Los Alamos National Security, LLC

Triad National Security, LLC

Decision Sciences International Corporation

Nicolas Hengartner

Michael James Sossong

Christopher L Morris

Larry Joe Schultz

Konstantin N Borozdin

Alexei V Klimenko

J Andrew Green

Richard A Smith

Alexander Saunders

James M Colthart

David C Klugh

Gary E Scoggins

Andrew Mcleod Fraser

David C Vineyard

John Christopher Orum

Leticia Cuellar-Hengartner

Effects in multiple linear regression may be decorrelated to decompose and attribute risk to common and proper effects. In other words, an attribute risk may be decomposed to two or more causes, where each cause is characterized by multiple attributes. The risk decomposition may decompose risk into a first residual part associated with a first set of risk factors, a second residual part associated with a second set of risk factors, and a common part associated with a set of common hidden variables that minimize a correlation between the first set of factors and the second set of factors. The common hidden variables may be modeled using a hidden factor model. An effect of the correlation may be minimized on the first set of risk factors and the second set of risk factors, and how correlated the terms of the risk decomposition are may be quantified.

Decorrelating effects in multiple linear regression to decompose and attribute risk to common and proper effects

Systems and methods for charged particle detection including statistical reconstruction of object volume scattering density profiles from charged particle tomographic data to determine the probability distribution of charged particle scattering using a statistical multiple scattering model and determine a substantially maximum likelihood estimate of object volume scattering density using expectation maximization (ML/EM) algorithm to reconstruct the object volume scattering density. The presence of and/or type of object occupying the volume of interest can be identified from the reconstructed volume scattering density profile. The charged particle tomographic data can be cosmic ray muon tomographic data from a muon tracker for scanning packages, containers, vehicles or cargo. The method can be implemented using a computer program which is executable on a computer.

Robust statistical reconstruction for charged particle tomography

Techniques, apparatus and systems for detecting particles such as muons for imaging applications. Subtraction techniques are described to enhance the processing of the muon tomography data.

Imaging and sensing based on muon tomography

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Los Alamos, NM, United States of America

Nicolas Hengartner