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Auflistung nach Autor:in "Malyshev,Dmitry"

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  • Textdokument
    Calculation of the Photon Flux in a Photo-Multiplier Tube with Deep Learning
    (INFORMATIK 2022, 2022) Bhanderi,Jigar; Funk,Stefan; Malyshev,Dmitry; Vogel,Naomi; Zmija,Andreas
    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.
  • Textdokument
    Towards probabilistic multiclass classification of gamma-ray sources
    (INFORMATIK 2022, 2022) Malyshev,Dmitry; Bhat,Aakash
    Machine learning algorithms have been used to determine probabilistic classifications of unassociated sources. Often classification into two large classes, such as Galactic and extra-galactic, is considered. However, there are many more physical classes of sources (23 classes in the latest Fermi-LAT 4FGL-DR3 catalog). In this note we subdivide one of the large classes into two subclasses in view of a more general multi-class classification of gamma-ray sources. Each of the three large classes still encompasses several of the physical classes. We compare the performance of classifications into two and three classes. We calculate the receiver operating characteristic curves for two-class classification, where in case of three classes we sum the probabilities of the sub-classes in order to obtain the class probabilities for the two large classes. We also compare precision, recall, and reliability diagrams in the two- and three-class cases.