Abstract
Physics’ rare event investigation, like the dark matter direct detection or the neutrinoless double beta decay research, is typically carried-out in low background facilities like the underground laboratories. Radon-222 (\(^{222}Rn\)) is a radionuclide that can be emitted by the uranium decay in the rock, thus the monitoring and the prediction of Rn contamination in the air of the laboratory is a key aspect to minimize the impact of this source of background. In the past, deep learning algorithms have been used to forecast the radon level, however, due to the noisy behavior of the \(^{222}Rn\) data, it is very difficult to generate high-quality predictions of this time series. In this work, the meteorological information concurrent to the radon time series from four distant places has been considered—nowcasting technique—in order to improve the forecasting of \(^{222}Rn\) in the Canfranc Underground Laboratory (Spain). With this work, we demonstrated and quantified the improvement in the prediction capability of a deep learning algorithm using nowcasting techniques.
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Notes
- 1.
The number of weeks ahead evaluated ranges from 2 week to 8 weeks (only pair numbers of week). This range allows quick scheduling for short unshielding periods, to intensive maintenance operations taking up to 8 weeks. Consultations with experiments managers indicate that these periods are adequate for planning different types of operations over the experiments.
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Acknowledgment
Authors wish to express their thanks for the data support and sharing to Canfranc Underground Laboratory and, particularly to Iulian C. Bandac. TSP is co-funded in a 91.89% by the European Social Fund within the Youth Employment Operating Program, as well as the Youth Employment Initiative (YEI), and co-found in a 8,11 by the “Comunidad de Madrid (Regional Government of Madrid)” through the project PEJ-2018-AI/TIC-10290. MCM is funded by the Spanish Ministry of Economy and Competitiveness (MINECO) for funding support through the grant “Unidad de Excelencia María de Maeztu”: CIEMAT - FÍSICA DE PARTÍCULAS through the grant MDM-2015-0509.
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Sánchez-Pastor, T., Cárdenas-Montes, M. (2021). Nowcasting for Improving Radon-222 Forecasting at Canfranc Underground Laboratory. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2021. Lecture Notes in Computer Science(), vol 12886. Springer, Cham. https://doi.org/10.1007/978-3-030-86271-8_41
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