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Гущин Михаил Иванович

Факультет компьютерных наук

Профиль на hse.ru ↗ тел.: 27261
Публикаций
313
Языков
3
Наград
7
Конференций
1
Профиль Публикации (313) Курсы (8)

Профессиональные интересы

анализ данныхфизика высоких энергий

Должности

  • Заместитель заведующего лабораторией — Факультет компьютерных наук, Институт искусственного интеллекта и цифровых наук, Научно-учебная лаборатория методов анализа больших данных
  • Ведущий научный сотрудник — Факультет компьютерных наук, Институт искусственного интеллекта и цифровых наук, Научно-учебная лаборатория методов анализа больших данных
  • Доцент — Факультет компьютерных наук, Департамент больших данных и информационного поиска

Био

  • · Начал работать в НИУ ВШЭ в 2017 году.
  • · Научно-педагогический стаж: 8 лет.

Образование

  • 2020 · Кандидат наук: Московский физико-технический институт (национальный исследовательский университет)
  • 2019 · Аспирантура: Московский физико-технический институт (национальный исследовательский университет), специальность «Информатика и вычислительная техника»
  • 2015 · Магистратура: Московский физико-технический институт (государственный университет), специальность «Прикладные математика и физика», квалификация «Магистр»
  • 2013 · Бакалавриат: Московский физико-технический институт (государственный университет), специальность «Прикладные математика и физика», квалификация «Бакалавр»

Опыт работы

  • · 2014 - 2017: Исследователь-разработчик в OOO "Яндекс"

Награды и поощрения

  • · Благодарность первого проректора НИУ ВШЭ (август 2024)
  • · Благодарность НИУ ВШЭ (май 2024)
  • · Благодарность проректора НИУ ВШЭ (сентябрь 2022)
  • · Благодарность факультета компьютерных наук НИУ ВШЭ (август 2022)
  • · Надбавка за публикацию в журнале из Списка А (и приравненном к нему научном издании) (2025–2026, 2024–2025, 2023–2024)
  • · Надбавка за публикацию в международном рецензируемом научном издании (2022–2023, 2021–2022, 2020–2022, 2018–2019)
  • · Лучший преподаватель — 2024

Гранты и проекты

  • — · на соискание учёной степени кандидата наук

Конференции (1)

Показать все
  • · 2021: ACAT 2021 (Daejeon). Доклад: Robust Neural Particle Identification Models

Идентификаторы исследователя

Публикации (313)

Open charm production and asymmetry in pNe collisions at √sNN=68.5GeV

2023 · ARTICLE · en

A measurement of D0 meson production by the LHCb experiment in its fixed-target configuration is presented. The production of D0 mesons is studied with a beam of 2.5 TeV protons colliding on a gaseous neon target at rest, corresponding to a nucleon-nucleon centre-of-mass energy of sNN√ = 68.5 GeV. The sum of the D0 and D0 production cross-section in pNe collisions in the centre-of-mass rapidity range y⋆∈[−2.29,0] is found to be σy⋆∈[−2.29,0]D0=48.2±0.3±4.5μb/nucleon where the first uncertainty is statistical and the second is systematic. The D0−D0 production asymmetry is also evaluated and suggests a trend towards negative values at large negative y⋆. The considered models do not account precisely for all the features observed in the LHCb data, but theoretical predictions including 1% intrinsic charm and 10% recombination contributions better describe the data than the other models considered.

DOI ↗ PDF ↗

Charmonium production in pNe collisions at √sNN = 68.5 GeV

2023 · ARTICLE · en

The measurement of charmonium states produced in proton-neon (pNe) collisions by the LHCb experiment in its fixed-target configuration is presented. The production of J/ψ and ψ(2S) mesons is studied with a beam of 2.5 TeV protons colliding on gaseous neon targets at rest, corresponding to a nucleon-nucleon centre-of-mass energy sNN‾‾‾‾√=68.5 GeV. The data sample corresponds to an integrated luminosity of 21.7±1.4 nb−1. The J/ψ and ψ(2S) hadrons are reconstructed in μ+μ− final states. The J/ψ production cross-section per target nucleon in the centre-of-mass rapidity range y∗∈[−2.29,0] is found to be 506±8±25 nb/nucleon. The ψ(2S) to J/ψ relative production rate is found to be (1.67±0.27±0.10)% in good agreement with other measurements involving beam and target nuclei of similar sizes.

