Modelling and Forecasting of the $$^{222}Rn$$ Radiation Level Time Series at the Canfranc Underground Laboratory

Abstract

The \(^{222}Rn\) level at underground laboratories, where Physics experiments of low-background are installed, is the largest source of background; and it is the main distortion for obtaining high accuracy results. At Spain, the Canfranc Underground Laboratory hosts ground-breaking experiments, such as Argon Dark Matter-1t aimed at the dark matter direct searches. For the collaborations exploiting these experiments, the modelling and forecasting of the \(^{222}Rn\) level are very relevant tasks for efficient planning activities of installation and maintenance. In this paper, four years of values of \(^{222}Rn\) level from the Canfranc Underground Laboratory are analysed using methods such as Holt-Winters, AutoRegressive Integrated Moving Averages, Seasonal and Trend Decomposition using Loess, Feed-Forward Neural Networks, and Convolutional Neural Networks. In order to evaluate the performance of these methods, both the Mean Squared Error and the Mean Absolute Error are used. Both metrics determine that the Seasonal and Trend Decomposition using Loess no periodic, and the Convolutional Neural Networks, are the techniques which obtain the best predictive results. This is the first time that the mentioned data are investigated, and it constitutes an excellent example of scientific time series with relevant implications for the quality of the scientific results of the experiments.