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Calculation of the Photon Flux in a Photo-Multiplier Tube with Deep Learning

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dc.contributor.authorBhanderi,Jigar
dc.contributor.authorFunk,Stefan
dc.contributor.authorMalyshev,Dmitry
dc.contributor.authorVogel,Naomi
dc.contributor.authorZmija,Andreas
dc.contributor.editorDemmler, Daniel
dc.contributor.editorKrupka, Daniel
dc.contributor.editorFederrath, Hannes
dc.date.accessioned2022-09-28T17:10:29Z
dc.date.available2022-09-28T17:10:29Z
dc.date.issued2022
dc.description.abstractIntensity interferometry is part of optical interferometry, which provides a sub-milliarcsecond resolution of astronomical objects. In intensity interferometry one correlates intensities of optical fluxes rather than amplitudes of waves. For a successful measurement one needs a large light collecting area for several telescopes separated by hundreds of meters and good time resolution of the intensity flux. Air Cherenkov telescopes, e.g., H.E.S.S. are a natural candidate for performing such a measurement. One of the important tasks is to determine the rate of photons hitting the PMTs to calculate expectations on the signal-to-noise ratio. For low rates, the individual pulses can be resolved and counted, but for high rates, relevant for the IACTs, the pulses from the photons overlap. We use different neural network algorithms in order to determine the rate of photons hitting the PMT, including the high rates.en
dc.identifier.doi10.18420/inf2022_42
dc.identifier.isbn978-3-88579-720-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39542
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-326
dc.subjectIntensity interferometry
dc.subjectIACTs
dc.subjectdeep neural networks
dc.titleCalculation of the Photon Flux in a Photo-Multiplier Tube with Deep Learningen
gi.citation.endPage516
gi.citation.startPage507
gi.conference.date26.-30. September 2022
gi.conference.locationHamburg
gi.conference.sessiontitleWorkshop on Machine Learning for Astroparticle Physics and Astronomy (ml.astro)

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P326 - INFORMATIK 2022 - Informatik in den Naturwissenschaften