  An extensive study of population control techniques (PCTs) for time-dependent
and eigenvalue Monte Carlo (MC) neutron transport calculations is presented. We
define PCT as a technique that takes a censused population and returns a
controlled, unbiased population. A new perspective based on an abstraction of
particle census and population control is explored, paving the way to improved
understanding and application of the concepts. Five distinct PCTs identified
from the literature are reviewed: Simple Sampling (SS), Splitting-Roulette
(SR), Combing (CO), modified Combing (COX), and Duplicate-Discard (DD). A
theoretical analysis of how much uncertainty is introduced to a population by
each PCT is presented. Parallel algorithms for the PCTs applicable for both
time-dependent and eigenvalue MC simulations are proposed. The relative
performances of the PCTs based on runtime and tally mean error or standard
deviation are assessed by solving time-dependent and eigenvalue test problems.
It is found that SR and CO are equally the most performant techniques, closely
followed by DD.
