1. A Mobile System for Detecting Gamma-Radiation Sources Part I. Physical and Statistical Background Physical and Statistical Background

2. Dept of Nuclear Engineering, University of Sofia, Bulgaria Ludmil Tsankov Dept of Nuclear Engineering, University of Sofia, Bulgaria ludmil@phys.uni-sofia.bg ludmil@phys.uni-sofia.bg Dept of Electronic Technique, Technical University of Sofia, Bulgaria Mityo Mitev Dept of Electronic Technique, Technical University of Sofia, Bulgaria mitev@ecad.tu-sofia.bg mitev@ecad.tu-sofia.bg Tchavdar Lenev Institute for Nuclear Research and Nuclear Energy, BAS, Sofia, Bulgaria lenev@inrne.bas.bg

3. Introduction Two general methods are used by the national nuclear safety services to recover out-of-control ('orphan') gamma-radiation sources: o o passive ('trapping‘) methods - to set up large high sensitive stationary detectors at the borders, at the entrances of the scrap recycling facilities and at other potentially suspicious sites; o o active ('hunting') methods - to use mobile equipment able to discover signals from the sources during a survey made either by car (CGS) or by aircraft (AGS).

4. Purpose   To perform a theoretical analysis of a CGS system in order to express the minimum detectable activity as a function of its basic parameters   To develop algorithms for real time data processing in order to come near to the theoretical limits

5. Statement of the problem Detector D has effective sensitive area S and moves across a point source s with a constant activity A. Assumptions: the effective area of the detector S does not depend on the source- detector disposition; the detector moves uniformly with respect to the source at a velocity V; the absorption of the gamma-rays emitted from the source in the air is negligible.

6. Integral counting - radiation flux density - flux Signal-Noise Ratio The background is estimated from preceding measurements and extrapolated:

7. Optimal system R(  ) has always a maximum V=10m/s A=3.7.10 7 Bq (1mCi), S=0.0058m (3"x3"), d=10m, B 0 =800cps,  {B 0 }=1.63cps (t BG =300s)

8. Nearly-optimal system Optimal system Nearly-optimal system for all R(  ) depends on V:

9. Adaptive algorithms for data analysis 1. Two samples are created: Background (BS) Signal (SS) 2. New data are first regarded as a signal 3. While the process is stationary, BS is extended

10. Spectral registration of gamma-quanta   NaI(Tl) detectors have a good energy resolution which can be used to improve sensitivity.   Statistical hypothesis is changed: now we have to compare not two numbers but two probabilities distributions:

11. Possible errors in decisions based on statistical tests   Type I (false alarm)   Type II (to bypass a source) Both types of errors have to be minimised simultaneously Relationship between Type I and Type II errors depends on the choice of the significancy level (SNR threshold value) Repeated test at yields a higher sensitivity than a single test at at the cost of 1s reaction delay.

12. Conclusion   Spectrometric registration mode is more complex, but yields a higher sensitivity   The problem for the optimal setting of the alarm threshold level is consistently deduced from the theory of statistical hypotheses testing

13. Acknowledgements This work is supported by the Bulgarian Nuclear Safety Authority under contract No 286-02.

14. To be continued … A Mobile System for Detecting Gamma- Radiation Sources: PART II. Design and First Experiments ( At the poster session)