Enhancing precision of low-cost detectors via machine learning for accurate mass attenuation coefficient estimation

Low-cost NaI(Tl) detectors are practical for gamma-ray spectrometry but their limited energy resolution can introduce systematic errors in mass attenuation coefficient measurements when scattered photons are included in the peak region. This study proposes a machine-learning correction framework that maps NaI(Tl)-derived attenuation coefficients to XCOM-based HPGe-equivalent reference values through a dimensionless correction factor. An EGS4/XCOM dataset of 756 model-development configurations, covering 18 atomic numbers, six monoenergetic photon energies, and seven collimator radii, was used to train twelve regression models and evaluate them over 60 randomized train–validation–test cycles. Sodium and Bismuth were withheld for external material-wise validation. Gaussian Process Regression with a Matérn 5/2 kernel provided the best overall testing performance. For the withheld materials, the correction reduced the mean deviation from XCOM references from 3.07% to 0.83%, supporting its use as a practical correction layer for low-cost attenuation measurements.