The Interplay Between the Statistical Correlations of g-ray Emission Probabilities and Efficiency Calibration * Ruy M. Castro 1,2, Vito R. Vanin 3, Paulo.

Slides:



Advertisements
Similar presentations
1 Regression as Moment Structure. 2 Regression Equation Y =  X + v Observable Variables Y z = X Moment matrix  YY  YX  =  YX  XX Moment structure.
Advertisements

Activity measurement of phosphorus-32 in the presence of pure beta-emitting impurities The CSIR Research and Innovation Conference National Metrology Laboratory.
 -Ray Emission Probabilities Edgardo Browne Decay Data Evaluation Project Workshop May 12 – 14, 2008 Bucharest, Romania.
6-1 Introduction To Empirical Models 6-1 Introduction To Empirical Models.
Brookhaven Science Associates U.S. Department of Energy ENSDF Analysis and Utility Codes Presentation for the ICTP-IAEA Workshop on Nuclear Structure and.
P M V Subbarao Professor Mechanical Engineering Department
Simple Linear Regression
A Short Introduction to Curve Fitting and Regression by Brad Morantz
Evaluation the relative emission probabilities for 56 Co and 66 Ga Yu Weixiang Lu Hanlin Huang Xiaolong China Nuclear Data Center China Institute of Atomic.
The Multiple Regression Model Prepared by Vera Tabakova, East Carolina University.
G. Cowan Lectures on Statistical Data Analysis 1 Statistical Data Analysis: Lecture 10 1Probability, Bayes’ theorem, random variables, pdfs 2Functions.
Error Propagation. Uncertainty Uncertainty reflects the knowledge that a measured value is related to the mean. Probable error is the range from the mean.
Introduction Radioactive nuclei decay in numerous ways: emitting electrons, protons, neutrons, alpha particles, gamma rays, x-rays, or some combination.
Statistics.
Calibration & Curve Fitting
Regression Analysis Regression analysis is a statistical technique that is very useful for exploring the relationships between two or more variables (one.
GEO7600 Inverse Theory 09 Sep 2008 Inverse Theory: Goals are to (1) Solve for parameters from observational data; (2) Know something about the range of.
880.P20 Winter 2006 Richard Kass Propagation of Errors Suppose we measure the branching fraction BR(Higgs  +  - ) using the number of produced Higgs.
Decay Data in View of Complex Applications Octavian Sima Physics Department, University of Bucharest Decay Data Evaluation Project Workshop May 12 – 14,
Gamma-ray efficiency of a HPGe detector as a function of energy and geometry of energy and geometry Mohsen B. Challan Physics Department, Faculty of Education.
1 Lecture 5 Overview on the Analytical Procedures (  ) Lecture 5 Overview on the Analytical Procedures (  ) Moncef Benmansour Moncef Benmansour CNESTEN,
Accurate gamma-ray spectrometry of environmental samples: a challenge O. Sima - Bucharest University D. Arnold - PTB Braunschweig C. Dovlete - ERL Bucharest.
On the question about energy of 3.5 eV state of 229 Th S.L. Sakharov Petersburg Nuclear Physics Institute, Gatchina, Russia.
3/2003 Rev 1 II.3.15b – slide 1 of 19 IAEA Post Graduate Educational Course Radiation Protection and Safe Use of Radiation Sources Part IIQuantities and.
Problem: 1) Show that is a set of sufficient statistics 2) Being location and scale parameters, take as (improper) prior and show that inferences on ……
Geology 5670/6670 Inverse Theory 21 Jan 2015 © A.R. Lowry 2015 Read for Fri 23 Jan: Menke Ch 3 (39-68) Last time: Ordinary Least Squares Inversion Ordinary.
–The shortest distance is the one that crosses at 90° the vector u Statistical Inference on correlation and regression.
E. Browne & J. Tuli USNDP Annual Meeting November 7-9, 2006 Absolute and Relative  -Ray Intensities in ENSDF E. Browne # and J.K. Tuli* National Nuclear.
Determination of activity of 51 Cr source on gamma radiation measurements V.V.Gorbachev, V.N.Gavrin, T.V.Ibragimova, A.V.Kalikhov, Yu.M.Malyshkin,A.A.Shikhin.
Simulation of HPGe detector efficiency Eunkyung Lee Ewha Womans University.
Math 4030 Final Exam Review. Probability (Continuous) Definition of pdf (axioms, finding k) Cdf and probability (integration) Mean and variance (short-cut.
Lecture 1: Basic Statistical Tools. A random variable (RV) = outcome (realization) not a set value, but rather drawn from some probability distribution.
Week 21 Order Statistics The order statistics of a set of random variables X 1, X 2,…, X n are the same random variables arranged in increasing order.
Week 21 Statistical Assumptions for SLR  Recall, the simple linear regression model is Y i = β 0 + β 1 X i + ε i where i = 1, …, n.  The assumptions.
Week 21 Statistical Model A statistical model for some data is a set of distributions, one of which corresponds to the true unknown distribution that produced.
Kriging - Introduction Method invented in the 1950s by South African geologist Daniel Krige (1919-) for predicting distribution of minerals. Became very.
Topical Lectures Fitting, Tracking and Vertexing NIKHEF, Feb Wouter Hulsbergen (CERN)
Jag Tuli DDP-Workshop Bucharest, Romania, May 08 Jagdish K. Tuli NNDC Brookhaven National Laboratory Upton, NY 11973, USA Decay Scheme Normalization.
Estimating standard error using bootstrap
Data Modeling Patrice Koehl Department of Biological Sciences
Investigation of the proton-induced reactions on natural molybdenum.
Multilevel modelling: general ideas and uses
FRCR II - Radioactivity
Practical Statistics for Physicists
M.I. Abbas - Alexandria University - EGYPT.
Statistical Quality Control, 7th Edition by Douglas C. Montgomery.
Correlation and Regression Analysis
B Tagging Efficiency and Mistag Rate Measurement in ATLAS
Fundamentals of regression analysis
Sampling Distributions
How precisely do we know the antineutrino source spectrum from a nuclear reactor? Klaus Schreckenbach (TU München) Klaus Schreckenbach.
بحث في التحليل الاحصائي SPSS بعنوان :
Nuclear Medicine Physics & Instrumentation I
The experiment on JINR Dubna Nuclotron
Multiple Regression Models
Analysis of a gamma spectrum: -Identification and evaluation Henrik Ramebäck (FOI) Trygve Bjerk (IFE)
Nuclear Data for Reactor Fluxes
5.4 General Linear Least-Squares
Simple Linear Regression
Simple Linear Regression
Problematic Yttrium reaction 25/02/2019
Product moment correlation
Fu Jinghua Institute of Particle Physics, Huazhong Normal University
1. Alpha decay of radium-226 with gamma emission
Regression and Correlation of Data
Radiopharmaceutical Production
Particle Emission Probabilities Edgardo Browne Decay Data Evaluation Project Workshop May 12 – 14, 2008 Bucharest, Romania.
Radioactivity – inverse square law, absorption, and rates
ATLAS full run-2 luminosity combination
Radioactivity – review of laboratory results
Presentation transcript:

