  Made-to-measure methods such as the parallel code NMAGIC are powerful tools
to build galaxy models reproducing observational data. They work by adapting
the particle weights in an N-body system until the target observables are well
matched. Here we introduce a moving prior regularization (MPR) method for such
particle models. It is based on determining from the particles a distribution
of priors in phase-space, which are updated in parallel with the weight
adaptation. This method allows one to construct smooth models from noisy data
without erasing global phase-space gradients. We first apply MPR to a spherical
system for which the distribution function can in theory be uniquely recovered
from idealized data. We show that NMAGIC with MPR indeed converges to the true
solution with very good accuracy, independent of the initial particle model.
Compared to the standard weight entropy regularization, biases in the
anisotropy structure are removed and local fluctuations in the intrinsic
distribution function are reduced. We then investigate how the uncertainties in
the inferred dynamical structure increase with less complete and noisier
kinematic data, and how the dependence on the initial particle model also
increases. Finally, we apply the MPR technique to the two
intermediate-luminosity elliptical galaxies NGC 4697 and NGC 3379, obtaining
smoother dynamical models in luminous and dark matter potentials.
