Data-driven Seeing Prediction for Optics Telescope: from Statistical Modeling, Machine Learning to Deep Learning Techniques

Abstract

Predicting seeing of astronomical observations can provide hints of the quality of optical imaging in the near future, and facilitate flexible scheduling of observation tasks to maximize the use of astronomical observatories. Traditional approaches to seeing prediction mostly rely on regional weather models to capture the in-dome optical turbulence patterns. Thanks to the developing of data gathering and aggregation facilities of astronomical observatories in recent years, data-driven approaches are becoming increasingly feasible and attractive to predict astronomical seeing. This paper systematically investigates data-driven approaches to seeing prediction by leveraging various big data techniques, from traditional statistical modeling, machine learning to new emerging deep learning methods, on the monitoring data of the Large sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST). The raw monitoring data are preprocessed to allow for big data modeling. Then we formulate the seeing prediction task under each type of modeling framework and develop seeing prediction models through using representative big data techniques, including ARIMA and Prophet for statistical modeling, MLP and XGBoost for machine learning, and LSTM, GRU and Transformer for deep learning. We perform empirical studies on the developed models with a variety of feature configurations, yielding notable insights into the applicability of big data techniques to the seeing prediction task.