Forecasting Source Stability in Scientific Experiments using Temporal Learning Models: A Case Study from Tritium Monitoring
A new study published on arXiv addresses the challenge of monitoring stability in the Karlsruhe Tritium Neutrino Experiment (KATRIN), which aims to measure absolute neutrino mass. Traditional drift detection methods often fail to handle the infrequent and transient instability events in gaseous tritium sources. This research bridges the gap between advanced time-series forecasting and experimental physics by applying deep learning models to predict the time required for source stability after disturbances. The team evaluated multiple architectures, including LSTM, N-BEATS, TFT, and Chronos-LLM, against complex, large-scale experimental data. Key challenges identified include learning from sparse instability events and forecasting long time horizons. The study found that the N-BEATS model offered superior accuracy and repeatability. These predictions provide direct experimental value by enabling more efficient scheduling and maintenance planning during stabilization periods. The findings demonstrate that deep learning can significantly optimize operations in large-scale physics experiments, offering a robust solution for real-time diagnostics via beta-induced X-ray spectroscopy.
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Forecasting Source Stability in Scientific Experiments using Temporal Learning Models: A Case Study from Tritium Monitoring
A new study published on arXiv addresses the challenge of monitoring stability in the Karlsruhe Tritium Neutrino Experiment (KATRIN), which aims to measure absolute neutrino mass. Traditional drift detection methods often fail to handle the infrequent and transient instability events in gaseous tritium sources. This research bridges the gap between advanced time-series forecasting and experimental physics by applying deep learning models to predict the time required for source stability after disturbances. The team evaluated multiple architectures, including LSTM, N-BEATS, TFT, and Chronos-LLM, against complex, large-scale experimental data. Key challenges identified include learning from sparse instability events and forecasting long time horizons. The study found that the N-BEATS model offered superior accuracy and repeatability. These predictions provide direct experimental value by enabling more efficient scheduling and maintenance planning during stabilization periods. The findings demonstrate that deep learning can significantly optimize operations in large-scale physics experiments, offering a robust solution for real-time diagnostics via beta-induced X-ray spectroscopy.
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