Statistical properties of an algorithm used for illicit substance detection by fast-neutron transmission

Donald L. Smith; Leonid Sagalovsky; Bradley J. Micklich; M. K. Harper; A. H. Novick

doi:10.1117/12.204181

3 March 1995 Statistical properties of an algorithm used for illicit substance detection by fast-neutron transmission

Donald L. Smith, Leonid Sagalovsky, Bradley J. Micklich, M. K. Harper, A. H. Novick

Proceedings Volume 2339, International Conference on Neutrons and Their Applications; (1995) https://doi.org/10.1117/12.204181
Event: 4th International Conference on Applications of Nuclear Techniques: Neutrons and their Applications, 1994, Crete, Greece

Abstract

A least-squares algorithm developed for analysis of fast-neutron transmission data resulting from non-destructive interrogation of sealed luggage and containers is subjected to a probabilistic interpretation. The approach is to convert knowledge of uncertainties in the derived areal elemental densities, as provided by this algorithm, into probability information that can be used to judge whether an interrogated object is either benign or potentially contains an illicit substance that should be investigated further. Two approaches are considered in this paper. One involves integration of a normalized probability density function associated with the least-squares solution. The other tests this solution against a hypothesis that the interrogated object indeed contains illicit material. This is accomplished by an application of the F-distribution from statistics. These two methods of data interpretation are applied to specific sets of neutron transmission results produced by Monte Carlo simulation.

Citation Download Citation

Donald L. Smith, Leonid Sagalovsky, Bradley J. Micklich, M. K. Harper, and A. H. Novick "Statistical properties of an algorithm used for illicit substance detection by fast-neutron transmission", Proc. SPIE 2339, International Conference on Neutrons and Their Applications, (3 March 1995); https://doi.org/10.1117/12.204181

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available