arXiv subjects

M. Brescia

Publications and source records attributed to M. Brescia.

340 records · Page 19Linked to original sources

arXiv2012

Inside catalogs: a comparison of source extraction software

The scope of this paper is to compare the catalog extraction performances obtained using the new combination of SExtractor with PSFEx, against the more traditional and diffuse application of DAOPHOT with ALLSTAR; therefore, the paper may provide a guide for the selection of the most suitable catalog extraction software. Both software packages were tested on two kinds of simulated images having, respectively, a uniform spatial distribution of sources and an overdensity in the center. In both cases, SExtractor is able to generate a deeper catalog than DAOPHOT. Moreover, the use of neural networks for object classification plus the novel SPREAD\_MODEL parameter push down to the limiting magnitude the possibility of star/galaxy separation. DAOPHOT and ALLSTAR provide an optimal solution for point-source photometry in stellar fields and very accurate and reliable PSF photometry, with robust star-galaxy separation. However, they are not useful for galaxy characterization, and do not generate catalogs that are very complete for faint sources. On the other hand, SExtractor, along with the new capability to derive PSF photometry, turns to be competitive and returns accurate photometry also for galaxies. We can assess that the new version of SExtractor, used in conjunction with PSFEx, represents a very powerful software package for source extraction with performances comparable to those of DAOPHOT. Finally, by comparing the results obtained in the case of a uniform and of an overdense spatial distribution of stars, we notice, for both software packages, a decline for the latter case in the quality of the results produced in terms of magnitudes and centroids.

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arXiv2011

The detection of globular clusters in galaxies as a data mining problem

We present an application of self-adaptive supervised learning classifiers derived from the Machine Learning paradigm, to the identification of candidate Globular Clusters in deep, wide-field, single band HST images. Several methods provided by the DAME (Data Mining & Exploration) web application, were tested and compared on the NGC1399 HST data described in Paolillo 2011. The best results were obtained using a Multi Layer Perceptron with Quasi Newton learning rule which achieved a classification accuracy of 98.3%, with a completeness of 97.8% and 1.6% of contamination. An extensive set of experiments revealed that the use of accurate structural parameters (effective radius, central surface brightness) does improve the final result, but only by 5%. It is also shown that the method is capable to retrieve also extreme sources (for instance, very extended objects) which are missed by more traditional approaches.

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arXiv2011

The DAME/VO-Neural Infrastructure: an Integrated Data Mining System Support for the Science Community

Astronomical data are gathered through a very large number of heterogeneous techniques and stored in very diversified and often incompatible data repositories. Moreover in the e-science environment, it is needed to integrate services across distributed, heterogeneous, dynamic "virtual organizations" formed by different resources within a single enterprise and/or external resource sharing and service provider relationships. The DAME/VONeural project, run jointly by the University Federico II, INAF (National Institute of Astrophysics) Astronomical Observatories of Napoli and the California Institute of Technology, aims at creating a single, sustainable, distributed e-infrastructure for data mining and exploration in massive data sets, to be offered to the astronomical (but not only) community as a web application. The framework makes use of distributed computing environments (e.g. S.Co.P.E.) and matches the international IVOA standards and requirements. The integration process is technically challenging due to the need of achieving a specific quality of service when running on top of different native platforms. In these terms, the result of the DAME/VO-Neural project effort will be a service-oriented architecture, obtained by using appropriate standards and incorporating Grid paradigms and restful Web services frameworks where needed, that will have as main target the integration of interdisciplinary distributed systems within and across organizational domains.

