Special Notice NAICS 334516

Machine Learning-Enhanced Spectroscopy Technology for High-Resolution Radiation Detection Using Low-Cost Detectors Transforms low-energy resolution gamma- and x-ray detector data into high-resolution spectra—reducing cost, size, and cooling requirements without sacrificing performance. Technology Summary This INL technology enables high-energy-resolution radiation spectroscopy using low-cost, room-temperature detectors such as sodium iodide (NaI) scintillators. Traditionally, researchers and engineers rely on high-purity germanium (HPGe) detectors, lanthanum bromide (LaBr3) or similar for applications requiring fine energy discrimination; however, these systems are expensive, fragile, or require cryogenic cooling. The presented approach applies a compact convolutional neural network (CNN) architecture to reconstruct high-energy-resolution spectra from low-resolution measurements. Using four convolution-max pooling layer pairs (128–16 filters) followed by dense layers, the model captures spectral features typically only visible with HPGe detectors. The network contains roughly 1.6 million parameters (6.2 MB total), enabling fast, portable deployment in embedded or field devices. The...

Notice ID: a04ad1ce25244c7aaaeb9b01972e0de6 · View on SAM.gov ↗