Massimo Brescia

Publications (49) View all

• Article: Inside catalogs: a comparison of source extraction software

  M. Annunziatella, A. Mercurio, M. Brescia, S. Cavuoti, G. Longo
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  ABSTRACT: 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.
  12/2012;
• Chapter: New Trends in E-Science: Machine Learning and Knowledge Discovery in Databases

  M. Brescia
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  ABSTRACT: Data mining, or Knowledge Discovery in Databases (KDD), while being the main methodology to extract the scientific information contained in Massive Data Sets (MDS), needs to tackle crucial problems since it has to orchestrate complex challenges posed by transparent access to different computing environments, scalability of algorithms, reusability of resources. To achieve a leap forward for the progress of e-science in the data avalanche era, the community needs to implement an infrastructure capable of performing data access, processing and mining in a distributed but integrated context. The increasing complexity of modern technologies carried out a huge production of data, whose related warehouse management and the need to optimize analysis and mining procedures lead to a change in concept on modern science. Classical data exploration, based on local user own data storage and limited computing infrastructures, is no more efficient in the case of MDS, worldwide spread over inhomogeneous data centres and requiring teraflop processing power. In this context modern experimental and observational science requires a good understanding of computer science, network infrastructures, Data Mining, etc. i.e. of all those techniques which fall into the domain of the so called e-science (recently assessed also by the Fourth Paradigm of Science). Such understanding is almost completely absent in the older generations of scientists and this reflects in the inadequacy of most academic and research programs. A paradigm shift is needed: statistical pattern recognition, object oriented programming, distributed computing, parallel programming need to become an essential part of scientific background. A possible practical solution is to provide the research community with easy-to understand, easy-to-use tools, based on the Web 2.0 technologies and Machine Learning methodology. Tools where almost all the complexity is hidden to the final user, but which are still flexible and able to produce efficient and reliable scientific results. All these considerations will be described in the detail in the chapter. Moreover, examples of modern applications offering to a wide variety of e-science communities a large spectrum of computational facilities to exploit the wealth of available massive data sets and powerful machine learning and statistical algorithms will be also introduced
  07/2012: pages 74; , ISBN: 978-1-61942-774-7
• Source

  Available from: Massimo Brescia

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

  M. Brescia, A. Corazza, S. Cavuoti, G. d'Angelo, R. D'Abrusco, C. Donalek, S. G. Djorgovski, N. Deniskina, M. Fiore, M. Garofalo, O. Laurino, G. Longo A. Mahabal, F. Manna, A. Nocella, B. Skordovski
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  ABSTRACT: 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.
  12/2011;
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  Available from: Massimo Brescia

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

  M. Brescia, S. Cavuoti, M. Paolillo, G. Longo, T. Puzia
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  ABSTRACT: 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.
  10/2011;
• Article: Recent achievements of the NEMO project

  E. Migneco, S. Aiello, A. Aloisio, F. Ameli, I. Amore, M. Anghinolfi, A. Anzalone, G. Barbarino, E. Barbarito, M. Battaglieri, [......], F. Speziale, M. Spurio, M. Taiuti, G. Terreni, L. Trasatti, S. Urso, V. Valente, M. Vecchi, P. Vicini, R. Wischnewski
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  ABSTRACT: The status of the activities towards the realization of a km3 Cherenkov neutrino detector carried out by the NEMO Collaboration is described. The realization of a Phase-1 project, which is under way, will validate the proposed technologies for the realization of the km3 detector on a Test Site at 2000 m depth. The realization of a new infrastructure on the candidate site (Phase-2 project) will provide the possibility to test detector components at 3500 m depth.
  Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment 01/2008; · 1.21 Impact Factor