# MTL_TX: A Multi-Task Transformer Model for Improved Radiation Time-Series Estimation

## TL;DR

This paper introduces MTL_TX, a transformer-based model that improves radiation dose estimation at JLab using historical sensor data.

## Contribution

The novel MTL_TX model integrates hierarchical feature embedding and multi-level decomposition attention for enhanced radiation time-series estimation.

## Key findings

- MTL_TX achieved an R2 score of 0.8584 on 2018 data and 0.8831 when generalizing to 2016-2019 datasets.
- The model outperformed existing state-of-the-art methods in radiation dose estimation accuracy.

## Abstract

Controlling radiation doses at potential radioactive facilities is critical to ensuring the safety of both personnel and the public. At the Thomas Jefferson National Accelerator Facility (JLab), multiple sensors are deployed around the three experimental halls to monitor key parameters, including single-beam current, energy levels, current leakage, and radiation values during accelerator operations. In this study, we developed a Multi-task Transformer model, MTL_TX, to accurately estimate radiation doses at sensor locations based on historical data, with the aim of enhancing safety in accelerator facilities and surrounding public areas. To improve estimation accuracy, we integrated two innovative components into the proposed model: hierarchical feature embedding (HFE) and multi-level decomposition attention (MDA). Additionally, the multi-task learning (MTL) framework effectively leverages correlations among multiple sensors, enabling individual estimations for each sensor. MTL_TX achieved outstanding results on data collected in 2018, with an MSE of 0.1464, an RMSE of 0.2353, and an R2 score of 0.8584. Furthermore, when trained on 2018 data, MTL_TX exhibited excellent generalization capability to unseen datasets from 2016 to 2019, achieving an MSE of 0.1407, an RMSE of 0.2263, and an R2 score of 0.8831. These results demonstrate a significant improvement over existing state-of-the-art models.