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arXiv2011

DAME: A Distributed Data Mining & Exploration Framework within the Virtual Observatory

Nowadays, many scientific areas share the same broad requirements of being able to deal with massive and distributed datasets while, when possible, being integrated with services and applications. In order to solve the growing gap between the incremental generation of data and our understanding of it, it is required to know how to access, retrieve, analyze, mine and integrate data from disparate sources. One of the fundamental aspects of any new generation of data mining software tool or package which really wants to become a service for the community is the possibility to use it within complex workflows which each user can fine tune in order to match the specific demands of his scientific goal. These workflows need often to access different resources (data, providers, computing facilities and packages) and require a strict interoperability on (at least) the client side. The project DAME (DAta Mining & Exploration) arises from these requirements by providing a distributed WEB-based data mining infrastructure specialized on Massive Data Sets exploration with Soft Computing methods. Originally designed to deal with astrophysical use cases, where first scientific application examples have demonstrated its effectiveness, the DAME Suite results as a multi-disciplinary platform-independent tool perfectly compliant with modern KDD (Knowledge Discovery in Databases) requirements and Information & Communication Technology trends.

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arXiv2010

Mining Knowledge in Astrophysical Massive Data Sets

Modern scientific data mainly consist of huge datasets gathered by a very large number of techniques and stored in very diversified and often incompatible data repositories. More in general, in the e-science environment, it is considered as a critical and urgent requirement to integrate services across distributed, heterogeneous, dynamic "virtual organizations" formed by different resources within a single enterprise. In the last decade, Astronomy has become an immensely data rich field due to the evolution of detectors (plates to digital to mosaics), telescopes and space instruments. The Virtual Observatory approach consists into the federation under common standards of all astronomical archives available worldwide, as well as data analysis, data mining and data exploration applications. The main drive behind such effort being that once the infrastructure will be completed, it will allow a new type of multi-wavelength, multi-epoch science which can only be barely imagined. Data Mining, or Knowledge Discovery in Databases, while being the main methodology to extract the scientific information contained in such MDS (Massive Data Sets), poses crucial problems since it has to orchestrate complex problems posed by transparent access to different computing environments, scalability of algorithms, reusability of resources, etc. In the present paper we summarize the present status of the MDS in the Virtual Observatory and what is currently done and planned to bring advanced Data Mining methodologies in the case of the DAME (DAta Mining & Exploration) project.

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arXiv2008

Astrophysics in S.Co.P.E

S.Co.P.E. is one of the four projects funded by the Italian Government in order to provide Southern Italy with a distributed computing infrastructure for fundamental science. Beside being aimed at building the infrastructure, S.Co.P.E. is also actively pursuing research in several areas among which astrophysics and observational cosmology. We shortly summarize the most significant results obtained in the first two years of the project and related to the development of middleware and Data Mining tools for the Virtual Observatory.

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arXiv2008

GRID-Launcher v.1.0

GRID-launcher-1.0 was built within the VO-Tech framework, as a software interface between the UK-ASTROGRID and a generic GRID infrastructures in order to allow any ASTROGRID user to launch on the GRID computing intensive tasks from the ASTROGRID Workbench or Desktop. Even though of general application, so far the Grid-Launcher has been tested on a few selected softwares (VONeural-MLP, VONeural-SVM, Sextractor and SWARP) and on the SCOPE-GRID.

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arXiv2008

The VO-Neural project: recent developments and some applications

VO-Neural is the natural evolution of the Astroneural project which was started in 1994 with the aim to implement a suite of neural tools for data mining in astronomical massive data sets. At a difference with its ancestor, which was implemented under Matlab, VO-Neural is written in C++, object oriented, and it is specifically tailored to work in distributed computing architectures. We discuss the current status of implementation of VO-Neural, present an application to the classification of Active Galactic Nuclei, and outline the ongoing work to improve the functionalities of the package.

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arXiv2007

Steps towards a map of the nearby universe

We present a new analysis of the Sloan Digital Sky Survey data aimed at producing a detailed map of the nearby (z < 0.5) universe. Using neural networks trained on the available spectroscopic base of knowledge we derived distance estimates for about 30 million galaxies distributed over ca. 8,000 sq. deg. We also used unsupervised clustering tools developed in the framework of the VO-Tech project, to investigate the possibility to understand the nature of each object present in the field and, in particular, to produce a list of candidate AGNs and QSOs.

