2004
DOI: 10.1088/1367-2630/6/1/114
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SNEWS: the SuperNova Early Warning System

Abstract: This paper provides a technical description of the SuperNova Early Warning System (SNEWS), an international network of experiments with the goal of providing an early warning of a galactic supernova.

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Cited by 266 publications
(232 citation statements)
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“…The detailed observation of the onset of a supernova explosion is of much interest to astronomers. Since 2009, IceCube has been sending real-time datagrams to the Supernova Early Warning System (SNEWS) (Antonioli et al 2004) when detecting supernova candidate events. SNEWS has been set up to broadcast a reliable alert to the astronomy community when a supernova has been detected by several neutrino detectors within seconds of each other.…”
Section: Introductionmentioning
confidence: 99%
“…The detailed observation of the onset of a supernova explosion is of much interest to astronomers. Since 2009, IceCube has been sending real-time datagrams to the Supernova Early Warning System (SNEWS) (Antonioli et al 2004) when detecting supernova candidate events. SNEWS has been set up to broadcast a reliable alert to the astronomy community when a supernova has been detected by several neutrino detectors within seconds of each other.…”
Section: Introductionmentioning
confidence: 99%
“…A network of neutrino detectors around the world (SNEWS) will alert astronomers in the event of a SN burst [27], neutrinos arriving earlier than photons on Earth. Moreover, we could determine the angular location of a SN with its neutrinos [28,29] with an error of about 5 o in e.g.…”
Section: What Could We Learn From the Next Supernova Burst?mentioning
confidence: 99%
“…As we are able to properly describe the background behaviour in LVD, we identify as neutrino burst candidates only those clusters which would be produced by background fluctuations less than 0.01 year −1 , relaxing the threshold to 1 month −1 when working in time coincidence with other detectors as in the SNEWS project (see Antonioli et al, 2004). Less sensitive thresholds on F im (i.e.…”
Section: The Selection Algorithmmentioning
confidence: 99%