323,904 research outputs found
Rigorous 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
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 textConstraining dark energy models using the lookback time to galaxy clusters and the age of the universe
No full textThe
No full textThe surface brightness distribution (SBD) function describes the
number density of galaxies as measured against their central
surface brightness. Because detecting galaxies with low central surface
brightnesses is both time-consuming and complicated, determining the
shape of this distribution function can be difficult.
In a recent paper Cross et al. suggested
a bell-shaped SBD disk-galaxy function which peaks near the canonical Freeman
value of 21.7 and then falls off significantly by 23.5 B mag arcsec-2.
This is in contradiction to previous studies which have typically
found flat (slope = 0) SBD functions out to 24–25 B mag arcsec-2
(the survey limits). Here we take advantage of a
recent surface-brightness limited survey by Andreon & Cuillandre which
reaches considerably fainter magnitudes than the Cross et al. sample
(MB reaches fainter than -12 for Andreon & Cuillandre while
the Cross et al. sample is limited to 16)
to re-evaluate both the SBD function as found by their data and the SBD
for a wide variety of galaxy surveys, including the Cross et al. data.
The result is a SBD function with a flat slope out through
the survey limits of 24.5 B mag arcsec-2, with high confidence limits
A Serverless Architecture for Efficient and Scalable Monte Carlo Markov Chain Computation
No full textComputer 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
Estimating the heterogeneity of pressure profiles within a complete sample of 55 galaxy clusters: A Bayesian hierarchical model
No full textGalaxy clusters exhibit heterogeneity in their pressure profiles, even after rescaling, highlighting the need for adequately sized samples to accurately capture variations across the cluster population. We present a Bayesian hierarchical model that simultaneously fits individual cluster parameters and the underlying population distribution, providing estimates of the population-averaged pressure profile and the intrinsic scatter, as well as accurate pressure estimates for individual objects. We introduce a highly flexible, low-covariance, and interpretable parametrization of the pressure profile based on restricted cubic splines. We model the scatter properly accounting for outliers, and we incorporate corrections for beam and transfer function, as is required for Sunyaev-Zel’dovich (SZ) data. Our model is applied to the largest non-stacked sample of individual cluster radial profiles, extracted from SPT+Planck Compton-y maps. This is a complete sample of 55 clusters, with 0.05 4 × 1014 M⊙, enabling subdivision into sizable morphological classes based on eROSITA data. Fitting is computationally feasible within a few days on a modern (2024) personal computer. The shape of the population-averaged pressure profile, at our 250 kpc full width at half maximum resolution, closely resembles the universal pressure profile, despite the flexibility of our model for accommodating alternative shapes, with a ∼12% lower normalization, similar to what is needed to alleviate the tension between cosmological parameters derived from the cosmic microwave background and Planck SZ cluster counts. Beyond r500, our pressure profile is steeper than previous determinations. The intrinsic scatter is consistent with or lower than previous estimates, despite the broader diversity expected from our SZ selection. Our flexible pressure modelization identifies a few clusters with nonstandard concavity in their radial profiles but no outliers in amplitude. When dividing the sample by morphology, we find remarkably similar pressure profiles across classes, though regular clusters show evidence of lower scatter and a more centrally peaked profile compared to disturbed ones
Constraining 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
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