The Interplay Between the Statistical Correlations of g-ray Emission Probabilities and Efficiency Calibration * Ruy M. Castro 1,2, Vito R. Vanin 3, Paulo R. Pascholati 3, Nora L. Maidana 3 1 IEAv - CTA, São José dos Campos, Brasil 2 DMAF - UNITAU, Taubaté, Brasil 3 LAL - IFUSP, São Paulo, Brasil * Presented in: 14th International Conference on Radionuclide Metrology and its Applications ICRM2003 - Dublin, Ireland Applied Radiation and Isotopes 60 (2004) 185-190 http://dx.doi.org/10.1016/j.apradiso.2003.11.014

Contents Are the data correlated? Efficiency Calibration Example Decay parameters

Are the data correlated? Analyzing the 152Eu published data ~ 32 experiments with the most intense transitions REF Relative Pg (P1408keV = 100 %) 121 keV 244 keV 344 keV 411 keV 1 145,0 ± 4,1 39,4 ± 1,3 128,2 ± 3,6 10,14 ± 0,54 2 138,5 ± 6,4 36,2 ± 1,8 ± 5,9 10,32 ± 0,51 3 132,9 ± 4,0 35,8 ± 1,0 ± 3,8 10,77 ± 0,38 4 144,6 ± 4,7 36,4 ± 1,2 ± 4,2 10,59 ± 0,27 5 141,0 36,6 ± 1,1 127,2 10,71 ± 0,11 6 140,6 ± 2,8 ± 0,6 ± 2,6 10,55 ± 0,22 7 136,9 ± 0,3 127,1 ± 0,7 10,84 ± 0,07 8 136,7 36,5 ± 0,4 126,9 ± 0,9 10,73 ± 0,10

Are the data correlated?

