| 000 | 03698nlm1a2200505 4500 | ||
|---|---|---|---|
| 001 | 665228 | ||
| 005 | 20231030041956.0 | ||
| 035 | _a(RuTPU)RU\TPU\network\36427 | ||
| 035 | _aRU\TPU\network\33886 | ||
| 090 | _a665228 | ||
| 100 | _a20210903a2021 k y0engy50 ba | ||
| 101 | 0 | _aeng | |
| 102 | _aGB | ||
| 135 | _adrcn ---uucaa | ||
| 181 | 0 | _ai | |
| 182 | 0 | _ab | |
| 200 | 1 |
_aThe application of machine learning to predictions of optical turbulence in the surface layer at Baikal Astrophysical Observatory _fL. A. Bolbasova, A. A. Andrakhanov, A. Yu. Shikhovtsev |
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| 203 |
_aText _celectronic |
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| 300 | _aTitle screen | ||
| 330 | _aIn this study, we apply machine learning to predict optical turbulence in the surface layer at the Baikal Astrophysical Observatory. Advance knowledge of optical turbulence is important for maximizing the efficiency of adaptive optics systems, telescope operations, and the scheduling of the planned observations. Typically, optical turbulence is characterized by the structure constant of the refractive index of air C2nCn2. The Monin-Obukhov similarity theory (MOST) provides a scientific basis for estimating the structure constant of the refractive index from meteorological variables in the surface layer. However, the MOST becomes unreliable for stable atmospheric conditions, which occurred for more periods regardless of the time of day at the Baikal Astrophysical Observatory. We propose the application of a neural network based on the group method of data handling (GMDH), one of the earliest deep-learning techniques, to predict the surface-layer refractive-index structure constant. The magnitudes of the predicted values of the structure constant of the refractive index and measurements are in agreement. Correlation coefficients ranging from 0.79-0.91 for a stably stratified atmosphere have been obtained. The explicit analytical expression is an advantage of the proposed approach, in contrast to many other machine-learning techniques that have a black-box model. | ||
| 333 | _aРежим доступа: по договору с организацией-держателем ресурса | ||
| 461 | _tMonthly Notices of the Royal Astronomical Society | ||
| 463 |
_tVol 504, iss. 4 _v[P. 6008–6017] _d2021 |
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| 610 | 1 | _aэлектронный ресурс | |
| 610 | 1 | _aтруды учёных ТПУ | |
| 610 | 1 | _aturbulence | |
| 610 | 1 | _aatmospheric effects | |
| 610 | 1 | _ainstrumentation | |
| 610 | 1 | _aadaptive optics | |
| 610 | 1 | _asite testing | |
| 610 | 1 | _atelescopes | |
| 610 | 1 | _aтурбулентность | |
| 610 | 1 | _aатмосферные явления | |
| 610 | 1 | _aадаптивная оптика | |
| 610 | 1 | _aтелескопы | |
| 610 | 1 | _aмашинное обучение | |
| 610 | 1 | _aприземные слои | |
| 610 | 1 | _aастрофизические обсерватории | |
| 700 | 1 |
_aBolbasova _bL. A. _gLidiya Adolfovna |
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| 701 | 1 |
_aAndrakhanov _bA. A. _cSpecialist in the field of electrical engineering _cAssistant of the Department of Tomsk Polytechnic University _f1982- _gAnatoliy Aleksandrovich _2stltpush _3(RuTPU)RU\TPU\pers\38561 |
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| 701 | 1 |
_aShikhovtsev _bA. Yu. _gArtem Yurjevich |
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| 712 | 0 | 2 |
_aНациональный исследовательский Томский политехнический университет _bИнженерная школа информационных технологий и робототехники _bОтделение автоматизации и робототехники _h7952 _2stltpush _3(RuTPU)RU\TPU\col\23553 |
| 801 | 2 |
_aRU _b63413507 _c20210903 _gRCR |
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| 856 | 4 | _uhttps://doi.org/10.1093/mnras/stab953 | |
| 942 | _cCF | ||