Developing DRF for 1 X1 LaBr 3 Detector. Sangkyu Lee

Transcription

1 Developing DRF for 1 X1 LaBr 3 Detector Sangkyu Lee

2 Detector Response Function Function: a function is a relation between a set of inputs and a set of permissible outputs with the property that each input is related to one output. Simulation Result (MCNP) DRF (Detector Response Function) 0 R = de dvr(r, E)Φ(r, E) V DRF R(r, E) Similar Result With Experiment R(r, E) is a Detector Response Function.

3 Why do we need DRF? Different Result between simulation and experiment How can I compare between simulation and experiment? Experiment - Simulation Full energy peak is not enough to compare. There are Compton scatter, Compton edge, escape peaks, backscatter, annihilation peak and sum peak other than full energy peak.

4 Components of DRF Energy resolution: Gaussian broadening effect Gaussian function : f E = Ce (E E 0 A )2 E = Broadened energy E 0 = Unbroadened energy A = Gaussian width = FWHM 2 ln2 C = Normalized constant Detector efficiency (Intrinsic efficiency) ε int = detector count rate rate of particles entering detector Background

5 Method to develop DRF Gamma spectroscopy experiment (with 1 X1 LaBr 3 detector and various sources) MCNP simulation with same setups of experiment Find Gaussian fitting curve equations at different energy (Fitting program) Writing code for DRF

6 Goal for this project Understand all events inside of the detector Improve fitting and code writing technic Compare between simulation results and experiment results for every gamma interactions Accurate and practical spectrum analysis

7 Experiment setup 1 X1 LaBr3 Detector Source position Ba133(keV) Cs137(keV) Eu152(MeV)

8 Energy calibration Channel Original First order Second order (kev) (kev) (kev)

9 count rate (CPS) count rate (CPS) count rate (CPS) Measured spectra count rate (CPS) Background kev Ba kev Cs kev Am241+Eu kev

10 Gaussian fitting 1/ Ba kev Ba kev fit_wave1= W_coef[0]+W_coef[1]*exp(-((x-W_coef[2])/W_coef[3])^2) W_coef={1.3028,11.643,80.869,4.8246} V_chisq= ;V_npnts= 15;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 14; W_sigma={0.168,0.27,0.0818,0.152} Coefficient values ± one standard deviation y0 =1.3028?0.168 A =11.643?0.27 x0 =80.869? width =4.8246?0.152 fit_wave3= W_coef[0]+W_coef[1]*exp(-((x-W_coef[2])/W_coef[3])^2) W_coef={ ,1.4176,302.97,7.9149} V_chisq= ;V_npnts= 15;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 14; W_sigma={0.0365,0.0341,0.0748,0.258} Coefficient values ± one standard deviation y0 = ? A =1.4176? x0 =302.97? width =7.9149?0.258

11 Gaussian fitting 2/ Ba kev 80 Cs kev fit_wave5= W_coef[0]+W_coef[1]*exp(-((x-W_coef[2])/W_coef[3])^2) W_coef={ ,3.6111,355.9,8.5697} V_chisq= ;V_npnts= 15;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 14; W_sigma={0.06,0.0552,0.0412,0.165} Coefficient values ± one standard deviation y0 = ?0.06 A =3.6111? x0 =355.9? width =8.5697?0.165 fit_wave7= W_coef[0]+W_coef[1]*exp(-((x-W_coef[2])/W_coef[3])^2) W_coef={ ,98.552,662.29,11.871} V_chisq= ;V_npnts= 19;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 18; W_sigma={0.475,0.435,0.012,0.0597} Coefficient values ± one standard deviation y0 = ?0.475 A =98.552?0.435 x0 =662.29?0.012 width =11.871?0.0597

12 Gaussian fitting 3/ Eu kev fit_wave9= W_coef[0]+W_coef[1]*exp(-((x-W_coef[2])/W_coef[3])^2) W_coef={ ,2.034,1407.2,18.779} V_chisq= ;V_npnts= 23;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 22; W_sigma={0.102,0.0972,0.0714,0.758} Coefficient values ± one standard deviation y0 = ?0.102 A =2.034? x0 =1407.2? width =18.779?0.758

13 Energy resolution for LaBr 3 x *CurveFit/M=2/W=0 Power, wave3/x=wave0/d Fit converged properly fit_wave3= W_coef[0]+W_coef[1]*x^W_coef[2] W_coef={ ,2.7356, } V_chisq= e-008;V_npnts= 5;V_numNaNs= 0;V_numINFs= 0; V_startRow= 0;V_endRow= 4; W_sigma={ ,0.107,0.0099} Coefficient values ± one standard deviation y0 = ? A =2.7356?0.107 pow = ? Resolution function The resolution function was expressed by a power law relation (Berger and Seltzer, 1972): r E r E = FWHM E = a E b r E = E

14 LaBr 3 Detector MCNP6 Simulation

15 Differences between Monte Carlo modeling and experiment Experiment result MCNP6 simulation Intensity 2. Gaussian broadening(energy resolution) 3. Coincidence counting(sum effect) 4. Background

16 Algorithm for DRF Input (Simulation Result) Gaussian Broadening Check the parameters Add Coincidence Counting Multiply by Intensity Add Background Bad Check the reduced Chi-square with experiment spectrum Good Output

17 Cs-137 decay mode

18 Count rate(n/s) DRF result Backscatter peak 100 Cs-137 peak(661.7kev) 10 Sum peak(1323.4kev) - Experiment - DRF K-40 (background) Energy(keV)

19 References [1] Glenn F. Knoll, Radiation Detection and Measurement, John Wiley and Sons, Fourth Edition, [2] Truong Thi Hong Loan, Study on the HPGe Detector Response Function by the Monte Carlo Method with Using MCNP Code, Doctoral special subject report, UNS-VNU-HCMC, [3] Gardner R. P. and Sood A. (2004) : A Monte Carlo simulation approach for generating NaI detector response functions (DRFs) that accounts for non-linearity and variable flat continua. Nucl. Instr. and Meth. B 213, [4] Response Function of a 3 3 in. NaI Scintillation Detector in the range of to MeV Hashem Miri Hakimabad, Hamed Panjeh* and Alireza Vejdani-Noghreiyan, Physics Department, Faculty of Science, Ferdowsi University of Mashhad, Mashhad, Iran [5] CALIBRATION OF THE HIGH AND LOW RESOLUTION GAMMA-RAY SPECTROMETERS* AURELIAN LUCA, BEATRIS NEACSU, ANDREI ANTOHE, MARIA SAHAGIA Horia Hulubei National Institute for Physics and Nuclear Engineering, IFIN-HH Bucharest, P.O. Box MG-6, RO Bucharest-Magurele, Romania, beatris.neacsu@gmail.com Received March 22, 2912

20 Questions?