1,721,955 research outputs found
Wide Field Imaging. I. Applications of Neural Networks to Object Detection and Star/Galaxy Classification
No full textAstronomical wide-field imaging performed with new large-format CCD detectors poses data reduction problems of unprecedented scale, which are difficult to deal with using traditional interactive tools. We present here NEXT (Neural Extractor), a new neural network (NN) based package capable of detecting objects and performing both deblending and star/ galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first distinguished from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold; they are then classified as stars or as galaxies through diagnostic diagrams having variables chosen according to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NEXT requires only the simplest a priori definition of 'what an object is' (i.e. it keeps all structures composed of more than one pixel) and performs the detection via an unsupervised NN, approaching detection as a clustering problem that has been thoroughly studied in the artificial intelligence literature. The first part of the NEXT procedure consists of an optimal compression of the redundant information contained in the pixels via a mapping from pixel intensities to a subspace individualized through principal component analysis. At magnitudes fainter than the completeness limit, stars are usually almost indistinguishable from galaxies, and therefore the parameters characterizing the two classes do not lie in disconnected subspaces, thus preventing the use of unsupervised methods. We therefore adopted a supervised NN (i.e. a NN that first finds the rules to classify objects from examples and then applies them to the whole data set). In practice, each object is classified depending on its membership of the regions mapping the input feature space in the training set. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features we use a NN to select the most significant features among the large number of measured ones, and then we use these selected features to perform the classification task. In order to optimize the performance of the system, we implemented and tested several different models of NN. The comparison of the NEXT performance with that of the best detection and classification package known to the authors (SEXTRACTOR) shows that NEXT is at least as effective as the best traditional packages
NExt (Neural Extractor): a new automated tool to extract catalogues from astronomical images
No full textRigorous luminosity function determination in the presence of a background: theory and application to two intermediate redshift clusters
No full textthis paper we present a rigorous derivation of the luminosity function (LF) in the presence of a background. Our approach is free from the logical contradictions of assigning negative values to positively defined quantities and avoids the use of incorrect estimates for the 68 per cent confidence interval (error bar). It accounts for Poisson fluctuations ignored in previous approaches and does not require binning of the data. The method is extensible to more complex situations, does not require the existence of an environment-independent LF, and clarifies issues common to field LF derivations. We apply the method to two clusters of galaxies at intermediate redshift (z similar to 0.3) with among the deepest and widest K-s observations ever taken. Finally, we point out the shortcomings of flip-flopping magnitudes
Constraining dark energy models using the lookback time to galaxy clusters and the age of the universe.
No full textConstraining dark energy models using the lookback time to galaxy clusters and the age of the universe
No full textWide Field Imaging. I. Applications of Neural Networks to object detection and star/galaxy separation
No full textWide Field Imaging. I. Applications of Neural Networks to object detection and star/galaxy separation
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
A Serverless Architecture for Efficient and Scalable Monte Carlo Markov Chain Computation
Full text linkComputer power is a constantly increasing demand in scientific data analyses,
in particular when Markov Chain Monte Carlo (MCMC) methods are involved, for
example for estimating integral functions or Bayesian posterior probabilities.
In this paper, we describe the benefits of a parallel computation of MCMC using
a cloud-based, serverless architecture: first, the computation time can be
spread over thousands of processes, hence greatly reducing the time the user
should wait to have its computation completed. Second, the overhead time
required for running in parallel several processes is minor and grows
logarithmically with respect to the number of processes. Third, the serverless
approach does not require time-consuming efforts for maintaining and updating
the computing infrastructure when/if the number of walkers increases or for
adapting the code to optimally use the infrastructure. The benefits are
illustrated with the computation of the posterior probability distribution of a
real astronomical analysis.Comment: 6 pages, 3 figures. Appeared in ICCBDC '23: Proceedings of the 2023
7th International Conference on Cloud and Big Data Computing - August 202
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