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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)

Measurement of the longitudinal polarization in decays

2024 · ARTICLE · en

The longitudinal polarization fraction of the meson is measured in decays, where the lepton decays to three charged pions and a neutrino, using proton-proton collision data collected by the LHCb experiment at center-of-mass energies of 7, 8 and 13 TeV and corresponding to an integrated luminosity of . The polarization fraction is measured in two regions, below and above , where is defined as the squared invariant mass of the system. The values are measured to be and for the lower and higher regions, respectively. The first uncertainties are statistical and the second systematic. The average value over the whole range is . These results are compatible with the Standard Model predictions.

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Test of lepton flavor universality using decays with hadronic 𝜏 channels

2024 · ARTICLE · en

The branching fraction ℬ⁡(𝐵0→𝐷*−⁢𝜏+⁢𝜈𝜏) is measured relative to that of the normalization mode 𝐵0 →𝐷*−⁢𝜋+⁢𝜋−⁢𝜋+ using hadronic 𝜏+ →𝜋+⁡𝜋−⁡𝜋+⁡(𝜋0)⁢¯𝜈𝜏 decays in proton-proton collision data at a center-of-mass energy of 13 TeV collected by the LHCb experiment, corresponding to an integrated luminosity of 2 fb−1. The measured ratio is ℬ⁡(𝐵0→𝐷*−⁢𝜏+⁢𝜈𝜏)/ℬ⁡(𝐵0→𝐷*−⁢𝜋+⁢𝜋−⁢𝜋+) =1.70 ±0.1⁢0+0.11−0.10, where the first uncertainty is statistical and the second is related to systematic effects. Using established branching fractions for the 𝐵0 →𝐷*−⁢𝜋+⁢𝜋−⁢𝜋+ and 𝐵0 →𝐷*−⁢𝜇+⁢𝜈𝜇 modes, the lepton universality test ℛ⁡(𝐷*−) ≡ℬ⁡(𝐵0→𝐷*−⁢𝜏+⁢𝜈𝜏)/ℬ⁡(𝐵0→𝐷*−⁢𝜇+⁢𝜈𝜇) is calculated, ℛ⁡(𝐷*−)=0.247±0.015±0.015±0.012, where the third uncertainty is due to the uncertainties on the external branching fractions. This result is consistent with the Standard Model prediction and with previous measurements.

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Measurement of the Λ c + Λ c + ​ to D 0 0 production ratio in periphera PbPb collisions at s NN s NN ​ ​ = 5.02 TeV

2024 · ARTICLE · en

We report on a measurement of the Λc+ to D0 production ratio in peripheral PbPb collisions at sNN = 5.02 TeV with the LHCb detector in the forward rapidity region 2 4.5. The Λc+ (D0) hadrons are reconstructed via the decay channel Λc+ → pK−π+ (D0 → K−π+) for 2 T 8 GeV/c and in the centrality range of about 65–90%. The results show no significant dependence on pT, y or the mean number of participating nucleons. They are also consistent with similar measurements obtained by the LHCb collaboration in pPb and Pbp collisions at sNN = 5.02 TeV. The data agree well with predictions from PYTHIA in pp collisions at s = 5 TeV but are in tension with predictions of the Statistical Hadronization model.

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Supernova Light Curves Approximation based on Neural Network Models

2023 · ARTICLE · en

Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine learning models. These models learn to classify supernova types using their light curves as inputs. Preprocessing these curves is a crucial step that significantly affects the final quality. In this talk, we study the application of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing flows (NF) to approximate observations for a single light curve. We use these approximations as inputs for supernovae classification models and demonstrate that the proposed methods outperform the state-of-the-art based on Gaussian processes applying to the Zwicky Transient Facility Bright Transient Survey light curves. MLP demonstrates similar quality as Gaussian processes and speed increase. Normalizing Flows exceeds Gaussian processes in terms of approximation quality as well.

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Toward an understanding of the properties of neural network approaches for supernovae light curve approximation

2023 в печати · ARTICLE · en

The modern 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. Most of the objects have never received a spectroscopic follow-up, which is especially crucial for transients e.g. supernovae. In such cases, observed light curves could present an affordable alternative. Time series are actively used for photometric classification and characterization, such as peak and luminosity decline estimation. However, the collected time series are multidimensional, irregularly sampled, contain outliers, and do not have well-defined systematic uncertainties. Machine learning methods help extract useful information from available data in the most efficient way. We consider several light curve approximation methods based on neural networks: Multilayer Perceptrons, Bayesian Neural Networks, and Normalizing Flows, to approximate observations of a single light curve. Tests using both the simulated PLAsTiCC and real Zwicky Transient Facility data samples demonstrate that even few observations are enough to fit networks and achieve better approximation quality than other state-of-the-art methods. We show that the methods described in this work have better computational complexity and work faster than Gaussian Processes. We analyze the performance of the approximation techniques aiming to fill the gaps in the observations of the light curves, and show that the use of appropriate technique increases the accuracy of peak finding and supernova classification. In addition, the study results are organized in a Fulu Python library available on GitHub, which can be easily used by the community.

