Context. VISTA Variables in the Via Lactea (VVV) is one of six ESO Public Surveys using the 4 meter Visible and Infrared Survey Telescope for Astronomy (VISTA). The VVV survey covers the Milky Way bulge and an adjacent section of the disk, and one of the principal objectives is to search for new star clusters within previously unreachable obscured parts of the Galaxy.Aims. The primary motivation behind this work is to discover and analyze obscured star clusters in the direction of the inner Galactic disk and bulge.Methods. Regions of the inner disk and bulge covered by the VVV survey were visually inspected using composite JHKS color images to select new cluster candidates on the basis of apparent overdensities. DR1, DR2, CASU, and point spread function photometry of 10 x 10 arcmin fields centered on each candidate cluster were used to construct color-magnitude and color-color diagrams. Follow-up spectroscopy of the brightest members of several cluster candidates was obtained in order to clarify their nature.Results. We report the discovery of 58 new infrared cluster candidates. Fundamental parameters such as age, distance, and metallicity were determined for 20 of the most populous clusters.

A growing fraction of simple stellar population models, in an aim to create more realistic simulations capable of including stochastic variation in their outputs, begin their simulations with a distribution of discrete stars following a power-law function of masses. Careful attention is needed to create a correctly sampled initial mass function (IMF), and here we provide a solid mathematical method, called MASSCLEAN IMF Sampling, for doing so. We use our method to perform 10 million MASSCLEAN Monte Carlo stellar cluster simulations to determine the most massive star in a mass distribution as a function of the total mass of the cluster. We find that a maximum mass range is predicted, not a single maximum mass. This range is (1) dependent on the total mass of the cluster and (2) independent of an upper stellar mass limit, Mlimit, for unsaturated clusters and emerges naturally from our IMF sampling method. We then turn our analysis around, starting with our new database of 25 million simulated clusters, to constrain the highest mass star from the observed integrated colors of a sample of 40 low-mass Large Magellanic Cloud stellar clusters of known age and mass. Finally, we present an analytical description of the maximum mass range of the most massive star as a function of the cluster's total mass and present a new Mmax–Mcluster relation.

223rd AAS Meeting, 5-9 January 2014 at the Gaylord National Resort and Convention Center, 201 Waterfront Street, National Harvor, Maryland 20745, USA.

Reddening-free indices represent a powerful, and yet little used, approach to problems where reddening/extinction constitute a serious issue, without having to deal with the extinction of any individual star. Here we report on our progress in obtaining reddening free indices, in the VISTA filter system.

One of the methods most commonly used for learning and classification is using decision trees. The greatest advantages that decision trees offer is that, unlike classical trees, they provide a support for handling uncertain data sets. The paper introduces a new algorithm for building fuzzy decision trees and also offers some comparative results, by taking into account other methods. We will present a general overview of the fuzzy decision trees and focus afterwards on the newly introduced algorithm, pointing out that it can be a very useful tool in processing fuzzy data sets by offering good comparative results.

The current paper presents an enhanced partitioning mechanism for numerical data. The efficiency of our method will be illustrated through a solid set of tests that have been performed. We have planned this partitioning phase as an initial step in a more complex algorithm to be further studied and implemented. The final goal is to use it for future decision making in automatic image annotation. Fuzzy Sets theory has been used as a base for our clustering algorithm and partitioning. We included this mechanism as a component of a framework we developed for image processing, more exactly for the image segmentation evaluation model we are building.

