preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202312.0637.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 7 December 2023 / Approved: 8 December 2023 / Online: 8 December 2023 (15:05:56 CET)

In the present article, we study the possibilities of machine learning for estimation of seeing at the Maidanak Astronomical Observatory (38∘40′24′′N, 66∘53′47′′E) using only Era-5 re-analysis data. Seeing is usually associated with the integral of the turbulence strength Cn2(z) over the height z. Based on the seeing measurements accumulated over 13 years we created ensemble models of multi-layer neural networks under the machine learning framework, including training and validation. For the first time in the world, we have simulated optical turbulence (seeing variations) during night-time with deep neural networks trained on a 13-year database of astronomical seeing. A set of neural networks for simulations of night-time seeing variations have been obtained. For these neural networks the linear correlation coefficient ranges from 0.48 to 0.68. We show that modeled seeing with neural networks is well described through meteorological parameters, which include wind speed components, air temperature, humidity and turbulent surface stresses. One of the fundamental new results is that the structure of small-scale (optical) turbulence over the Maidanak Astronomical Observatory does not depend or depends negligibly on the vortex component of atmospheric flows.

turbulence; optical turbulence; seeing; telescope; neural network

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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