Goffredo Puccetti
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Goffredo Puccetti has a total of 14 co-authors. Here are the top co-authors by number of shared publications:
- Adriano Di Giovanni
Gran Sasso Science Institute (GSSI) Via Iacobucci 2 I-67100 L'Aquila Italy
1 shared publication - Andrea Valerio Macciò
New York University Abu Dhabi
1 shared publication - Andrew M. T. Pollock
Department of Physics and Astronomy, University of Sheffield, Hounsfield Road, Sheffield S3 7RH, UK
1 shared publication - Carlos Brandt
New York University Abu PO Box 129188 Dhabi Abu Dhabi United Arab Emirates
1 shared publication - David Israyelyan
ICRANet-Armenia, Marshall Baghramian Avenue 24a, Yerevan 0019, Armenia
1 shared publication Dhurba Tripathi
New York University Abu Dhabi, P.O. Box 129188 Abu Dhabi, United Arab Emirates
1 shared publication
Latest Publications
- Open Access
- 19 February 2024
- Published by American Astronomical Society in The Astronomical Journal
- Vol. 167 (3) , 116
Firmamento: A Multimessenger Astronomy Tool for Citizen and Professional Scientists
Firmamento (https://firmamento.hosting.nyu.edu) is a new-concept, web-based, and mobile-friendly data analysis tool dedicated to multifrequency/multimessenger emitters, as exemplified by blazars. Although initially intended to support a citizen researcher project at New York University–Abu Dhabi, Firmamento has evolved to be a valuable tool for professional researchers due to its broad accessibility to classical and contemporary multifrequency open data sets. From this perspective Firmamento facilitates the identification of new blazars and other multifrequency emitters in the localization uncertainty regions of sources detected by current and planned observatories such as Fermi-LAT, Swift, eROSITA, CTA, ASTRI Mini-Array, LHAASO, IceCube, KM3Net, SWGO, etc. The multiepoch and multiwavelength data that Firmamento retrieves from over 90 remote and local catalogs and databases can be used to characterize the spectral energy distribution and the variability properties of cosmic sources as well as to constrain physical models. Firmamento distinguishes itself from other online platforms due to its high specialization, the use of machine learning and other methodologies to characterize the data, and for its commitment to inclusivity. From this particular perspective, its objective is to assist both researchers and citizens interested in science, strengthening a trend that is bound to gain momentum in the coming years as data retrieval facilities improve in power and machine-learning/artificial-intelligence tools become more widely available.
- 19 February 2024
- Published by American Astronomical Society in The Astronomical Journal
- Vol. 167 (3) , 116
Open AccessFirmamento: A Multimessenger Astronomy Tool for Citizen and Professional Scientists
Firmamento (https://firmamento.hosting.nyu.edu) is a new-concept, web-based, and mobile-friendly data analysis tool dedicated to multifrequency/multimessenger emitters, as exemplified by blazars. Although initially intended to support a citizen researcher project at New York University–Abu Dhabi, Firmamento has evolved to be a valuable tool for professional researchers due to its broad accessibility to classical and contemporary multifrequency open data sets. From this perspective Firmamento facilitates the identification of new blazars and other multifrequency emitters in the localization uncertainty regions of sources detected by current and planned observatories such as Fermi-LAT, Swift, eROSITA, CTA, ASTRI Mini-Array, LHAASO, IceCube, KM3Net, SWGO, etc. The multiepoch and multiwavelength data that Firmamento retrieves from over 90 remote and local catalogs and databases can be used to characterize the spectral energy distribution and the variability properties of cosmic sources as well as to constrain physical models. Firmamento distinguishes itself from other online platforms due to its high specialization, the use of machine learning and other methodologies to characterize the data, and for its commitment to inclusivity. From this particular perspective, its objective is to assist both researchers and citizens interested in science, strengthening a trend that is bound to gain momentum in the coming years as data retrieval facilities improve in power and machine-learning/artificial-intelligence tools become more widely available.