Stokes inversion techniques with neural networks: analysis of uncertainty in parameter estimation

2023 · ARTICLE · en

Magnetic fields are responsible for a multitude of Solar phenomena, including such destructive events as solar flares and coronal mass ejections, with the number of such events rising as we approach the peak of the 11-year solar cycle, in approximately 2025. High-precision spectropolarimetric observations are necessary to understand the variability of the Sun. The field of quantitative inference of magnetic field vectors and related solar atmospheric parameters from such observations has long been investigated. In recent years, very sophisticated codes for spectropolarimetric observations have been developed. Over the past two decades, neural networks have been shown to be a fast and accurate alternative to classic inversion technique methods. However, most of these codes can be used to obtain point estimates of the parameters, so ambiguities, the degeneracies, and the uncertainties of each parameter remain uncovered. In this paper, we provide end-to-end inversion codes based on the simple Milne-Eddington model of the stellar atmosphere and deep neural networks to both parameter estimation and their uncertainty intervals. The proposed framework is designed in such a way that it can be expanded and adapted to other atmospheric models or combinations of them. Additional information can also be incorporated directly into the model. It is demonstrated that the proposed architecture provides high accuracy of results, including a reliable uncertainty estimation, even in the multidimensional case. The models are tested using simulation and real data samples.

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Search for the rare hadronic decay Bs0->pp

2023 · ARTICLE · en

A search for the rare hadronic decay B0s→p¯p is performed using proton-proton collision data recorded by the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 6 fb−1. No evidence of the decay is found and an upper limit on its branching fraction is set at B(B0s→p¯p)

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Measurement of the Branching Fractions B(B0→pp¯pp¯) and B(Bs0→pp¯pp¯)

2023 · ARTICLE · en

Searches for the rare hadronic decays B0→p¯pp¯p and B0s→p¯pp¯p are performed using proton-proton collision data recorded by the LHCb experiment and corresponding to an integrated luminosity of 9 fb−1. Significances of 9.3σ and 4.0σ, including statistical and systematic uncertainties, are obtained for the B0→p¯pp¯p and B0s→p¯pp¯p signals, respectively. The branching fractions are measured relative to the topologically similar normalization decays B0→J/ψ(→p¯p)K*0(→K+π−) and B0s→J/ψ(→p¯p)ϕ(→K+K−). The branching fractions are measured to be B(B0→p¯pp¯p)=(2.2±0.4±0.1±0.1)×10−8 and B(B0s→p¯pp¯p)=(2.3±1.0±0.2±0.1)×10−8. In these measurements, the first uncertainty is statistical, the second is systematic, and the third one is due to the external branching fraction of the normalization channel.

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Measurement of the Time-Integrated CP Asymmetry in D0→K-K+ Decays

2023 · ARTICLE · en

The time-integrated CP asymmetry in the Cabibbo-suppressed decay D0→K-K+ is measured using proton-proton collision data, corresponding to an integrated luminosity of 5.7 fb-1 collected at a center-of-mass energy of 13 TeV with the LHCb detector. The D0 mesons are required to originate from promptly produced D*+→D0π+ decays, and the charge of the companion pion is used to determine the flavor of the charm meson at production. The time-integrated CP asymmetry is measured to be ACP(K-K+)=[6.8±5.4±1.6]×10-4 where the first uncertainty is statistical and the second systematic. The direct CP asymmetries in D0→K-K+ and D0→π-π+ decays, aK-K+d and aπ-π+d, are derived by combining ACP(K-K+) with the time-integrated CP asymmetry difference, ΔACP=ACP(K-K+)-ACP(π-π+), and other inputs, giving aK-K+d=(7.7±5.7)×10-4,aπ-π+d=(23.2±6.1)×10-4,with a correlation coefficient corresponding to ρ=0.88. The compatibility of these results with CP symmetry is 1.4 and 3.8 standard deviations for D0→K-K+ and D0→π-π+ decays, respectively. This is the first evidence for direct CP violation in a specific D0 decay.

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Test of lepton flavor universality using B0→D*-τ+ντ decays with hadronic τ channels

2023 · ARTICLE · en

The branching fraction B(B0→D*−τ+ντ) is measured relative to that of the normalization mode B0→D*−π+π−π+ using hadronic τ+→π+π−π+(π0)¯ντ decays in proton-proton collision data at a center-of-mass energy of 13 TeV collected by the LHCb experiment, corresponding to an integrated luminosity of 2 fb−1. The measured ratio is B(B0→D*−τ+ντ)/B(B0→D*−π+π−π+)=1.70±0.10+0.11−0.10, where the first uncertainty is statistical and the second is related to systematic effects. Using established branching fractions for the B0→D*−π+π−π+ and B0→D*−μ+νμ modes, the lepton universality test R(D*−)≡B(B0→D*−τ+ντ)/B(B0→D*−μ+νμ) is calculated, R(D*−)=0.247±0.015±0.015±0.012, where the third uncertainty is due to the uncertainties on the external branching fractions. This result is consistent with the Standard Model prediction and with previous measurements.

DOI ↗ PDF ↗

Курсы (8)