ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
Apr 2026
Jan 2026
Latest Journal Issues
Nuclear Science and Engineering
May 2026
Nuclear Technology
February 2026
Fusion Science and Technology
Latest News
DTRA’s advancements in nuclear and radiological detection
A new, more complex nuclear age has begun. Echoing the tensions of the Cold War amid rapidly evolving nuclear and radiological threats, preparedness in the modern age is a contest of scientific innovation. The Research and Development Directorate (RD) at the Defense Threat Reduction Agency (DTRA) is charged with winning this contest.
Massimo A. Larsen, Simon Bolding, Todd Palmer, Jim Morel
Nuclear Science and Engineering | Volume 200 | Number 3 | March 2026 | Pages 525-538
Research Article | doi.org/10.1080/00295639.2025.2495608
Articles are hosted by Taylor and Francis Online.
Residual Monte Carlo (RMC) methods have been previously used in neutron transport for monoenergetic and multigroup problems. In this paper, we implement an RMC algorithm for solving continuous energy problems with elastic scattering functions. We use a piecewise constant finite-element trial space to approximate the transport solution and build the residual representation. Because of the complexity of the scattering term, an analytic distribution cannot be computed for the residual; instead, we sample source particles directly from the scattering integrand using unnormalized importance sampling. We achieve exponentially convergent Monte Carlo (MC) with the use of an additional weight cancellation technique to reduce the magnitude of particle weights. We then demonstrate the algorithm on continuous energy problems and compare the results with standard MC simulations to demonstrate the increased efficiency of the RMC method.