Dr. Yury Malyshkin

Dr. Yury Malyshkin
  • January 2023 - Present (Postdoctoral Research Associate)
  • ORCID: 0000-0002-8759-7545
  • Scopus: 54409046000
  • Focus of research: Precise measurement of nueutino oscillation parameters, determination of mass ordering and study of the geoneutrino signal in JUNO.
Contact:
  • Phone: +49 2461 61 3723
  • E-mail: y.malyshkin_AT_fz-juelich.de

Education

  • PhD in Physics (2011 - 2014)
    Goethe University of Frankfurt
    Thesis Title: Modeling of neutron production and transport in spallation targets

  • Specialist Diploma (2005 - 2011)
    Lomonosov Moscow State University, Department of Cosmic Ray Astrophysics and Space Physics at Faculty of Physics
    Thesis Title: Simulation of thunderstorm neutron generation and transport in the atmosphere up to altitude of 500 km

Past Research Topics

  • Development of simulation software for the Baikal-GVD neutrino telescope.
  • Study of applicability of machine learning techniques for energy reconstruction of reactor neutrino events in JUNO.
  • Sensitivity analysis of precision measurement of neutrino oscillation parameters with JUNO.
  • Data analysis at the SAGE and BEST experiments.
  • Joint sterile neutrino analysis of the MINOS, Daya Bay and Bugey-3 experiments.
  • Investigation of time-dependent non-uniformity of signal in Daya Bay.
  • Monte Carlo simulaiton of neutron production in accelerator driven systems with fissile targets.
  • Simulation of neutron production in thunderstorms.

Positions

  • 2019 - 2022: PostDoc (JINR Distinguish Postdoctoral Research Fellowship) at Joint Institute for Nuclear Research, Russia.
  • 2018 – 2022: Research Assistant – Junior Researcher – Researcher – Senior Researcher at Institute for Nuclear Research RAS, Russia.
  • 2017 – 2019: PostDoc (INFN post-doctoral fellowship) at National Institute for Nuclear Physics, Roma Tre University, Italy.
  • 2014 – 2017: PostDoc (FONDECYT fellowship) at Pontifical Catholic University of Chile, Chile.

Expertise

  • Monte Carlo simulation with Geant4.
  • Machine learning techniques.
  • Statistical data analysis.

Teaching and suprevision

  • Baikal Summer School on Particle Physics and Astrophysics 2022: Tutorial on Geant4 simulation.
  • Co-supervision of bachelor and master students at JINR.
  • Co-supervision of bachelor students at Padova University.

Selected Publications:

  • Yu. Malyshkin et al. (on behalf of Baikal-GVD collaboration), Baikal-GVD neutrino telescope: Design reference 2022, Nucl. Instr. Meth. A, 1050, 168117 (2023).
  • A. Gavrikov, Yu. Malyshkin, and F. Ratnikov, Energy reconstruction for large liquid scintillator detectors with machine learning techniques: aggregated features approach, Eur. Phys. Jour. C 82, 1021 (2022).
  • M. He et al. (JUNO collaboration), Sub-percent Precision Measurement of Neutrino Oscillation Parameters with JUNO, Chin. Phys. C 46, 123001 (2022).
  • V. Barinov et al. (BEST collaboration), Results from the Baksan Experiment on Sterile Transitions (BEST), Phys. Rev. Lett., 128, 232501 (2022).
  • Z. Qian et al., Vertex and energy reconstruction in JUNO with machine learning methods, Nucl. Instr. Meth. A, 1010, 165527 (2021).
  • Yu. Malyshkin et al., Modeling of a MeV-scale particle detector based on organic liquid scintillator, Nucl. Instr. Meth. A, 951, 162920 (2020).
  • P. Adamson et al. (Daya Bay and MINOS collaborations), Limits on Active to Sterile Neutrino Oscillations from Disappearance Searches in the MINOS, Daya Bay, and Bugey-3 Experiments, Phys. Rev. Lett, 117, 151801 (2016).
  • T. Adam et al. (JUNO collaboration), JUNO Conceptual Design Report, arXiv:1508.07166 (2015)
  • D. Abdurashitov, Yu. Malyshkin, V. Matushko and B. Suerfu, Response of a proportional counter to Ar-37 andGe-71: Measured spectra versus Geant4 simulation, Nucl. Instr. Meth. B, 375, 5-9 (2016).
  • B.T. Cleveland, V.N. Gavrin, V.V. Gorbachev, T.V. Ibragimova, T.V. Knodel, Yu. Malyshkin, I.N. Mirmov, E.P. Veretenkin, Use of enriched isotopes to measure efficiency of chemical extraction in the SAGE solar neutrino experiment, International Journal of Mass Spectrometry, Vol. 392, 41-44 (2015).
  • Yu. Malyshkin, I. Pshenichnov, I. Mishustin, W. Greiner, Synthesis of neutron-rich transuranic nuclei in fissile spallation targets, Nucl. Instr. Meth. B, 349, 133-140 (2015).

Conference Talks (Since 2016):

  • Baikal-GVD Neutrino Telescope (plenary), CRIS 2022, Naples, Italy.
  • A new software framework for the Baikal-GVD neutrino telescope (plenary), JINR AYSS Conference 2022, Alushta, Russia.
  • Status and Physical Potential of JUNO (plenary), NUCLEUS 2021, online.
  • Application of machine learning techniques for event reconstruction in JUNO (parallel), NuFact 2021, Cagliari/online, Italy.
  • Oscillation Physics in JUNO (parallel), NeuTel 2021, online.
  • Baksan experiment on sterile transitions (plenary), Lomonosov 2019, Moscow, Russia.
  • Event Reconstruction in Large Liquid Scintillator Detectors with Machine Learning Techniques (plenary), MISP 2019, Voronovo, Russia.
  • 3-inch PMT System and Double Calorimetry at JUNO (parallel), Baksan-50 (2017), Nalchik, Russia.
  • Status of the JUNO Experiment (parallel), Baksan-50 (2017), Nalchik, Russia.
  • Daya Bay and JUNO Reactor Neutri (plenary), Particle Physics and Cosmology Conference 2017, Corpus Christi, USA.
  • Sterile neutrino search at the Baksan Neutrino Observatory – Experiment BEST (plenary), Particle Physics and Cosmology Conference 2017, Corpus Christi, USA.
  • JUNO: a Next Generation Multipurpose Reactor Neutrino Experiment (parallel), Lomonosov 2016, Moscow, Russia.
  • JUNO: Next Generation Reactor Neutrino Experiment (plenary), HEP 2016 , Valparaiso, Chile.