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arXiv2007

Statistical analysis of the trigger algorithm for the NEMO project

We discuss the performances of a trigger implemented for the planned neutrino telescope NEMO. This trigger seems capable to discriminate between the signal and the strong background introduced by atmospheric muons and by the beta decay of the K-40 nuclei present in the water. The performances of the trigger, as evaluated on simulated data are analyzed in detail.

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arXiv2007

Implementation of the trigger algorithm for the NEMO project

We describe the implementation of trigger algorithm specifically tailored on the characteristics of the neutrino telescope NEMO. Extensive testing against realistic simulations shows that, by making use of the uncorrelated nature of the noise produced mainly by the decay of K-40 beta-decay, this trigger is capable to discriminate among different types of muonic events.

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arXiv2007

The use of neural networks to probe the structure of the nearby universe

In the framework of the European VO-Tech project, we are implementing new machine learning methods specifically tailored to match the needs of astronomical data mining. In this paper, we shortly present the methods and discuss an application to the Sloan Digital Sky Survey public data set. In particular, we discuss some preliminary results on the 3-D taxonomy of the nearby (z < 0.5) universe. Using neural networks trained on the available spectroscopic base of knowledge we derived distance estimates for ca. 30 million galaxies distributed over 8,000 sq. deg. We also use unsupervised clustering tools to investigate whether it is possible to characterize in broad morphological bins the nature of each object and produce a reliable list of candidate AGNs and QSOs.

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arXiv2005

VST - VLT Survey Telescope Integration Status

The VLT Survey Telescope (VST) is a 2.6m aperture, wide field, UV to I facility, to be installed at the European Southern Observatory (ESO) on the Cerro Paranal Chile. VST was primarily intended to complement the observing capabilities of VLT with wide-angle imaging for detecting and pre-characterising sources for further observations with the VLT.

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arXiv2001

The VST telescope control software in the ESO VLT environment

The VST (VLT Survey Telescope) is a 2.6 m Alt-Az telescope to be installed at Mount Paranal in Chile, in the European Southern Observatory (ESO) site. The VST is a wide-field imaging facility planned to supply databases for the ESO Very Large Telescope (VLT) science and carry out stand-alone observations in the UV to I spectral range. This paper will focus mainly on control software aspects, describing the VST software architecture in the context of the whole ESO VLT control concept. The general architecture and the main components of the control software will be described.

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arXiv2001

Active optics control of the VST telescope with the CAN field-bus

The VST (VLT Survey Telescope) is a 2.6 m class Alt-Az telescope to be installed at Mount Paranal in the Atacama desert, Chile, in the European Southern Observatory (ESO) site. The VST is a wide-field imaging facility planned to supply databases for the ESO Very Large Telescope (VLT) science and carry out stand-alone observations in the UV to I spectral range. This paper will focus on the distributed control system of active optics based on CAN bus and PIC microcontrollers. Both axial and radial pads of the primary mirror will be equipped by astatic lever supports controlled by microcontroller units. The same CAN bus + microcontroller boards approach will be used for the temperature acquisition modules.

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arXiv2001

Integration of the VIMOS control system

The VIRMOS consortium of French and Italian Institutes (PI: O. Le Fevre, co-PI: G. Vettolani) is manufacturing two wide field imaging multi-object spectrographs for the European Southern Observatory Very Large Telescope (VLT), with emphasis on the ability to carry over spectroscopic surveys of large numbers of sources: the VIsible Multi-Object Spectrograph, VIMOS, and the Near InfraRed Multi-Object Spectrograph, NIRMOS. There are 52 motors to be controlled in parallel in the spectrograph, making VIMOS a complex machine to be handled. This paper will focus on the description of the control system, designed in the ESO VLT standard control concepts, and on some integration issues and problem solving strategies.

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