Funct. Mater. 2020; 27 (1): 179-183.

doi:https://doi.org/10.15407/fm27.01.179

Advanced approach to estimation scintillator energy resolution

A.Gektin1, A.Vasil'ev2, V.Suzdal1, I.Tawrovsky1, A.Sobolev1

1Institute for Scintillation Materials, STC "Institute for Single Crystals", National Academy of Sciences of Ukraine, 60 Nauky Ave., 61001 Kharkiv, Ukraine
2Skobeltsyn Institute of Nuclear Physics of Lomonosov Moscow State University, 1(2) Leninskie Gory Str., 119991 Moscow, Russia

Abstract: 

Digitalization of scintillation pulse data allows to get significantly more information comparing with analogue approach dominated in scintillation technique. Previous investigations with 137Cs source demonstrated the ability to refine the structure of photopeak and significantly improve energy resolution. The present work is devoted to application of new method to multipeak isotope analysis. It is shown that this approach allows to separate data from close located peaks and demonstrate efficiency of this method in wide range of ionizing particle energies from 100 to 1500 keV.

Keywords: 
scintillator, energy resolution, photopeak, pulse digitalization.
References: 
1. P.Dorenbos, J.de Haas, C.W.E.Van Eijk, IEEE Trans. Nucl. Sci., 42, 2190 (1996).
https://doi.org/10.1109/23.489415
 
2. K.Yang, P.Menge, J.Appl. Phys., 118, 213106 (2015).
https://doi.org/10.1063/1.4937126
 
3. V.Khodyuk, S.A.Messina, T.J.Hayden et al., J. Appl. Phys., 118, 084901 (2015).
https://doi.org/10.1063/1.4928771
 
4. V.V.Nagarkar, S.C.Thacker, V.Gaysinskiy et al., IEEE Trans Nucl. Sci., 1, 565 (2009).
https://doi.org/10.1109/TNS.2009.2016198
 
5. A.V.Gektin, B.G.Zaslavsky, Halogenide Scintillators: Crystal Growth and Performance. in: Crystal Growth Technology, ed. by H.Scheel and T.Fukuda, Willey (2003), p.511.
https://doi.org/10.1002/0470871687.ch23
 
6. A.Gektin, A.Vasil'ev, V.Suzdal, A.Sobolev. Energy Resolution of Scintillators in Connection with Track Structure, INT19 (2019). Abstract.
https://doi.org/10.1109/TNS.2020.2978236
 
7. A.Gektin, A.Vasil'ev, V.Suzdal, A.Sobolev, IEEE NSS-MIC, Manchester UK (2019), Conference Abstract.
 
8. A.Gektin, A.Vasil'ev, in: Springer Proceed. Phys., v.227, ed. by M.Korzhik and A.Gektin, Springer Nature Switzerland AG (2019), p.29.
 
9. A.N.Vasil'ev, in: Springer Proceed. Phys., v.200, ed. by A.Gektin, M.Korzhik, Springer Intern. Publishing, Berlin (2017), p.3.
 
10. A.V.Gektin, A.N.Vasil'ev, Functional Materials, 24, 62 (2017).
https://doi.org/10.15407/fm24.04.621
 
11. A.Gektin, A.Vasil'ev, Radiat. Meas., 122, 108 (2019).
https://doi.org/10.1016/j.radmeas.2019.02.004
 
12. Glenn F.Knoll, Radiationa Detection and Measurements, 4th ed., ed. John Willey, NY (2010).
 
13. A.Gektin, V.Suzdal, A.Boyarintsev, A.Sobolev, Functional Materials, 26, 127 (2019).
https://doi.org/10.15407/fm26.01.127
 
14. Kantardzic Mehmed, Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley & Sons (2003).
 
15. Han Kamber, Pei Jaiwei, Micheline Jian, Data Mining: Concepts and Techniques, 3rd ed., Morgan Kaufmann (2011).
 
16. Witten Ian H., Frank Eibe, Hall Mark A. Data Mining: Practical Machine Learning Tools and Techniques, 3 ed., Elsevier, Amsterdam (2011).
https://doi.org/10.1016/B978-0-12-374856-0.00001-8
 
17. Robert Layton, Learning Data Mining with Python, 2 ed., Packt Publishing (2015).
 
18. J.MacQueen. in: Proc. of the Fifth Berkeley Symposium on Mathematics, Statistics and Probability, v.1, (1967), p.281.
 
19. L.Kaufman, In Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York (1990).
https://doi.org/10.1002/9780470316801
 
20. R.O.Duda, P.E.Hart, D.G.Stork, Pattern Classification, Wiley, New York (2001).
 

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