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  • Oxford University Press  (39)
  • Wien [u.a.] : Springer  (1)
  • 1
    Monograph available for loan
    Monograph available for loan
    Wien [u.a.] : Springer
    Associated volumes
    Call number: O 7142(309)
    In: Courses and lectures / International Centre for Mechanical Sciences
    Type of Medium: Monograph available for loan
    Pages: 175 S.
    ISBN: 3211821430
    Series Statement: Courses and lectures / International Centre for Mechanical Sciences 309
    Language: English
    Location: Upper compact magazine
    Branch Library: GFZ Library
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  • 2
    Publication Date: 2015-05-21
    Description: We discuss whether modern machine learning methods can be used to characterize the physical nature of the large number of objects sampled by the modern multiband digital surveys. In particular, we applied the MLPQNA (Multi Layer Perceptron with Quasi Newton Algorithm) method to the optical data of the Sloan Digital Sky Survey (SDSS) Data Release 10, investigating whether photometric data alone suffice to disentangle different classes of objects as they are defined in the SDSS spectroscopic classification. We discuss three groups of classification problems: (i) the simultaneous classification of galaxies, quasars and stars; (ii) the separation of stars from quasars; (iii) the separation of galaxies with normal spectral energy distribution from those with peculiar spectra, such as starburst or star-forming galaxies and AGN. While confirming the difficulty of disentangling AGN from normal galaxies on a photometric basis only, MLPQNA proved to be quite effective in the three-class separation. In disentangling quasars from stars and galaxies, our method achieved an overall efficiency of 91.31 per cent and a QSO class purity of ~95 per cent. The resulting catalogue of candidate quasars/AGNs consists of ~3.6 million objects, of which about half a million are also flagged as robust candidates, and will be made available on CDS VizieR facility.
    Print ISSN: 0035-8711
    Electronic ISSN: 1365-2966
    Topics: Physics
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  • 3
    Publication Date: 2016-02-20
    Description: The abundance of compact, massive, early-type galaxies (ETGs) provides important constraints to galaxy formation scenarios. Thanks to the area covered, depth, excellent spatial resolution and seeing, the ESO Public optical Kilo Degree Survey (KiDS), carried out with the VLT Survey Telescope, offers a unique opportunity to conduct a complete census of the most compact galaxies in the Universe. This paper presents a first census of such systems from the first 156 deg 2 of KiDS. Our analysis relies on g -, r - and i -band effective radii ( R e ), derived by fitting galaxy images with point spread function (PSF)-convolved Sérsic models, high-quality photometric redshifts, z phot , estimated from machine learning techniques, and stellar masses, M * , calculated from KiDS aperture photometry. After massiveness ( ${M_{\star }}\gtrsim 8 \times 10^{10}\, \rm {\text{M}_{\odot }}$ ) and compactness ( ${R_{\rm e}}\lesssim 1.5 \, \rm kpc$ in g , r and i bands) criteria are applied, a visual inspection of the candidates plus near-infrared photometry from VIKING-DR1 are used to refine our sample. The final catalogue, to be spectroscopically confirmed, consists of 92 systems in the redshift range z ~ 0.2–0.7. This sample, which we expect to increase by a factor of 10 over the total survey area, represents the first attempt to select massive supercompact ETGs ( MSCGs ) in KiDS. We investigate the impact of redshift systematics in the selection, finding that this seems to be a major source of contamination in our sample. A preliminary analysis shows that MSCGs exhibit negative internal colour gradients, consistent with a passive evolution of these systems. We find that the number density of MSCGs is only mildly consistent with predictions from simulations at z  〉 0.2, while no such system is found at z  〈 0.2.
    Print ISSN: 0035-8711
    Electronic ISSN: 1365-2966
    Topics: Physics
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  • 4
    Publication Date: 2016-02-22
    Description: The exploitation of present and future synoptic (multiband and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient Survey (CRTS), we investigate the classification performance of some well tested methods: Random Forest, MultiLayer Perceptron with Quasi Newton Algorithm and K-Nearest Neighbours, paying special attention to the feature selection phase. In order to do so, several classification experiments were performed. Namely: identification of cataclysmic variables, separation between galactic and extragalactic objects and identification of supernovae.
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  • 5
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    Oxford University Press
    Publication Date: 2011-11-24
    Description: How do we prove true but unprovable propositions? Gödel produced a statement whose undecidability derives from its ad hoc construction. Concrete or mathematical incompleteness results are interesting unprovable statements of formal arithmetic. We point out where exactly the unprovability lies in the ordinary ‘mathematical’ proofs of two interesting formally unprovable propositions, the Kruskal-Friedman theorem on trees and Girard's normalization theorem in type theory. Their validity is based on robust cognitive performances, which ground mathematics in our relation to space and time, such as symmetries and order, or on the generality of Herbrand's notion of ‘prototype proof’.
