Thomas M. Semkow, Xin Li, Liang T. Chu Wadsworth Center, New York State Department of Health, Empire State Plaza, Albany, NY 12201, USA Department of Environmental Health Sciences, School of Public Health, University at Albany, SUNY, Rensselaer, NY 12144, USA [email protected]Workshop on Detection Limits without Noise American Society for Testing and Materials International Webinar April 2, 2020 Statistics of noiseless detectors 1
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Thomas M. Semkow, Xin Li, Liang T. Chu
Wadsworth Center, New York State Department of Health, Empire State Plaza, Albany, NY
12201, USA
Department of Environmental Health Sciences, School of Public Health, University at Albany,
Workshop on Detection Limits without NoiseAmerican Society for Testing and Materials
InternationalWebinar April 2, 2020
Statistics of noiseless detectors
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Where can we expect zero counts?
2
Rare-event physics: neutrino Astrophysics: dark matter Radioactivity counting Mass spectrometry Asbestos counting under microscope Bacteria counting on Petri dish Zero accident
Example: alpha spectrometry
3
239Pu 236Pu
300 400 500 600 700 800
Rel
ativ
e in
tens
ity, s
hifte
d
Pulse height (channel)
LCS
MB = 0
MB > 0
BKG = 0
BKG > 0
Pu-239
Pu-236
Signal and noise trials
Signal trial (gross)
Noise trial
(bkg)Net Comment
General case
Any Regular case
Special case
Ideal detector
Special case
How can you do statistics when signal is zero?
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The following approaches are described to study theory of zero counts
How to set an upper limit for zero counts using Bayesian solution for negative binomial distribution?
We have 3 parameters: evaluate experimentally or theoretically
dispersion coefficient make assumptions about prior
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Zero-inflated Poisson
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1E-5
1E-4
1E-3
1E-2
1E-1
1E+0
0 3 6 9 12 15
Pmf
Counts
Poissonz-PoissonPoisson meanz-Poisson mean
Poisson z-Poisson ExampleParameters 𝜇 𝜇, 0 𝜔 1
𝜇 4𝜔 0.25
Mean 𝜇 1 𝜔 𝜇Dispersion coefficient 𝛿 1 𝛿 1 𝜔𝜇
Bayesian solution 3 parameters: 𝜇, 𝛿 , 𝑎
Johnson et al. 2005
Summary and conclusions
The key solution to zero counts is to calculate an upper-limit for the mean.
A 1-count upper limit results in poor confidence.
A simple probabilistic approach to zero counts is effectively a Bayesian method with uniform prior, and it is biased.
All Bayesian methods based on Poisson distribution used for zero counts are biased, except for the Jaynes prior, the latter is however improper and does not have the upper-limit solution.
The best choice for the upper limit appears to be using Jeffreys prior.
Methods based on negative binomial or z-Poisson require an additional parameter from theory or experiment to estimate the upper limit.
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Selected references
• Beach S.E., Semkow T.M., Remling D.J., Bradt C.J. (2017) Demonstration of fundamental statistics by studying timing of electronics signals in a physics-based laboratory. Am. J. Phys. 85, 515-521.
• Box G.E.P., Tiao G.C. (1994) Bayesian Inference in Statistical Analysis. J. Wiley & Sons, New York, NY.
• Jaynes E.T. (1968) Prior probabilities. IEEE Trans. Sys. Sci. Cyb., SSC-4, 227-241.• Jeffreys H. (2003) Theory of Probability. Clarendon Press, Oxford.• Johnson N.L., Kotz S., Kemp A.W. (2005) Univariate Discrete Distributions. Wiley-Interscience,
Hoboken, NJ.• Müller J.W. (1978) A test for judging the presence of additional scatter in a Poisson process.
BIPM Report, 78/2, Bureau International des Poids et Mesures, Sèvres, France.• Semkow T.M. (2007) Bayesian inference from the binomial and Poisson processes for multiple
sampling. In Applied Modeling and Computations in Nuclear Science, ACS Symposium Series 945, ACS/OUP, Washington, DC, 335-356.
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AbstractIn physical and analytical measurements, we have a noise (or background) trial and a signal trial(i.e., signal plus noise). One can have three experimental outcomes: i) both signal and noisetrials are positive, ii) signal trial is positive whereas noise trial is zero (an ideal detector), as wellas iii) where the signal and noise trials are both zero, which is the subject of this work. The noisetrial could be even greater than zero in an excessively long measurement but may be zero in ameasurement at hand. Can we infer anything about the signal in the latter case iii)? We assumethat the detector is properly calibrated and tested using strong-signal sources. This topic isrelated to the science of rare events, which are often encountered in physics, astrophysics, aswell as in analytical measurements utilizing ionizing radiation, mass spectrometry, asbestosdetection, among others. In our laboratory, the noiseless detection is often encountered in alphaspectrometry performed for radiological health protection. Poisson distribution is central to thescience of rare events and we present a brief introduction to it. The statistics of noiselessdetectors can be handled using Bayesian statistics applied to the Poisson likelihood. We discussthe use of priors: conjugate as well as noninformative (uniform, Jeffreys and Jaynes). Wecalculate the posteriors for the noiseless detector using several priors. From the posteriors, wecalculate statistical measures for the signal such as upper limits, mean values, as well asvariances, when the measured signal is zero. Then, we discuss the effect of different priors onsuch calculated statistical measures as well as their interpretation. The Poisson distribution is asingle-parameter distribution which cannot accommodate overdispersion. A possible presence ofoverdispersion in the detection systems can be determined using strong signals. An extension ofthis work is discussed to include a negative binomial distribution, which can handleoverdispersion. We also describe a zero-inflated Poisson distribution which can handle anenhancement at zero signal as well as overdispersion. 20