Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Boinee, P."'
Autor:
Frailis, Marco, Mansutti, Oriana, Boinee, Praveen, Cabras, Giuseppe, De Angelis, Alessandro, De Lotto, Barbara, Forti, Alberto, Dell'Orso, Mauro, Paoletti, Riccardo, Scribano, Angelo, Turini, Nicola, Mariotti, Mose', Peruzzo, Luigi, Saggion, Antonio
We studied the application of the Pseudo-Zernike features as image parameters (instead of the Hillas parameters) for the discrimination between the images produced by atmospheric electromagnetic showers caused by gamma-rays and the ones produced by a
Externí odkaz:
http://arxiv.org/abs/cs/0602083
Neural networks have proved to be versatile and robust for particle separation in many experiments related to particle astrophysics. We apply these techniques to separate gamma rays from hadrons for the MAGIC Cerenkov Telescope. Two types of neural n
Externí odkaz:
http://arxiv.org/abs/astro-ph/0503539
Autor:
Bastieri, D., Bavikadi, R., Bigongiari, C., Bisesi, E., Boinee, P., De Angelis, A., De Lotto, B., Forti, A., Lenisa, T., Longo, F., Mansutti, O., Mariotti, M., Moralejo, A., Pascoli, D., Peruzzo, L., Saggion, A., Sartori, P., Scalzotto, V., collaboration, The MAGIC
With its diameter of 17m, the MAGIC telescope is the largest Cherenkov detector for gamma ray astrophysics. It is sensitive to photons above an energy of 30 GeV. MAGIC started operations in October 2003 and is currently taking data. This report summa
Externí odkaz:
http://arxiv.org/abs/astro-ph/0503534
Autor:
Longo, F., Azzi, P., Bastieri, D., Busetto, G., Lei, Y., Rando, R., Tibolla, O., Baldini, L., Kuss, M., Latronico, L., Omodei, N., Razzano, M., Spandre, G., Boinee, P., De Angelis, A., Frailis, M., Brigida, M., Gargano, F., Giglietto, N., Loparco, F., Mazziotta, M. N., Cecchi, C., Lubrano, P., Marcucci, F., Pepe, M., Tosti, G., Lionetto, A., Morselli, A., Pittori, C.
This paper presents the simulation of the GLAST high energy gamma-ray telescope. The simulation package, written in C++, is based on the Geant4 toolkit, and it is integrated into a general framework used to process events. A detailed simulation of th
Externí odkaz:
http://arxiv.org/abs/astro-ph/0503545
Multi-dimensional data classification is an important and challenging problem in many astro-particle experiments. Neural networks have proved to be versatile and robust in multi-dimensional data classification. In this article we shall study the clas
Externí odkaz:
http://arxiv.org/abs/cs/0412023
Physics analysis in astroparticle experiments requires the capability of recognizing new phenomena; in order to establish what is new, it is important to develop tools for automatic classification, able to compare the final result with data from diff
Externí odkaz:
http://arxiv.org/abs/cs/0402014
Autor:
Boinee, P., Cabras, G., De Angelis, A., Favretto, D., Frailis, M., Giannitrapani, R., Milotti, E., Longo, F., Brigida, M., Gargano, F., Giglietto, N., Loparco, F., Mazziotta, M. N., Cecchi, C., Lubrano, P., Pepe, M., Baldini, L., Cohen-Tanugi, J., Kuss, M., Latronico, L., Omodei, N., Spandre, G., Bogart, J., Dubois, R., Kamae, T., Rochester, L., Usher, T., Burnett, T., Robinson, S., Bastieri, D., Rando, R.
Publikováno v:
S. Ciprini, A. De Angelis, P. Lubrano and O. Mansutti (eds.): Proc. of ``Science with the New Generation of High Energy Gamma-ray Experiments'' (Perugia, Italy, May 2003). Forum, Udine 2003, p. 141
This paper presents the simulation of the GLAST high energy gamma-ray telescope. The simulation package, written in C++, is based on the Geant4 toolkit, and it is integrated into a general framework used to process events. A detailed simulation of th
Externí odkaz:
http://arxiv.org/abs/astro-ph/0308120
Publikováno v:
S. Ciprini, A. De Angelis, P. Lubrano and O. Mansutti (eds.): Proc. of ``Science with the New Generation of High Energy Gamma-ray Experiments'' (Perugia, Italy, May 2003). Forum, Udine 2003, p. 177
Self-Organising Maps (SOMs) are effective tools in classification problems, and in recent years the even more powerful Dynamic Growing Neural Networks, a variant of SOMs, have been developed. Automatic Classification (also called clustering) is an im
Externí odkaz:
http://arxiv.org/abs/cs/0307031
Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and shown improvements in classification results for several benchmarking data sets with mainly decision trees as their base classifiers. In this paper we experime
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::45429c1a97371051c703e4f020c18f75
Autor:
Frailis, M., Mansutti, O., Boinee, P., Cabras, G., De Angelis, A., De Lotto, B., Forti, A., Dell'Orso, M., Paoletti, R., Scribano, A., Turini, N., Mariotti, M., Peruzzo, L., Saggion
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3659::7feb6f4250fb3622ef683ddda6c5c54b
http://hdl.handle.net/11390/855908
http://hdl.handle.net/11390/855908