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J/ψ and D0 production in √sNN = 68.5 GeV PbNe collisions

2023 · ARTICLE · en

The first measurement of J/ψ and D0 production in PbNe collisions by the LHCb experiment in its fixed-target configuration is reported. The production of J/ψ and D0 mesons is studied with a beam of lead ions with an energy of 2.5 TeV per nucleon colliding on gaseous neon targets at rest, corresponding to a nucleon-nucleon centre-of-mass energy of √sNN = 68.5 GeV. The J/ψ/D0 production cross-section ratio is studied as a function of rapidity, transverse momentum and collision centrality. These data are compared with measurements from pNe collisions at the same energy and show no difference in the observed J/ψ suppresion trend when comparing pNe and PbNe peripheral collisions with PbNe central collisions.

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Observation of the B0s→ D*+D*− decay

2023 · ARTICLE · en

The first observation of the Bs0 → D∗+D∗− decay and the measurement of its branching ratio relative to the B0 → D∗+D∗− decay are presented. The data sample used corresponds to an integrated luminosity of 9fb−1 of proton-proton collisions recorded by the LHCb experiment at centre-of-mass energies of 7, 8 and 13 TeV between 2011 and 2018. The decay is observed with more than 10 standard deviations and the time-integrated ratio of branching fractions is determined to be B(Bs0 → D∗+D∗−) = 0.269 ± 0.032 ± 0.011 ± 0.008 , B(B0 → D∗+D∗−) where the first uncertainty is statistical, the second systematic and the third due to the uncertainty of the fragmentation fraction ratio fs/fd. The Bs0 → D∗+D∗− branching fraction is calculated to be B(Bs0 → D∗+D∗−) = (2.15 ± 0.26 ± 0.09 ± 0.06 ± 0.16) × 10−4 , where the fourth uncertainty is due to the B0 → D∗+D∗− branching fraction. These results are calculated using the average Bs0 meson lifetime in simulation. Correction factors are reported for scenarios where either a purely heavy or a purely light Bs0 eigenstate is considered.

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Observation of the decays B0(s) → Ds1(2536)∓K±

2023 · ARTICLE · en

https://link.springer.com/article/10.1007/JHEP10(2023)106#Abs1

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Understanding of the properties of neural network approaches for transient light curve approximations

2023 · ARTICLE · en

Context. Modern-day time-domain photometric surveys collect a lot of observations of various astronomical objects and the coming era of large-scale surveys will provide even more information on their properties. Spectroscopic follow-ups are especially crucial for transients such as supernovae and most of these objects have not been subject to such studies. Aims. Flux time series are actively used as an affordable alternative for photometric classification and characterization, for instance, peak identifications and luminosity decline estimations. However, the collected time series are multidimensional and irregularly sampled, while also containing outliers and without any well-defined systematic uncertainties. This paper presents a search for the best-performing methods to approximate the observed light curves over time and wavelength for the purpose of generating time series with regular time steps in each passband. Methods. We examined several light curve approximation methods based on neural networks such as multilayer perceptrons, Bayesian neural networks, and normalizing flows to approximate observations of a single light curve. Test datasets include simulated PLAsTiCC and real Zwicky Transient Facility Bright Transient Survey light curves of transients. Results. The tests demonstrate that even just a few observations are enough to fit the networks and improve the quality of approximation, compared to state-of-the-art models. The methods described in this work have a low computational complexity and are significantly faster than Gaussian processes. Additionally, we analyzed the performance of the approximation techniques from the perspective of further peak identification and transients classification. The study results have been released in an open and user-friendly Fulu Python library available on GitHub for the scientific community.

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The Tracking Machine Learning Challenge: Throughput Phase

2023 · ARTICLE · en

This paper reports on the second “Throughput” phase of the Tracking Machine Learning (TrackML) challenge on the Codalab platform. As in the first “Accuracy” phase, the participants had to solve a difficult experimental problem linked to tracking accurately the trajectory of particles as e.g. created at the Large Hadron Collider (LHC): given �(105) points, the participants had to connect them into �(104) individual groups that represent the particle trajectories which are approximated helical. While in the first phase only the accuracy mattered, the goal of this second phase was a compromise between the accuracy and the speed of inference. Both were measured on the Codalab platform where the participants had to upload their software. The best three participants had solutions with good accuracy and speed an order of magnitude faster than the state of the art when the challenge was designed. Although the core algorithms were less diverse than in the first phase, a diversity of techniques have been used and are described in this paper. The performance of the algorithms is analysed in depth and lessons derived.

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Observation of New Baryons in the Ξ−bπ+π−and Ξ0bπ+π− Systems

2023 · ARTICLE · en

https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.131.171901

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Observation and branching fraction measurement of the decay Ξ−b→Λ0bπ−

2023 · ARTICLE · en

https://journals.aps.org/prd/abstract/10.1103/PhysRevD.108.072002

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Search for CP violation in the phase space of D0 → π−π+π0 decays with the energy test

2023 · ARTICLE · en

https://link.springer.com/article/10.1007/JHEP09(2023)129#Abs1

DOI ↗ PDF ↗

Курсы (8)