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Francesco Longo

Researcher at University of Trieste

Publications -  786
Citations -  100221

Francesco Longo is an academic researcher from University of Trieste. The author has contributed to research in topics: Fermi Gamma-ray Space Telescope & Blazar. The author has an hindex of 142, co-authored 745 publications receiving 89859 citations. Previous affiliations of Francesco Longo include INAF & Max Planck Society.

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Geant4—a simulation toolkit

S. Agostinelli, +126 more
TL;DR: The Gelfant 4 toolkit as discussed by the authors is a toolkit for simulating the passage of particles through matter, including a complete range of functionality including tracking, geometry, physics models and hits.
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Geant4 developments and applications

TL;DR: GeGeant4 as mentioned in this paper is a software toolkit for the simulation of the passage of particles through matter, it is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection.
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The Large Area Telescope on the Fermi Gamma-ray Space Telescope Mission

W. B. Atwood, +292 more
TL;DR: The Large Area Telescope (Fermi/LAT) as mentioned in this paper is the primary instrument on the Fermi Gamma-ray Space Telescope, which is an imaging, wide field-of-view, high-energy gamma-ray telescope, covering the energy range from below 20 MeV to more than 300 GeV.
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Recent developments in GEANT4

John Allison, +102 more
TL;DR: Geant4 as discussed by the authors is a software toolkit for the simulation of the passage of particles through matter, which is used by a large number of experiments and projects in a variety of application domains, including high energy physics, astrophysics and space science, medical physics and radiation protection.
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Fermi Large Area Telescope Second Source Catalog

P. L. Nolan, +293 more
TL;DR: The second Fermi-LAT catalog (2FGL) as mentioned in this paper includes source location regions, defined in terms of elliptical fits to the 95% confidence regions and spectral fits in terms either power-law, exponentially cutoff power law, or log-normal forms.