nuclear stockpile stewardship and bayesian image analysis (darht and the bie) (u)

Operated by Los Alamos National Security, LLC for the U.S. Department of Energy’s NNSA

U N C L A S S I F I E D Slide 1

Nuclear Stockpile Stewardship and Bayesian Image Analysis (DARHT

and the BIE) (U)

By James L. Carroll

Jan 2011

LA-UR 11-00201


U N C L A S S I F I E D

Abstract Since the end of nuclear testing, the reliability of our nation’s nuclear weapon

stockpile has been performed using sub-critical hydrodynamic testing. These tests involve some pretty “extreme” radiography. We will be discussing the challenges and solutions to these problems provided by DARHT (the world’s premiere hydrodynamic testing facility) and the BIE or Bayesian Inference Engine (a powerful radiography analysis software tool). We will discuss the application of Bayesian image analysis techniques to this important and difficult problem. (U)

Slide 2



Talk Outline Stockpile Stewardship DARHT Bayesian Image Analysis BIE

Slide 3



Nuclear Weapons 101 Nuclear weapons are

comprised of• Primary• Secondary• Radiation Case• Delivery Packaging

Slide 4

Primary Pit:• Sub-critical fissile mass surrounded by

HE.• Implosion creates super critical mass.• Chain reaction energy and radiation

“initiate” the secondary.



Testing Nuclear Weapons:

Atmospheric TestingJuly 16, 1945 (Trinity) – 1963 (Limited Test Ban Treaty)

Slide 5

Underground Testing1963 (Limited Test Ban Treaty) – 1992 (last critical US nuclear test)



Sub-Critical Nuclear Testing Radiographic images from sub-critical testing help ensure the

credibility of our enduring nuclear weapon stockpile.• Evaluating effects of aging on materials• Fine tuning computer modeling of weapon performance and behavior.• Evaluating re-manufactured components.• Certification of existing weapon systems in stockpile.

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What is Hydrodynamic Testing? High Explosives (HE) driven experiments to study nuclear weapon

primary implosions.• Radiographs of chosen instants during dynamic conditions.• Metals and other materials flow like liquids under high temperatures and pressures

produced by HE.

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Static Cylinder Set-up Static Cylinder shot Static Cylinder Radiograph



The challenge of imaging the pit:

Spot size Motion blur Scatter Dose Noise

• Poisson events• Camera• Cosmic rays

Tilt/Cone Effects

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Challenges

Spot size:

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ChallengesMotion blur:

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Challenges

Scatter:

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Challenges:Dose

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Challenges

Dose

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Graded collimator



Noise

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Tilt Effects

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Cone Effects

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England: Moguls

United States: FXR

United States: PHERMEX

Russia: BIM-MFrance: AIRIX



DARHT:Phase 2: “Second Axis”

Phase 1: “First Axis”

Lab Space and Control Rooms

Firing Point

Optics and Detector Bunker



DARHT Two linear induction accelerators at right angles

produce extremely powerful and tight electron beams Metal target stops electrons

and makes x-rays with a very small spot size Scintillator converts x-rays to

visible light Light is captured by specialized cameras Axis 2 can be “pulsed” to produce four separate “dynamic”

images

Slide 19



DARHT Accelerator Principles of Operation

The DARHT accelerators use pulsed power sources to produce and accelerate a single electron beam pulse. DARHT Axis 2 chops the beam into 4 pulses just before the target.

The two machines use different pulse power technology

Slide 20

DARHT Axis 1 Accelerator 60-ns, 2-kA, 19.8-MeV electron beam for

single pulse radiography. Linear Induction Accelerator with ferrite

cores and Blumlein pulsed power. The injector uses a capacitor bank and a

Blumlein at 4-MV. Cold velvet cathode. Single 60 ns pulse. Operation began in July 1999.

DARHT Axis 2 Accelerator 2-ms, 2-kA, 18.4-MeV electron beam for 4-pulse radiography. Linear Induction Accelerator with wound

Metglass cores and Pulse Forming Networks (PFNs) .

The Injector uses a MARX bank with 88 type E PFN stages at 3.2 MV.

Thermionic cathode. 4 micropulses - variable pulse width. Operations began in 2008.



Inside DARHT

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DARHT image resolution:

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“Bayesian” Image Analysis

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THE FTO

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A forward modeling approach is currently used in analysis of (single-time) radiographic data

True radiographicphysics

?

True density(unknown)

Inverse approach(approximatephysics)

Transmission (experimental)





?




How do we extract density from this transmission?





?


Model density(allowed to vary)


Comparestatistically

Simulatedradiographicphysics


Transmission (simulated)





We develop a parameterizedmodel of the density (parameters here might be edge locations, density values)


?














?









Model parameters are varied so that the simulated radiograph matches the experiment



What does any of this have to do with Bayes Law?

Slide 30






?










p(m|r)

h(m)



Bayesian Analysis

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Assumptions

Where n is some independent , additive noise. If n is Gaussian then:

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Plug that in:

If I assume a uniform prior then:

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?










p(m|r)

h(m)



The Solution Building h(m) Optimizing parameters m

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Using the Prior Computer vision is notoriously under constrained. Penalty terms on the function to be optimized can often overcome this

problem. These terms can be seen as ill-posed priors

• GGMRF

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Example, approximating scatter

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BIE A programming language to express h(m)

• Graphical• Reactive• Interactive

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Future Work:

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The forward-modeling framework makes possible a global optimization procedure

t2t1 t3 t4

Prior knowledge provides additionalconstraints at each time SOLUTION:

Evaluated Density

DATA:Transmission(experiment)

Data constrain solution at each time

Now, physics-based constraints on the evolution of the time-series data will also constrain the (global) solution

t2t1 t3 t4



These physics-based constraints will maximize information extracted from each dataset

Concept: Can we learn something about the solution at time 3 (blue) from the data at surrounding times?

Approach: use physics to constrain solution at each time based upon time-series of data.

WHEN WILL THIS APPROACH HAVE GREATEST VALUE?When certain conditions are met:1) Must have the time between measurements (t) on the order of a relevant time scale of the flow; and

2) Must have non-perfect data (due to noise, background levels, etc).

timet1t2t3t4t5

Consider an evolving interface:

Data must be correlated in time!

Perfect data would be the only required constraint… (Noisier data means the global optimization adds more value).



Statistical Improvements Hypothesis testing. Uncertainty estimation.

Slide 45

nuclear stockpile stewardship and bayesian image analysis (darht and the bie) (u)

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