Neutrino Group of Livia Ludhova

Dr. Yury Malyshkin

Dr. Yury Malyshkin

January 2023 - Present (Postdoctoral Research Associate)
ORCID: 0000-0002-8759-7545
Scopus: 54409046000
Focus of research: Data quality monitoring, precise measurement of nueutino oscillation parameters, determination of mass ordering and study of the geoneutrino signal in JUNO.

Contact:

Phone: --
E-mail: y.malyshkin_AT_gsi.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.

Publications since 2023:

A. Abusleme et al., The Design and Technology Development of the JUNO Central Detector, arXiv:2311.17314 (2023, accepted by EPJ Plus).
Yu. Malyshkin (on behalf on the JUNO collaboration), JUNO Sensitivity to Neutrino Oscillation Parameters, Neutrino-2024 poster (2024).
A. Abusleme et al., Potential to Identify the Neutrino Mass Ordering with Reactor Antineutrinos in JUNO, arXiv:2405.18008 (2024, accepted by Chin.Phys.C).
A. Abusleme et al., Prediction of Energy Resolution in the JUNO Experiment, arXiv:2405.17860 (2024, accepted by Chin.Phys.C).
A. Abusleme et al., JUNO Sensitivity to Invisible Decay Modes of Neutrons, arXiv:2405.17792 (2024).
A. Abusleme et al., Model Independent Approach of the JUNO ⁸B Solar Neutrino Program, ApJ 965, 122 (2024).
A. Abusleme et al., Real-time Monitoring for the Next Core-Collapse Supernova in JUNO, JCAP 01, 057 (2024).
A. Abusleme et al., JUNO sensitivity to ⁷Be, pep, and CNO solar neutrinos, JCAP 10, 022 (2023).
A. Abusleme et al., JUNO Sensitivity on Proton Decay p→ν¯K+ Searches, Chin. Phys. C 47, 113002 (2023).
A. Abusleme et al., The JUNO Experiment Top Tracker, NIM-A 1057, 168680 (2023).
A. Abusleme et al., JUNO Sensitivity to the Annihilation of MeV Dark Matter in the Galactic Halo, J. Cos. Astro. Phys. 09 (2023).

Selected Publications before 2023:

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):

Novel Fitting Approach Based on a Neural Network for JUNO (parallel), CHEP 2024, Krakow, Poland.
JUNO Sensitivity to Neutrino Oscillation Parameters (poster), Neutrino 2024, Milano, Italy.
New Chapter in Neutrino Physics with JUNO (invited talk), PDG Spring Meeting 2024, Dresden, Germany.
JUNO Status and Prospects (plenary), NeuTel 2023, Venice, Italy.
JUNO's Perspective for Geoneutrinos (plenary), MMTE 2023, Paris, France.
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 Neutrino Experiments (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.