Astronomical Seeing Prediction with Machine Learning
Year: 2025
Type: article
Abstract: We investigate the use of supervised machine learning models to predict current atmospheric seeing at the Observatorio del Roque de Los Muchachos, La Palma. The prediction is made from current, local meteorological conditions as measured by an on-site weather station. The model was trained over a six-month period from 2024 February 8 to 2024 July 3. We found that a simple random forest method provides better prediction than an multilayer perceptron neural network. The most important predictor is the maximum wind speed over a one-minute interval (0.23), followed by air pressure (0.18), wind speed standard deviation (0.17), relative humidity (0.16), air temperature (0.13), wind direction (0.10) and average wind speed (0.06). All other features had importance <0.05.
Source: Research Notes of the AAS
Authors: Fernando Abárzuza, Iain A Steele, Helen Jermak, Joao Bento, David Copley, David Heffernan
Institution: Liverpool John Moores University
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Award: G7463224461
OpenAlex ID: W4407184874