After receiving his PhD in applied physics from the Colorado School of Mines, Caleb Mattoon completed a postdoctoral appointment at the National Nuclear Data Center. He joined LLNL in 2010 and currently works in the Nuclear Data & Theory group.
Mattoon’s research interests include improving LLNL’s nuclear data pipeline to get new experimental data into simulation codes more quickly, testing the quality of evaluated nuclear data libraries, and working with radiation transport code teams to improve the fidelity of nuclear data. In addition, he works with the Nuclear Security Physics group at LLNL, using machine learning to improve detection algorithms and reduce false alarm rates when screening vehicle traffic for special nuclear material. His has work has recently received recognition from both the Physical and Life Sciences Directorate—for helping to improve LLNL’s nuclear data evaluation capability—and the Global Security Directorate—for his contributions to the Enhanced Radiological and Nuclear Inspection and Evaluation (ERNIE) toolkit.
C.M. Mattoon, "Covariances in the Generalized Nuclear Data (GND) Structure",
A. Czeszumska et al., "Determining the Np-239(n,f) cross section using the surrogate ratio method",
C.M. Mattoon, B.R. Beck, N.R. Patel, N.C. Summers, G.W. Hedstrom, D.A. Brown, “Generalized Nuclear Data: A New Structure (with Supporting Infrastructure) for Handling Nuclear Data”,