F
Fabrizio Gentile
Researcher at University of Bologna
Publications - 10
Citations - 35
Fabrizio Gentile is an academic researcher from University of Bologna. The author has contributed to research in topics: Computer science & Galaxy. The author has an hindex of 2, co-authored 3 publications receiving 20 citations. Previous affiliations of Fabrizio Gentile include INAF.
Papers
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Journal ArticleDOI
The Fornax Deep Survey with VST. IX. The catalog of sources in the FDS area, with an example study for globular clusters and background galaxies
Michele Cantiello,Aku Venhola,Aniello Grado,Maurizio Paolillo,Raffaele D'Abrusco,Gabriella Raimondo,Massimo Quintini,Michael Hilker,Steffen Mieske,Crescenzo Tortora,Marilena Spavone,Massimo Capaccioli,Enrica Iodice,Reynier Peletier,Jesus Falcon Barroso,Luca Limatola,Nicola R. Napolitano,Pietro Schipani,Glenn van de Ven,Fabrizio Gentile,Giovanni Covone +20 more
TL;DR: In this paper, the authors presented the catalogs of compact stellar systems in the Fornax cluster as well as extended background sources and point-like sources and derived ugri photometry of ~1.7 million sources over the $\sim$21 sq. degree area of FDS centered on NGC1399.
Journal ArticleDOI
The Fornax Deep Survey with VST. IX. Catalog of sources in the FDS area with an example study for globular clusters and background galaxies
Michele Cantiello,Aku Venhola,Aniello Grado,Maurizio Paolillo,Raffaele D'Abrusco,Gabriella Raimondo,Massimo Quintini,Michael Hilker,Steffen Mieske,Crescenzo Tortora,Marilena Spavone,Massimo Capaccioli,Enrica Iodice,Enrica Iodice,Reynier Peletier,Jesus Falcon Barroso,Luca Limatola,Nicola R. Napolitano,Nicola R. Napolitano,Pietro Schipani,Glenn van de Ven,Fabrizio Gentile,Giovanni Covone +22 more
TL;DR: In this article, the authors presented the catalogs of compact stellar systems in the Fornax cluster, as well as extended background sources and point-like sources and derived ugri photometry of ∼1.7 million sources over the ∼21 square degree area of FDS centered on the bright central galaxy NGC 1399.
Peer Review
COSMOS-Web: An Overview of the JWST Cosmic Origins Survey
Caitlin M. Casey,Jeyhan S. Kartaltepe,Nicole E. Drakos,M. Franco,O. Ilbert,Caitlin Rose,Isabella G. Cox,J. Nightingale,Brant Robertson,John D. Silverman,Anton M. Koekemoer,Richard Massey,H. J. McCracken,Jason Rhodes,Hollis B. Akins,Aristeidis Amvrosiadis,Rafael C. Arango-Toro,Micaela Bagley,Peter Capak,Jaclyn B. Champagne,Nima Chartab,Oscar Ortiz,Kevin C. Cooke,Olivia Cooper,Behnam Darvish,Xu Ding,Andreas L. Faisst,Steven L. Finkelstein,Seiji Fujimoto,Fabrizio Gentile,Steven Gillman,K. Gould,Ghassem Gozaliasl,Santosh Harish,Christopher C. Hayward,Qiuhan He,Shoubaneh Hemmati,Michaela Hirschmann,Shuowen Jin,Ali Ahmad Khostovan,Vasily Kokorev,Erini Lambrides,Clotilde Laigle,Gene C. K. Leung,Daizhong Liu,T. Liaudat,Arianna S. Long,Georgios E. Magdis,Guillaume Mahler,Vincenzo Mainieri,Sinclaire M. Manning,Claudia Maraston,Crystal L. Martin,Jacqueline McCleary,Jed McKinney,Conor McPartland,Bahram Mobasher,Rohan Pattnaik,Alvio Renzini,R. Michael Rich,David B. Sanders,Zahra Sattari,Diana Scognamiglio,Nick Scoville,Kartik Sheth,Marko Shuntov,Martin Sparre,Tomoko L. Suzuki,Margherita Talia,Sune Toft,Benny Trakhtenbrot,C. Megan Urry,Francesco Valentino,Brittany N. Vanderhoof,Eleni Vardoulaki,John R. Weaver,Katherine E. Whitaker,Stephen M. Wilkins,Lilan Yang,Jorge A. Zavala +79 more
TL;DR: COSMOS-Web as discussed by the authors is a NIRCam imaging survey in four filters (F115W, F150w, F277W, and F444W) that will reach 5$\sigma$ point source depths ranging from 27.5-28.2 magnitudes.
Journal ArticleDOI
The small world coefficient 4.8 ± 1 optimizes information processing in 2D neuronal networks
TL;DR: In this paper , the authors provided a quantitative estimate of the efficiency of small-world networks by using a model of the brain in which neurons are described as agents that integrate the signals from other neurons and generate an output that spreads in the system and then used the Shannon Information Entropy to decode those signals and compute the information transported in the grid as a function of its smallworldness.
Journal ArticleDOI
The small world coefficient 4.8 ± 1 optimizes information processing in 2D neuronal networks
TL;DR: In this article , the authors provided a quantitative estimate of the efficiency of small-world networks by using a model of the brain in which neurons are described as agents that integrate the signals from other neurons and generate an output that spreads in the system and then used the Shannon Information Entropy to decode those signals and compute the information transported in the grid as a function of its smallworldness.