- Country: USA
- Summary: Available for Licensing Machine LearningEnhanced Spectroscopy Technology for HighResolution Radiation Detection Using LowCost Detectors
- UST Ref No: 129231200
- Deadline: 01 Dec 2025
- Financier: Self Financed
- Purchaser Ownership: Government
- Tender Value: Refer Document
- Notice Type: Tender
- Document Ref. No.: BA-1346
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- Description:
- 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 technology offers a new analytical pathway for radiation spectroscopy—maintaining data fidelity while reducing total system cost, weight, and operational complexity.
Problem Addressed
High cost and complexity of high-energy-resolution detectors: HPGe systems p... - Documents:
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CLICK HERE TO LOGINAvailable for Licensing Machine LearningEnhanced Spectroscopy Technology for HighResolution Radiation Detection Using LowCost Detectors - USA Tender
The ENERGY, DEPARTMENT OF, a Government sector organization in USA, has announced a new tender for Available for Licensing Machine LearningEnhanced Spectroscopy Technology for HighResolution Radiation Detection Using LowCost Detectors. This tender is published on USARFP under UST Ref No: 129231200 and is categorized as a Tender. Interested and eligible suppliers are invited to participate by reviewing the tender documents and submitting their bids before the deadline on 2025-12-01.
The estimated tender value is Refer Document, and full details, including technical specifications and submission requirements, are provided in the official tender documents. Ensure all submissions meet the criteria outlined to be considered for evaluation.
