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  1. Determination of Spin-Parity Quantum Numbers for the Narrow Structure near the pΛ[over ¯] Threshold in e^{+}e^{-}→pK^{-}Λ[over ¯]+c.c

    BESIII Collaboration, Мучной, Н. Ю. & Николаев, И. Б., 13 Oct 2023, In: Physical Review Letters. 131, 15, p. 151901

    Research output: Contribution to journalArticlepeer-review

  2. Determination of spin and parity of D(s)⁎ mesons

    BESIII Collaboration & Мучной, Н. Ю., 1 Nov 2023, In: Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics. 846, p. 138245

    Research output: Contribution to journalArticlepeer-review

  3. Determination of jet calibration and energy resolution in proton–proton collisions at √s=8TeV using the ATLAS detector

    The ATLAS collaboration & Bogdanchikov, A. G., Dec 2020, In: European Physical Journal C. 80, 12, 81 p., 1104.

    Research output: Contribution to journalArticlepeer-review

  4. Detailed report on the measurement of the positive muon anomalous magnetic moment to 0.20 ppm

    The Muon g-2 Collaboration, 8 Aug 2024, In: Physical Review D. 110, 3, 032009.

    Research output: Contribution to journalArticlepeer-review

  5. Design of the Central Solenoid of the HV Electron Cooling System for the NICA Collider

    Kremnev, N. S., Bryzgunov, M. I., Bubley, A. V., Parkhomchuk, V. V., Panasyuk, V. M., Reva, V. B., Putmakov, A. A., Pospolita, S. P. & Shiyankov, S. V., 1 Jul 2020, In: Physics of Particles and Nuclei Letters. 17, 4, p. 438-442 5 p.

    Research output: Contribution to journalArticlepeer-review

  6. Deriving eigenmode excitation spectrum of synthetic photonic lattices by means of optical heterodyning

    Tikan, A. M., Vatnik, I. D., Churkin, D. V. & Sukhorukov, A. A., 1 Feb 2017, In: Laser Physics. 27, 2, 6 p., 026203.

    Research output: Contribution to journalArticlepeer-review

  7. Dependence of inclusive jet production on the anti-kT distance parameter in pp collisions at √s = 13 TeV

    The CMS collaboration, Dec 2020, In: Journal of High Energy Physics. 2020, 12, 43 p., 82.

    Research output: Contribution to journalArticlepeer-review

  8. Deep Reinforcement Learning Control of Cylinder Flow Using Rotary Oscillations at Low Reynolds Number

    Tokarev, M., Palkin, E. & Mullyadzhanov, R., 2 Nov 2020, In: Energies. 13, 22, 11 p., 5920.

    Research output: Contribution to journalArticlepeer-review

  9. Deep learning with synthetic photonic lattices for equalization in optical transmission systems

    Pankov, A. V., Sidelnikov, O. S., Vatnik, I. D., Sukhorukov, A. A. & Churkin, D. V., 20 Nov 2019, Real-Time Photonic Measurements, Data Management, and Processing IV. Li, M., Jalali, B. & Asghari, M. H. (eds.). The International Society for Optical Engineering, p. 24 11 p. 111920N. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 11192).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

  10. Deep learning segmentation to analyze bubble dynamics and heat transfer during boiling at various pressures

    Malakhov, I., Seredkin, A., Chernyavskiy, A., Serdyukov, V., Mullyadzanov, R. & Surtaev, A., May 2023, In: International Journal of Multiphase Flow. 162, 104402.

    Research output: Contribution to journalArticlepeer-review

ID: 3085446