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Recently Added Papers
 Unsupervised Classification of Galaxies. I. ICA feature selection

Chattopadhyay, Tanuka; FraixBurnet, Didier; Mondal, Saptarshi
Subjective classification of galaxies can mislead us in the quest of the origin regarding formation and evolution of galaxies since this is necessarily limited to a few features. The human mind is not able to apprehend the complex correlations in a ...  USING XML AND SEMANTIC TECHNOLOGIES IN ASTROINFORMATICS TO MANAGE DATA

Guy Beech
The aim of this doctoral research project is to develop contemporary data management solutions for the management and utilization of astronomy data. One key objective is to define an XMLSchema and ontologies catering for the whole scope of AstroInfo ...  Unveiling the cosmological information beyond linear scales: forecasts for sufficient statistics

M. Wolk, J. Carron and I. Szapudi
Beyond the linear regime, Fourier modes of cosmological random fields become correlated, and the power spectrum of density fluctuations contains only a fraction of the available cosmological information. To unveil this formerly hidden information, t ...  SOMz: photometric redshift PDFs with self organizing maps and random atlas

Carrasco Kind, M.; Brunner, R. J.
In this paper we explore the applicability of the unsupervised machine learning technique of Self Organizing Maps (SOM) to estimate galaxy photometric redshift probability density functions (PDFs). This technique takes a spectroscopic training set, ...  Robust PCA and MIC statistics of baryons in early minihaloes

de Souza, R. S.; Maio, U.; Biffi, V.; Ciardi, B.
We present a novel approach, based on robust principal components analysis (RPCA) and maximal information coefficient (MIC), to study the redshift dependence of halo baryonic properties. Our data is composed by a set of different physical quantities ...  Photometric redshifts for Quasars in multi band Surveys

Brescia, M.; Cavuoti, S.; D'Abrusco, R.; Longo, G.; Mercurio, A.
MLPQNA stands for Multi Layer Perceptron with Quasi Newton Algorithm and it is a machine learning method which can be used to cope with regression and classification problems on complex and massive data sets. In this paper we give the formal descrip ...  Covariance of lucky images for increasing objects contrast: diffractionlimited images in groundbased telescopes

Cagigal, Manuel P.; Valle, Pedro J.; ColodroConde, Carlos; VillóPérez, Isidro; PérezGarrido, Antonio
Images of stars adopt shapes far from the ideal Airy pattern due to atmospheric density fluctuations. Hence, diffractionlimited images can only be achieved by telescopes without atmospheric influence, e.g. spatial telescopes, or by using techniques ...  NonGaussian Error Distributions of LMC Distance Moduli Measurements

Crandall, Sara; Ratra, Bharat
We construct error distributions for a compilation of 232 Large Magellanic Cloud (LMC) distance moduli values from de Grijs et al. that give an LMC distance modulus of (m  M)0 = 18.49 ± 0.13 mag (median and 1σ symmetrized error). Central estimate ...  Mapping stellar content to dark matter haloes using galaxy clustering and galaxygalaxy lensing in the SDSS DR7

Zu, Ying; Mandelbaum, Rachel
The mapping between the distributions of the observed galaxy stellar mass and the underlying dark matter haloes provides the crucial link from theories of largescale structure formation to interpreting the complex phenomena of galaxy formation and ...  On weak lensing shape noise

Niemi, SamiMatias; Kitching, Thomas D.; Cropper, Mark
One of the most powerful techniques to study the dark sector of the Universe is weak gravitational lensing. In practice, to infer the reduced shear, weak lensing measures galaxy shapes, which are the consequence of both the intrinsic ellipticity of ...  A demonstration of position angleonly weak lensing shear estimators on the GREAT3 simulations

Whittaker, Lee; Brown, Michael L.; Battye, Richard A.
We develop and apply the position angleonly shear estimator of Whittaker, Brown & Battye to realistic galaxy images. This is done by demonstrating the method on the simulations of the third GRavitational lEnsing Accuracy Testing (GREAT3) challenge, ...  Spectrophotometric distances to stars: A general purpose Bayesian approach

Santiago, Basílio X.; Brauer, Dorothée E.; Anders, Friedrich; Chiappini, Cristina; Queiroz, Anna B.; Girardi, Léo; RochaPinto, Helio J.; Balbinot, Eduardo; da Costa, Luiz N.; Maia, Marcio A. G.; Schultheis, Mathias; Steinmetz, Matthias; Miglio, Andrea; Montalbán, Josefina; Schneider, Donald P.; Beers, Timothy C.; Frinchaboy, Peter M.; Lee, Young Sun; Zasowski, Gail
Context. Determining distances to individual field stars is a necessary step towards mapping Galactic structure and determining spatial variations in the chemodynamical properties of stellar populations in the Milky Way. Aims: In order to provide ...  The amazing diversity in the hot gas content of an Xray unbiased massive galaxy clusters sample

Andreon, S.; Serra, Ana Laura; Moretti, A.; Trinchieri, G.
We aim to determine the intrinsic variety, at a given mass, of the properties of the intracluster medium in clusters of galaxies. This requires a cluster sample selected independently of the intracluster medium content for which reliable masses and ...  ELM: an Algorithm to Estimate the Alpha Abundance from Lowresolution Spectra

Bu, Yude; Zhao, Gang; Pan, Jingchang; Bharat Kumar, Yerra
We have investigated a novel methodology using the extreme learning machine (ELM) algorithm to determine the α abundance of stars. Applying two methods based on the ELM algorithm—ELM+spectra and ELM+Lick indices—to the stellar spectra from the ...  The MIP ensemble simulation: local ensemble statistics in the Cosmic Web

AragonCalvo, M. A.
We present a new technique that allows us to compute ensemble statistics on a local basis, directly relating halo properties to their local environment. This is achieved by the use of a correlated ensemble in which the largescale structure (LSS) is ...  Efficient, uninformative sampling of limbdarkening coefficients for a threeparameter law

Kipping, David M.
Stellar limb darkening impacts a wide range of astronomical measurements. The accuracy to which it is modelled limits the accuracy in any covariant parameters of interest, such as the radius of a transiting planet. With the ever growing availability ...  Transdimensional Bayesian inference for gravitational lens substructures

Brewer, Brendon J.; Huijser, David; Lewis, Geraint F.
We introduce a Bayesian solution to the problem of inferring the density profile of strong gravitational lenses when the lens galaxy may contain multiple dark or faint substructures. The source and lens models are based on a superposition of an unkn ...  A Bayesian approach to linear regression in astronomy

Sereno, Mauro
Linear regression is common in astronomical analyses. I discuss a Bayesian hierarchical modelling of data with heteroscedastic and possibly correlated measurement errors and intrinsic scatter. The method fully accounts for time evolution. The slope, ...  Bayesian model selection without evidences: application to the dark energy equationofstate

Hee, S.; Handley, W. J.; Hobson, M. P.; Lasenby, A. N.
A method is presented for Bayesian model selection without explicitly computing evidences, by using a combined likelihood and introducing an integer model selection parameter n so that Bayes factors, or more generally posterior odds ratios, may be r ...  Cosmological parameter inference from galaxy clustering: the effect of the posterior distribution of the power spectrum

Kalus, B.; Percival, W. J.; Samushia, L.
We consider the shape of the posterior distribution to be used when fitting cosmological models to power spectra measured from galaxy surveys. At very large scales, Gaussian posterior distributions in the power do not approximate the posterior distr ...