Firmamento: A Multimessenger Astronomy Tool for Citizen and Professional Scientists
Authors:
- Paolo GiommiPGPaolo Giommi
- Adriano Di GiovanniAGAdriano Di Giovanni
- Francesco ArneodoFAFrancesco Arneodo
- Andrea V. MacciòAMAndrea V. Macciò
- Goffredo PuccettiGPGoffredo Puccetti
- Ulisses Barres de AlmeidaUlisses Barres de Almeida
- Carlos BrandtCBCarlos Brandt
- Simonetta Di PippoSPSimonetta Di Pippo
- Michele DoroMichele Doro
Open Access
- 19 February 2024
- journal article
- research article
- Published by American Astronomical Society in The Astronomical Journal
- Vol. 167 (3) , 116
Abstract
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.Keywords
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