Calculation of the Photon Flux in a Photo-Multiplier Tube with Deep Learning
| dc.contributor.author | Bhanderi,Jigar | |
| dc.contributor.author | Funk,Stefan | |
| dc.contributor.author | Malyshev,Dmitry | |
| dc.contributor.author | Vogel,Naomi | |
| dc.contributor.author | Zmija,Andreas | |
| dc.contributor.editor | Demmler, Daniel | |
| dc.contributor.editor | Krupka, Daniel | |
| dc.contributor.editor | Federrath, Hannes | |
| dc.date.accessioned | 2022-09-28T17:10:29Z | |
| dc.date.available | 2022-09-28T17:10:29Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Intensity 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.doi | 10.18420/inf2022_42 | |
| dc.identifier.isbn | 978-3-88579-720-3 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39542 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik, Bonn | |
| dc.relation.ispartof | INFORMATIK 2022 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-326 | |
| dc.subject | Intensity interferometry | |
| dc.subject | IACTs | |
| dc.subject | deep neural networks | |
| dc.title | Calculation of the Photon Flux in a Photo-Multiplier Tube with Deep Learning | en |
| gi.citation.endPage | 516 | |
| gi.citation.startPage | 507 | |
| gi.conference.date | 26.-30. September 2022 | |
| gi.conference.location | Hamburg | |
| gi.conference.sessiontitle | Workshop on Machine Learning for Astroparticle Physics and Astronomy (ml.astro) |
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