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Butler, P.

Publications and source records attributed to Butler, P..

Machine Learning Modeling Pipeline for Extracting Nuclear Proliferation Events of Interest from Open Data Sources (U)

In FY2020, the Savannah River National Laboratory (SRNL) and the Sanghani Center for Artificial Intelligence and Data Analytics at Virginia Polytechnic Institute and State University entered a collaboration funded by Department of Energy’s (DOE) Office of Defense Nuclear Nonproliferation Research and Development. The project’s mission was to take the first steps toward developing a demonstration prototype system that uses multiple machine learning and data analytics methods on largescale open data sources to identify new, developing, and/or undeclared nuclear programs. Given the SRNL team’s on-site perspective of events culminating in the DOE’s decision to pursue the Savannah River Plutonium Processing Facility (SRPPF), the team targeted the identification of events and indicators in retrospective datasets that pointed to the activity of “fissile core fabrication at the Savannah River Site” prior to the official announcement in May of 2018. A preliminary modeling pipeline was developed in FY20 that showed the datasets contained adequate signal for continuation of efforts. In FY21, a modular demonstration prototype modeling pipeline has continued in development for two text-based data sources: a broad internet archive (Webhose Ltd.) and a decahose Twitter database (i.e., a global sampling of one in every ten Tweets). The techniques that have been developed rely on graph theory and anomaly detection to identify contextual shifts in key words and phrases at various points in time such that indicators of events of interest could be identified and subsequently, events could be extracted from the corpuses. The foundational concept behind the approaches is that contextual shifts in key words and phrases can act as indicators of events of interest. Both datasets have proven successful in extracting events of interest related to pit production at the Savannah River Site prior to the official announcement. In addition, the pipelines have generated a wide range of events broadly summarized as: the awarding of DOE contracts at major sites, DOE investments in various programs, accidents at DOE national laboratories, speculations about the fate of pit production in the DOE complex, domestic and international shipments and receipts of nuclear materials at DOE sites, termination of non-proliferation agreements with Russia, termination of MOX, new weapons development approvals/testing, nuclear posture reviews, major DOE cleanup/production milestones, political opinions, and nuclear watch groups’ opinions, among many others.

97 MATHEMATICS AND COMPUTING↗

Machine Learning Using Open Data Sources for Detection of Nuclear Proliferation Activities (U)

In FY2020, Savannah River National Laboratory (SRNL) in collaboration with the Sanghani Center for Artificial Intelligence and Data Analytics (SCAIDA) at Virginia Polytechnic Institute and State University (VT) and funded by the Department of Energy’s (DOE) Defense Nuclear Nonproliferation Research and Development, began developing a demonstration prototype system that uses multiple machine learning and data analytic methods on large-scale open data sources to identify new, developing, and/or undeclared nuclear programs. Using the announcement in May 2018 of the proposed Savannah River Plutonium Processing Facility (SRPPF) as a test subject, the goal of this 2-year project is to forecast the SRPPF using only data prior to May 2018. The project work is split into a preliminary prototype development for the first year with an initial evaluation of viability followed by the second year of development to create an integrated prototype system and more extensive performance evaluation. This report documents the results of the preliminary-phase tasks.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

Multispectral imaging of the lunar regolith core samples - Preliminary results for 74002

Multispectral images of a lunar core segment are a new form of continuous data along the length of the core. A new laboratory arrangement was developed to obtain these images. Data processing and initial data analysis for core section 74002 were performed. The images are sensitive to variations in mineralogy and/or maturity of the soil and are easily used (1) in stratigraphic studies of the lunar regolith, (2) for sample selection of representative material, and (3) as ground truth for remote sensing studies.

Pieters, C. M.↗

The dissection and consortium allocation of Apollo 17 lunar rocks from the boulder at station 7

The Apollo 17 astronauts removed four rocks samples to represent each of the lithologies they recognized in the boulder at station 7: sample 77215 from an off-white meter-sized block; sample 77075 from one of the thin dikes that cross the off-white block; 77115 from the blue-gray rock adjacent to the off-white block and apparently continuous with thin dikes that cross the block; sample 77135 of the tan-gray or green-gray vesicular rock adjacent to the blue-gray (77115) rock. A consortium of investigators has been organized to study the samples. Each sample shows a number of lithologic types in terms of clasts (or xenoliths) and matrices. A table shows how subsamples have been allocated for consortium study. Maps and photographs show the relations between subsample locations and lithologies for the two more dissected samples, 77115 and 77135.

Butler, P.↗