characterization of reactor fuel burn-up from antineutrino spectral distortions
DESCRIPTION
ANGRA Neutrinos Project. Characterization of reactor fuel burn-up from antineutrino spectral distortions. E. Kemp, L.F. G. Gonzalez, T.J.C. Bezerra and B. Miguez for the ANGRA Collaboration State University of Campinas - UNICAMP Physics Institute- Cosmic Rays Department. So/Si. - PowerPoint PPT PresentationTRANSCRIPT
Characterization of reactor fuel burn-up from antineutrino
spectral distortions E. Kemp, L.F. G. Gonzalez, T.J.C. Bezerra
and B. Miguezfor the ANGRA Collaboration
State University of Campinas - UNICAMPPhysics Institute- Cosmic Rays Department
ANGRA Neutrinos Project
So/Si
Neutrino Spectra Parametrization:• Precision spectroscopy with reactor anti-neutrinos.
Patrick Huber, Thomas Schwetz , hep-ph/0407026
Simulation Steps
Energy draw from selected spectrum
Isotope Selection
Weighting by cross-section
Fitting routine to extract the
isotope fraction
6,...,1
1,9,8,5
)exp(
j
i
Ebai
j
jji
1000 events
Fuel evolution
Static Fuel
Energy resolution dE = k.E
dE=0
Fitting convergence study:239Pu fraction
Events
239
Pu
Fis
sion
con
trib
utio
n
•Perfect energy resolution
•Static Fuel
•Assumed rate: 1000/day (Angra expectation)
High Statistics (exposure) Needs• Shape comparison: Kolmogorov-Smirnov test
– Neutrino spectrum: Composition from normalized Schreckemback’s spectra (235U, 239Pu and 241Pu)
Spectral Distortion:expectations from burn-up
• Taking the ratio between the spectra measured in the n-th month and the first one, we can observe the distortion induced by the burn-up
2 3 4 5 6 7 8 9 10 110.80
0.85
0.90
0.95
1.00
1.05
1.10
R(t) = Nith-bin
(t=n) / Nith-bin
(t=1)
R(t
)
Energy [MeV]
Month 1 2 3 4 5 6 7 8 9101112
Spectral Distortion:expectations from burn-up
Nucifer Simulations:we are in good agreement
Thanks to D. Lhuillier !
The Spectral Ratio Fit: an example
3 4 5 6 7 8 90.80
0.85
0.90
0.95
1.00
1.05
1.10S
(t=
6)/S
(t=
0) t
[mon
ths]
Energy (MeV)
Data Linear Fit of Spectral Ratio
6th Month after reactor starting
Spectral Ratio FitRed: linear fit
Green: 95% C.L. bands
The slope time dependence
0 2 4 6 8 10 12-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0.00
red line: exponential modelA + B * exp(t-t
0/)
blue lines: 95% C.L. band
slo
pe
t [months]
Is the slope of R(t) a good indicator for deviations from the expected behavior ?
• Let’s assume a diversion of 1/3 of the reactor fuel during the 6th month
Burnup: impact on the spectrum shape with 1/3 of the fuel replaced at half-cycle
Day number
Fis
sion
fra
ctio
n
Day number
Fis
sion
fra
ctio
nBurnup:
impact on the spectrum shape with 1/3 of the fuel replaced at half-cycle
The slope time dependence
0 2 4 6 8 10 12-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0.00
red line: exponential modelA + B * exp(t-t
0/)
blue lines: 95% C.L. band
slop
e
t [months]
Is it an outlier?
The slope time dependence
0 2 4 6 8 10 12-0.06
-0.05
-0.04
-0.03
-0.02
-0.01
0.00
red line: exponential modelA + B * exp(t-t
0/)
blue lines: 95% C.L. band
slop
e
t [months]
Yes, with 75% C.L.
Simulation Steps
6,...,1
1,9,8,5
)exp(
j
i
Ebai
j
jji
Energy draw from selected spectrum
Isotope Selection
Weighting by cross-section
Χ2 – KS testsNull hypothesis:
No distortion
Repeat until
Fuel evolution:Poisson-like time interval
μ= f.Δt frequency f = f(D,M)
D,M: detector distance and mass
Static Fuel
Energy resolution dE = k.E
dE=0
Experimental Data
(Shreckemback’s Spectra)
T
Simulation Steps
6,...,1
1,9,8,5
)exp(
j
i
Ebai
j
jji
Energy draw from selected spectrum
Isotope Selection
Weighting by cross-section
Χ2 – KS testsNull hypothesis:
No distortion
Repeat until
Fuel evolution:Poisson-like time interval
μ= f.Δt frequency f = f(D,M)
D,M: detector distance and mass
Static Fuel
Energy resolution dE = k.E
dE=0
Experimental Data
(Shreckemback’s Spectra)
T
Simulation Steps
6,...,1
1,9,8,5
)exp(
j
i
Ebai
j
jji
Energy draw from selected spectrum
Isotope Selection
Weighting by cross-section
Χ2 – KS testsNull hypothesis:
No distortion
Repeat until
Fuel evolution:Poisson-like time interval
μ= f.Δt frequency f = f(D,M)
D,M: detector distance and mass
Static Fuel
Energy resolution dE = k.E
dE=0
Experimental Data
(Shreckemback’s Spectra)
T
Hypothesis Tests Results
Chi^2 vs. KS
• Chi^2 (is more optimistic…)– More Type II Errors
• KS test– More Type I Errors
• See T.J.C. Bezerra, B. Miguez and R.M.Almeida works (poster session) for detailed numbers and generalities on this (including oscillation studies)
Chi^2 vs. KS
• Is it possible to profit the better from both of the tests?– Fisher’s method:
• Combination of N different results (p-values) of independent statistical tests resulting in a Chi^2 like quantity with 2K degrees of freedom
Next step for this study…
Conclusions• Isotopic composition measurements by shape analysis
only requires a large number of events– Reduce the time integration:
• Large time intervals degrades information– High exposure:
source luminosity + detector mass+ time• Recognition of fuel diversion is possible by observing
UNEXPECTED spectral distortions (but, how much?)• Required Improvements:
– More sophisticated analysis methods to quote the sensitivity in mass of the recognition method
– Combining information: • Shape + Counting Rates• different statistical methods working together
– Fisher’s method• PCA, LDA: decomposition of a mixed signal (?)
• Thank you !