Application of the Monte Carlo method to study the alpha particle energy spectra for radioactive aerosol sampled by an air filter

Artificial neural network based isotopic analysis of airborne radioactivity measurement for radiological incident detection

Responders need tools to rapidly detect and identify airborne alpha radioactivity during consequence management scenarios. Traditional continuous air monitoring systems used for this purpose compute the net counts in various energy windows to determine the presence of specified isotopes, such as ²³⁵U, ²³⁹Pu, and ²⁴¹Am. These calculations rely on having a well-calibrated detector, which is challenging in low-background environments. Here, an alternative approach of using artificial neural networks to classify alpha spectra is presented. Two network architectures, fully connected and convolutional neural networks (CNNs), were trained to classify alpha spectra into four categories: background and background plus the three isotopes above. Sources were injected into measured background at various fractions of the derived response level (DRL) corresponding to early-phase Protective Action Guides. The convolutional network identifies all sources at 1% of the DRL with average probability of detection of 95% and false alarm probability of 1%. Further, the network identifies sources ranging between 0.25% and 1% of the DRL with higher than 80% probability of detection and lower than 7% false alarm probability. Most significantly, the network performance improves in low-count background conditions, increasing its minimum probability of detection to 93% and reducing the false alarm probabilities to lower than 0.25%. These results show that, once trained on datasets representing a range of detection scenarios, artificial neural networks can accurately identify alpha isotopes of interest without the need for detector calibration.

Alpha Air Sample Counting Efficiency Versus Dust Loading: Evaluation of a Large Data Set

Dust loading on air sample filters is known to cause a loss of efficiency for direct counting of alpha activity on the filters, but the amount of dust loading and the correction factor needed to account for attenuated alpha particles is difficult to assess. In this paper, correction factors are developed by statistical analysis of a large database of air sample results for a uranium and plutonium processing facility at the Savannah River Site. As is typically the case, dust-loading data is not directly available, but sample volume is found to be a reasonable proxy measure; the amount of dust loading is inferred by a combination of the derived correction factors and a Monte Carlo model. The technique compares the distribution of activity ratios [beta/(beta + alpha)] by volume and applies a range of correction factors on the raw alpha count rate. The best-fit results with this method are compared with MCNP modeling of activity uniformly deposited in the dust and analytical laboratory results of digested filters. A linear fit is proposed to evenly-deposited alpha activity collected on filters with dust loading over a range of about 2 mg cm to 1,000 mg cm.

Production of reference sources of radioactive aerosols in filters for proficiency testing

In the framework of the organization of proficiency testing, filters with deposits of ¹³⁷Cs and ⁹⁰Sr+⁹⁰Y radioactive aerosols have been submitted to laboratories for radionuclide measurement. Procedures for the special preparation and characterization of filters have been developed. The different steps of filter preparation, determination of the deposited radionuclide activity and characterization of the homogeneity of these deposits are presented. This method of filter preparation can also be used in the production of secondary standards, whose properties are more adapted to the needs of laboratories measuring radioactivity in filters than are the solid sources that they typically use.