تقرير
Firmamento: a multi-messenger astronomy tool for citizen and professional scientists
| العنوان: | Firmamento: a multi-messenger astronomy tool for citizen and professional scientists |
|---|---|
| المؤلفون: | Tripathi, Dhurba, Giommi, Paolo, Di Giovanni, Adriano, Almansoori, Rawdha R., Hamly, Nouf Al, Arneodo, Francesco, Macciò, Andrea V., Puccetti, Goffredo, de Almeida, Ulisses Barres, Brandt, Carlos, Di Pippo, Simonetta, Doro, Michele, Israyelyan, David, Pollock, Andrew M. T., Sahakyan, Narek |
| سنة النشر: | 2023 |
| المجموعة: | Astrophysics |
| مصطلحات موضوعية: | Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics |
| الوصف: | Firmamento (https://firmamento.hosting.nyu.edu) is a new-concept web-based and mobile-friendly data analysis tool dedicated to multi-frequency/multi-messenger emitters, as exemplified by blazars. Although initially intended to support a citizen researcher project at New York University-Abu Dhabi (NYUAD), Firmamento has evolved to be a valuable tool for professional researchers due to its broad accessibility to classical and contemporary multi-frequency open data sets. From this perspective Firmamento facilitates the identification of new blazars and other multi-frequency emitters in the localisation 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 multi-epoch and multi-wavelength data that Firmamento retrieves from over 90 remote and local catalogues and databases can be used to characterise 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 characterise 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 Comment: Accepted for publication in AJ |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2311.15102 |
| رقم الأكسشن: | edsarx.2311.15102 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |