Publications

  1. Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks with 9.3 Years of Data in IceCube DeepCore: R. Abbasi et al., Phys. Rev. Lett., doi: 10.1103/PhysRevLett.134.091801
  2. Bayesian Deep-stacking for high-energy neutrino searches: I. Bartos et al., J. Cosmol. Astropart. Phys., doi: 10.1088/1475-7516/2025/06/064
  3. Cosmic ray spectrum from 250 TeV to 10 PeV using IceTop: The IceCube Collaboration et al., Phys. Rev. D, doi: 10.1103/PhysRevD.102.122001
  4. Background rejection in atmospheric Cherenkov telescopes using recurrent convolutional neural networks: R. D. Parsons et al., Eur. Phys. J. C, doi: 10.1140/epjc/s10052-020-7953-3
  5. Russian–German Astroparticle Data Life Cycle Initiative: Igor Bychkov et al., Data, doi: 10.3390/data3040056
  6. Measurement of the νµ energy spectrum with IceCube-79: M. G. Aartsen et al., Eur. Phys. J. C, doi: 10.1140/epjc/s10052-017-5261-3
  7. Improved γ/hadron separation for the detection of faint γ -ray sources using boosted decision trees: Maria Krause et al., Astroparticle Physics, doi: 10.1016/j.astropartphys.2017.01.004