counting statistics and error prediction
DESCRIPTION
Counting Statistics and Error Prediction. Poisson Distribution ( pTRANSCRIPT
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
1
Counting Statistics and Error PredictionPoisson Distribution (p << 1)
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Success ≡ Birthday today.p = 1/365.n = 1000.
• Low cross section.• Weak resonance.• Short measurement (compared to t1/2).
Appendix C
We need to know only the
product.
HW 25HW 25
Asymmetric
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
2
Counting Statistics and Error Prediction
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Gaussian (Normal) Distribution (p << 1, > 20) xSuccess ≡ Birthday today.p = 1/365.n = 10000.
Symmetric
and
slowly
varyi
ng
• Can be expressed as a function of .• Can be expressed in a continuous form.
HW 26HW 26
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
3
Counting Statistics and Error Prediction
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
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Counting Statistics and Error PredictionCalculate the percentage of the samples that will deviate from the mean by less than:• one .• two .• etc …
HW 27HW 27
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
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2
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Counting Statistics and Error Prediction
Baselineoffset
Total area under the curve above the baseline
22, approximately 0.849 the, approximately 0.849 thewidth of the peak at half heightwidth of the peak at half height
This model describes a bell-shaped curve like the normal (Gaussian) probability distribution function. The center x0 represents the "mean", while ½ w is the standard deviation.
What is FWHM? Resolution? Peak centroid? What is FWHM? Resolution? Peak centroid?
HW 28HW 28
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
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Counting Statistics and Error PredictionApplicationsApplications 1- Match experiment to model
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need) what we(all exx
N
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and not
because we set exx
• Assume a specific distribution (Poisson, Gaussian).
• Set distribution mean to be equal to experimental mean.
• Compare variance to determine if distribution is valid for actual data set (Chi-squared test).
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
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• We can’t use Gaussian model for this data set. Why?• Qualitative comparison.• Is 2 close to s2?
• Close!? Less fluctuation than predicted! • But quantitatively?• Chi-squared test.
Counting Statistics and Error Prediction
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Ns
Only to guide the eye!
8.82 x
Back to our example
HW 29HW 29
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By definition:
Thus:
or
Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
8
Counting Statistics and Error Prediction
N
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Chi-squaredChi-squared
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222
sN
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sN
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2
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1 s
N
The degree to which 2 differs from (N-1) is a measure of the departure of the data from predictions of the distribution.
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
9
Counting Statistics and Error Prediction
15.891 (interpolation) or
smaller <= Fluctuation => larger
either gives = 0.6646Conclusion:
no abnormal fluctuation.
Perfect fit
http://www.stat.tamu.edu/~west/applets/chisqdemo.html
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
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Counting Statistics and Error Prediction
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
11
Counting Statistics and Error Prediction
xx
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Single measurementSingle measurement
S2 = 2 ≈ x
68% probability that this interval includes the true average value.What if we want 99%..?
Fractional standard deviation
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Need 1%?Need 1%?Count 10000.Count 10000.
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
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Counting Statistics and Error Prediction
A series of A series of “single” “single”
measurements.measurements.
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
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Counting Statistics and Error Prediction
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
14
Counting Statistics and Error Prediction
Net counts = Gross counts – Background
MeasuredDerived
Gross counts = 1000Background counts = 400Net counts = 600 37 (not 600 24)
Count Rate = ? ?
• What about derived quantities? (Error propagation).(Error propagation).
Compare to addition instead of subtraction.(Count, stop, count).
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
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Counting Statistics and Error Prediction
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21 Nxxx
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Mean value of multiple independent counts.
• Assume we record N repeated counts from a single source for equal counting times:
• For Poisson or Gaussian distributions:
So that
iX xi
Nxxx ...212
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
16
Counting Statistics and Error Prediction
x N
x N
N
Nx
N
x x
NStandard error of the mean
• To improve statistical precision of a given measurement by a factor of two requires four times the initial counting time.
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
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Counting Statistics and Error Prediction
TSBTB optimum
S B
B
Optimizing counting time.
S = (net) source count rate.B = background count rate.TS+B = time to count source + background.TB = time to count background.
To minimize s :
Low-level radioactivity
Weak resonance
Very strong background
High signal-
to-
background
ratio
How to divide the limited available beam time?
HW 30HW 30
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Radiation Detection and Measurement, JU, First Semester, 2010-2011 (Saed Dababneh).
18
Counting Statistics and Error Prediction
• Minimum detectable amount.• False positives and false negatives.
• Background measurement?• Without the “source”.• Should include all sources except the “source”.• Accelerator applications: background with beam.
• Rest of Chapter 3.