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Myers, Kary Lynn

Publications and source records attributed to Myers, Kary Lynn.

Radiation Detection Data Competition Report

In FY2018 through FY2020, NA-22, the Defense Nuclear Nonproliferation Research and Development Program, funded a Data Science project to develop and implement statistical methodology to effectively host data competitions with the goal of leveraging the opportunity provided by crowdsourcing. By accessing and engaging expertise from a broader research community, there is an opportunity to attract innovative solutions from a variety of different research disciplines to advance the ability to solve important non-proliferation problems. This report summarizes the key results of this project after hosting two data competitions focused on urban radiation detection. The first competition was focused on attracting participants from the U.S. national laboratories, while the second, hosted by TopCoder, was open to the broader international community and awarded prize money to the top 10 competitors. At the start of the project, there was strong interest from NA-22 to explore and develop the capability to host data competitions as a means of leveraging the broader community to solve important nuclear nonproliferation problems. Having a standard data set on which to compare different approaches based on clearly defined criteria was desirable to be able to evaluate the state of solutions for important problems. Initially, it was not clear that it would even be possible logistically and bureaucratically to host a competition with an international field of competitors and to award the prize money needed to attract solutions from top competitors. Happily, a path to host the competitions was ultimately found that allowed this powerful accelerator of improvements to be leveraged.

61 RADIATION PROTECTION AND DOSIMETRY↗

An initial exploration of Bayesian model calibration for estimating the composition of rocks and soils on Mars

The Mars Curiosity rover carries an instrument, ChemCam, designed to measure the composition of surface rocks and soil using laser-induced breakdown spectroscopy (LIBS). The measured spectra from this instrument must be analyzed to identify the component elements in the target sample, as well as their relative proportions. This process, which we call disaggregation, is complicated by so-called matrix effects, which describe nonlinear changes in the relative heights of emission lines as an unknown function of composition due to atomic interactions within the LIBS plasma. In this work, we explore the use of the plasma physics code ATOMIC, developed at Los Alamos National Laboratory, for the disaggregation task. ATOMIC has recently been used to model LIBS spectra and can robustly reproduce matrix effects from first principles. The ability of ATOMIC to predict LIBS spectra presents an exciting opportunity to perform disaggregation in a manner not yet tried in the LIBS community, namely via Bayesian model calibration. However, using it directly to solve our inverse problem is computationally intractable due to the large parameter space and the computation time required to produce a single output. Therefore, we also explore the use of emulators as a fast solution for this analysis. We discuss a proof of concept Gaussian process emulator for disaggregating two-element compounds of sodium and copper. The training and test datasets were simulated with ATOMIC using a Latin hypercube design. After testing the performance of the emulator, we successfully recover the composition of 25 test spectra with Bayesian model calibration.

97 MATHEMATICS AND COMPUTING↗