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    <p>The paper reviews the Astro-WISE infrastructure and demonstrates that the Astro-WISE Information System provides a Grid itself. We describe the integration of Astro-WISE with an external Grid infrastructure (BiGGrid). The integration is performed on all infrastructural layers (data storage, metadata and processing layers) with Astro-WISE as a "master" infrastructure. We report the use of the integrated infrastructure for the processing of Astro-WISE hosted data and for the future development of Astro-WISE and Target projects.</p>

    DenseLens - Using DenseNet ensembles and information criteria for finding and rank-ordering strong gravitational lenses

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    Convolutional neural networks (CNNs) are the state-of-the-art technique for identifying strong gravitational lenses. Although they are highly successful in recovering genuine lens systems with a high true-positive rate, the unbalanced nature of the data set (lens systems are rare), still leads to a high false positive rate. For these techniques to be successful in upcoming surveys (e.g. with Euclid) most emphasis should be set on reducing false positives, rather than on reducing false negatives. In this paper, we introduce densely connected neural networks (DenseNets) as the CNN architecture in a new pipeline-ensemble model containing an ensemble of classification CNNs and regression CNNs to classify and rank-order lenses, respectively. We show that DenseNets achieve comparable true positive rates but considerably lower false positive rates (when compared to residual networks; ResNets). Thus, we recommend DenseNets for future missions involving large data sets, such as Euclid, where low false positive rates play a key role in the automated follow-up and analysis of large numbers of strong gravitational lens candidates when human vetting is no longer feasible.</p
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