Cognitive R-Tree for Stabilizing Temperature and Load Induced Gain Shifts of Scintillation Detectors 1,3
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Elmar Jacobs , Christian Henke , Frank Lueck , Norbert Link and Marcus J. Neuer 1
innoRIID GmbH, Am Eichenbroich 19, 41516 Grevenbroich, Germany, 2 VDEh.-Betriebsforschungsinstitut, Sohnstr. 65, 40237 Duesseldorf, Germany, 3 smartoptics GmbH., Lise-Meitner Allee 10, 44801 Bochum, Germany
Introduction
Context: Stabilisation of radio-isotope identification devices (RIIDs) against temperature and load shift effects
Problem 1. Peak width and position are temperature and load dependent [1, 2]. Both are important inputs for nuclide identification algorithms.
Problem 2. Users apply the instrument under challenging conditions.
Solution: Combine a R-Tree [3] index based learning technique on time series data [4] with the established cognitive filtering [5, 6] I Continuous assessment of signal shape and filter coefficients I Storage in self-learning R-Tree index object [7] even after delivering the product to the customer I Reliable control of peak position and width
Figure 2: Measurement of 22Na source in a climate chamber, ramping from −20◦C to 55◦C. Two peaks can be used to calculate a correction factor: the 511keV and the 1274keV gamma peak.
Figure 3: Temperature curve part for CeBr3 stabilisation. κT (T, /dt) is a relative correction factor for the gain g. Color and vectors indicate temperature and temperature gradient.
Cognitive filter Results Digital filter implemented at the firmware level of a MCA, presented in [5, 6] Allows to extract scintillation decay times τi from the signal shape Retrieves the temperature T , current load χ and optimised filter coefficients bi for the a given signal
Easy retrieval of stabilisation information from R-Tree (see Fig. 4)
Stabilisation of peak position within ±0.2% (see Fig. 5)
Stabilisation of peak position against load shift within ±0.4%@100kcps/1s
Automatic update of R-Tree after delivery of instruments deployed to first customers for prototype testing
R-Tree indexing
Tree index structure, that uses multi-dimensional hypercubes for organising the data it contains
Indexes the gain g according to the 4-dimensional data belonging to the physical conditions of the measurement: temperature T , temperature gradient dT /dt, load gradient dN/dt and the conditional probability of success P (g|s)
Instances that can insert entries in the tree (see Fig. 3) are
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Training mode during production, s = 1
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User triggered re-calibration of instrument with known source s = 1
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Nuclide identification triggered re-calibration of instrument s = P (Source) knowing the source only with a certain probability
Figure 4: Extraction of gain correction data from R-Tree, splitted into positive (red) and negative (blue) temperature gradient parts.
Conclusion and outlook
Figure 5: Stabilisation quality of the peak position for the NaI detector, black dots = peak position without stabilisation, colored dots = peak positions during warming (red) and cool down (blue) with activated R-Tree stabilisation.
Potential of machine learning algorithms and optimisation concepts have not been fully exploited in the past Sophisticated methodologies from artificial intelligence yield novel possibilities in our field of radiation detection Investigate other approaches from the field of time-series mining [8] for spectroscopic analysis
dT dN g T, , dt dt
References
Testing
Figure 1: Illustration of the rectangles of a R-Tree. 00
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Cerium Brodmide detector CeBr3 with 1.5 × 1.5 and sodium iodide detector NaI:Tl with 300 × 0.7500 mounted on innoRIID MCA unit Further equipment: Binder industrial grade climate chamber, Raspberry Pi 2 for automatised data acquisition Programming languages: Python (using Numpy+Pandas) & Julia for prototyping and R-Tree design, C/C++ for cognitive filter implementation on firmware layer
[1] G. Pausch and J. Stein, “Stabilizing scintillation detector systems by exploiting the temperature dependence of the light pulse decay time,” IEEE Trans. Nucl. Sci., vol. 52, no. 5, pp. 1849–1855, 2005. [2] G. Pausch, K. Saucke, J. Stein, H.-G. Ortlepp, and P. Schotanus, “Stabilizing scintillation detector systems with pulsed leds: A method to derive the led temperature from pulse height spectra,” IEEE Trans. Nucl. Sci., vol. 52, no. 6, pp. 3160–3165, 2005. [3] A. Guttmann, “R-trees: A dynamic index structure for spatial searching,” Proc. ACM SIGMOD International Conf. on Management of Data, 1984. [4] E. Keogh, S. Lonardi, and B. Y.-c. Chiu, “Finding surprising patterns in a time series database in linear time and space,” in Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’02, (New York, NY, USA), pp. 550–556, ACM, 2002. [5] M. J. Neuer and E. Jacobs, “A cognitive filter to automatically determine the scintillation detector material and to control their spectroscopic resolution during temperature changes,” IEEE Trans. Nucl. Sci., vol. 61, June 2014. [6] E. Jacobs, C. Henke, and M. J. Neuer, “A cognitive filter to stabilize peak positions and widths of a scintillation detector and to determine its material,” IEEE Nuclear Science Symposium, Seattle, November 2014. [7] R. Jin and G. Agrawal, “Frequent pattern mining in data streams,” in Data Streams (C. Aggarwal, ed.), vol. 31 of Advances in Database Systems, pp. 61–84, Springer US, 2007. [8] R. Povinelli, “Identifying temporal patterns for characterization and prediction of financial time series events,” in Temporal, Spatial, and Spatio-Temporal Data Mining (J. Roddick and K. Hornsby, eds.), vol. 2007 of Lecture Notes in Computer Science, pp. 46–61, Springer Berlin Heidelberg, 2001.
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http://www.innoriid.com
corresponding author:
[email protected]