Context. The ESO public survey VISTA variables in the Via Lactea (VVV) started in 2010. VVV targets 562 sq. deg in the Galactic bulge and an adjacent plane region and is expected to run for about five years.Aims. We describe the progress of the survey observations in the first observing season, the observing strategy, and quality of the data obtained.Methods. The observations are carried out on the 4-m VISTA telescope in the ZYJHK(s) filters. In addition to the multi-band imaging the variability monitoring campaign in the K-s filter has started. Data reduction is carried out using the pipeline at the Cambridge Astronomical Survey Unit. The photometric and astrometric calibration is performed via the numerous 2MASS sources observed in each pointing.Results. The first data release contains the aperture photometry and astrometric catalogues for 348 individual pointings in the ZYJHK(s) filters taken in the 2010 observing season. The typical image quality is similar to 0 ''.9-1 ''.0. The stringent photometric and image quality requirements of the survey are satisfied in 100% of the JHK(s) images in the disk area and 90% of the JHK(s) images in the bulge area. The completeness in the Z and Y images is 84% in the disk, and 40% in the bulge. The first season catalogues contain 1.28 x 10(8) stellar sources in the bulge and 1.68 x 10(8) in the disk area detected in at least one of the photometric bands. The combined, multi-band catalogues contain more than 1.63 x 10(8) stellar sources. About 10% of these are double detections because of overlapping adjacent pointings. These overlapping multiple detections are used to characterise the quality of the data. The images in the JHK(s) bands extend typically similar to 4 mag deeper than 2MASS. The magnitude limit and photometric quality depend strongly on crowding in the inner Galactic regions. The astrometry for K-s = 15-18 mag has rms similar to 35-175 mas.Conclusions. The VVV Survey data products offer a unique dataset to map the stellar populations in the Galactic bulge and the adjacent plane and provide an exciting new tool for the study of the structure, content, and star-formation history of our Galaxy, as well as for investigations of the newly discovered star clusters, star-forming regions in the disk, high proper motion stars, asteroids, planetary nebulae, and other interesting objects.

We present new age and mass estimates for 920 stellar clusters in the Large Magellanic Cloud (LMC) based on previously published broadband photometry and the stellar cluster analysis package, MASSCLEANage. Expressed in the generic fitting formula, d2N/dMdt∝Mαtβ, the distribution of observed clusters is described by α = −1.5 to −1.6 and β = −2.1 to −2.2. For 288 of these clusters, ages have recently been determined based on stellar photometric color–magnitude diagrams, allowing us to gauge the confidence of our ages. The results look very promising, opening up the possibility that this sample of 920 clusters, with reliable and consistent age, mass, and photometric measures, might be used to constrain important characteristics about the stellar cluster population in the LMC. We also investigate a traditional age determination method that uses a χ2 minimization routine to fit observed cluster colors to standard infinite-mass limit simple stellar population models. This reveals serious defects in the derived cluster age distribution using this method. The traditional χ2 minimization method, due to the variation of U, B, V, R colors, will always produce an overdensity of younger and older clusters, with an underdensity of clusters in the log (age/yr) = [7.0, 7.5] range. Finally, we present a unique simulation aimed at illustrating and constraining the fading limit in observed cluster distributions that includes the complex effects of stochastic variations in the observed properties of stellar clusters.

The present paper addresses the nowadays field of image segmentation by offering an evaluation of several existing approaches. The paper offers a comparison of the experimental results from the error measurement point of view. We introduce a new method of salient object recognition that takes into consideration color and geometric features in order to offer a conclusive result. Our new segmentation method introduced in the paper has revealed very good results in terms of comparison with the already known object detection methods. We use a set of error measures to analyze the consistency of different segmentations provided by several well known algorithms. The experimental results offer a complete basis for parallel analysis with respect to the precision of our algorithm, rather than the individual efficiency.

The present paper addresses the problem of image segmentation evaluation by comparing seven different approaches. We are presenting a new method of salient object detection with very good results relative to other already known object detection methods. We developed a simple evaluation framework in order to compare the results of our method with other segmentation methods. The results of our experimental work offer good perspectives for our algorithm, in terms of efficiency and precision.

In the present paper we are introducing a new method of salient object detection with very good results relative to other already known segmentation methods. We address through our research the problem of image segmentation evaluation by an efficient comparison of four complex algorithms. In order to compare our method with other approaches, we built an evaluation framework that helped us with our experiments. The experimental results offer a complete basis for parallel analysis with respect to the precision of our algorithm, rather than the individual efficiency.

The present paper is aimed to compare the efficiency of a new segmentation method with several existing approaches. The paper addresses the problem of image segmentation evaluation from the error measurement point of view.We are introducing a new method of salient object recognition with very good results relative to other already known object detection methods. We developed a simple evaluation framework in order to compare the results of our method with other segmentation methods. The experimental results offer a complete basis for parallel analysis with respect to the precision of our algorithm, rather than the individual efficiency.