Are the data correlated?

Are the data correlated? Some other cases:

Are the data correlated? Efficiency e - Efficiency Area - Peak area F - Factor Activity Normalization Constant Reference area Pg - Emission probability Usually: Area, F and P are independent Area is uncorrelated Pg are correlated for a multi gamma source, but their correlation coefficients are generally unknown

Efficiency Calibration Typical efficiency curve For energies above 150-200 keV Eg – Gamma Energy Eb – Reference Energy

Efficiency Calibration For example: Using a Matrix notation:

Efficiency Calibration Fitting Procedure Problem: How to obtain V? What is V? Variance in Y due to: peak area, factor and emission probability

Efficiency Calibration Variance in Y due to Area: VY-Area

Efficiency Calibration Variance in Y due to F: VY-F

Efficiency Calibration Variance in Y due to Pg: VY-Pg

Efficiency Calibration Fitting Procedure Quality of the fitting: Interpolation:

Example Typical efficiency calibration Relative efficiency 1 multi gamma-ray source - 152Eu (partially know covariance) 2 105 Bq Activity 20 cm source-detector distance 2 h of measurement (live time) Pile-up rejection and dead time correction Relative efficiency

Example Efficiency curve

Example Calibration Data Energy Ig Correlation Matrix (keV) 344 411 778 1089 1299 0,26689(13) 1 0,10 0,32 0,15 -0,07 0,02229(3) 0,31 0,72 -0,08 779 0,1303(8) 0,45 -0,20 0,01712(3) -0,12 0,01612(8) Energy Ig Correlation Matrix (keV) 121 244 4445-2 44415-9 867 964 1085 1112 1212 1408 0,2875(6) 1 -0,68 -0,70 -0,69 -0,75 -1 -0,65 -0,62 -0,53 0,0773(9) 0,76 0,62 0,69 0,68 0,59 0,52 0,00316(4) 0,74 0,75 0,0263(3) 0,73 0,58 0,0422(5) 0,61 0,57 0,49 0,1448(16) 0,66 0,53 0,1004(12) 0,64 0,1339(17) 0,55 0,01432(20) 0,51 0,208(3)

Example Variance Matrix Variance Matrix (x 106) VY-P (x 106) 153 7 92 244 344 411 444 778 867 964 1085 1089 1112 1212 1299 1408 153 7 92 96 102 98 104 94 11 8 41 9 6 122 90 85 86 82 87 66 12 1 179 103 99 91 141 105 163 110 192 106 97 332 111 227 VY-P (x 106) 244 344 411 444 778 867 964 136 85 86 89 1 2 3 92 83 79 38 140 97 122

Example Results: Without covariances With covariances a0 = 0,1537(23)

Example Results: Uncertainties

Decay parameters Decay Scheme Peak Areas + corrections Pg and corrections dependents of the decay parameters

Decay parameters Decay Scheme Constraints Probability & area

Conclusion The emission probabilities of a multi gamma source are correlated The emission probabilities and efficiencies are dependents. Should use branching ratios and feeding fractions instead of gamma intensities.

References Ruy M. Castro , Vito R. Vanin , Otaviano Helene , Paulo R. Pascholati , Nora L. Maidana , Mauro S. Dias and Marina F. Koskinas, The Interplay Between the Statistical Correlations of g-ray Emission Probabilities and Efficiency Calibration, Applied Radiation and Isotopes 60 (2004) 185-190 http://dx.doi.org/10.1016/j.apradiso.2003.11.014 Vito R. Vanin and Otaviano Helene, Covariance Analysis Within the Framework of the Least-squares Method in Update of X Ray and Gamma Ray Data Standards for Detector Calibration and Other applications, International Atomic Energy Agency, Viena (2007)