    Print ISSN: 0031-8019
    Electronic ISSN: 1744-6406
    Topics: Mathematics , Philosophy
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  • 6
    Publication Date: 2013-12-06
    Description: In this paper, we discuss an application of machine-learning-based methods to the identification of candidate active galactic nucleus (AGN) from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine-learning algorithms, namely the Multi Layer Perceptron, trained, respectively, with the Conjugate Gradient, the Scaled Conjugate Gradient, the Quasi Newton learning rules and the Support Vector Machines, to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs versus non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features, we discuss also the behaviour of the classifiers on finer AGN classification tasks, namely Seyfert I versus Seyfert II, and Seyfert versus LINER. Furthermore, we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure followed to select the photometric parameters and the performances of our methods in terms of multiple statistical indicators. The results of the experiments show that the application of self-adaptive data mining algorithms trained on spectroscopic data sets and applied to carefully chosen photometric parameters represents a viable alternative to the classical methods that employ time-consuming spectroscopic observations.
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  • 7
    Publication Date: 2014-03-25
    Description: The number of cosmic strings in the observable Universe is relevant in determining the probability of detecting such cosmic defects through their gravitational signatures. In particular, we refer to the observation of gravitational lensing events and anisotropy in the cosmic microwave background (CMB) radiation induced by cosmic strings. In this article, a simple method is adopted to obtain an approximate estimate of the number of segments of cosmic strings crossing the particle horizon that fall inside the observed part of the Universe. We show that there is an appreciable difference in the expected number of segments that differentiates cosmic strings arising in Abelian Higgs and Nambu–Goto models and that a different choice of setting for the cosmological model can lead to significant differences in the expected number of cosmic string segments. Of this number, the fraction that are realistically detectable may be considerably smaller.
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    Topics: Physics
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  • 8
    Publication Date: 2016-12-13
    Description: A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo- z ). A wide plethora of methods have been developed, based either on template models fitting or on empirical explorations of the photometric parameter space. Machine-learning-based techniques are not explicitly dependent on the physical priors and able to produce accurate photo- z estimations within the photometric ranges derived from the spectroscopic training set. These estimates, however, are not easy to characterize in terms of a photo- z probability density function (PDF), due to the fact that the analytical relation mapping the photometric parameters on to the redshift space is virtually unknown. We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method designed to provide a reliable PDF of the error distribution for empirical techniques. The method is implemented as a modular workflow, whose internal engine for photo- z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine-learning model chosen to predict photo- z . We present a summary of results on SDSS-DR9 galaxy data, used also to perform a direct comparison with PDFs obtained by the L e P hare spectral energy distribution template fitting. We show that METAPHOR is capable to estimate the precision and reliability of photometric redshifts obtained with three different self-adaptive techniques, i.e. MLPQNA, Random Forest and the standard K-Nearest Neighbors models.
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    Topics: Physics
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  • 9
    Publication Date: 2015-07-31
    Description: We have estimated photometric redshifts ( z phot ) for more than 1.1 million galaxies of the public European Southern Observatory (ESO) Kilo-Degree Survey (KiDS) data release 2. KiDS is an optical wide-field imaging survey carried out with the Very Large Telescope (VLT) Survey Telescope (VST) and the OmegaCAM camera, which aims to tackle open questions in cosmology and galaxy evolution, such as the origin of dark energy and the channel of galaxy mass growth. We present a catalogue of photometric redshifts obtained using the Multi-Layer Perceptron with Quasi-Newton Algorithm (MLPQNA) model, provided within the framework of the DAta Mining and Exploration Web Application REsource (DAMEWARE). These photometric redshifts are based on a spectroscopic knowledge base that was obtained by merging spectroscopic data sets from the Galaxy and Mass Assembly (GAMA) data release 2 and the Sloan Digital Sky Survey III (SDSS-III) data release 9. The overall 1 uncertainty on z  = ( z spec  –  z phot )/(1 +  z spec ) is ~0.03, with a very small average bias of ~0.001, a normalized median absolute deviation of ~0.02 and a fraction of catastrophic outliers (| z | 〉 0.15) of ~0.4 per cent.
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  • 10
    Publication Date: 2020-09-30
    Description: The YMCA (Yes, Magellanic Clouds Again) and STEP (The SMC in Time: Evolution of a Prototype interacting late-type dwarf galaxy) projects are deep g, i photometric surveys carried out with the VLT Survey Telescope (VST) and devoted to study the outskirts of the Magellanic System. A main goal of YMCA and STEP is to identify candidate stellar clusters and complete their census out to the outermost regions of the Magellanic Clouds. We adopted a specific overdensity search technique coupled with a visual inspection of the colour–magnitude diagrams to select the best candidates and estimate their ages. To date, we analysed a region of 23 square degrees in the outskirts of the Large Magellanic Cloud, detecting 85 candidate cluster candidates, 16 of which have estimated ages falling in the so-called age gap. We use these objects together with literature data to gain insight into the formation and interaction history of the Magellanic Clouds.
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    Topics: Physics
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