Made-to-measure methods such as the parallel code NMAGIC have to solve underdetermined problems, where the number of constraints (photometric of kinematic observables) are usually much lower than the number of particle weights to define. A regularization method is therefore needed to make the method converge. Here we introduce a Moving Prior entropy Regularization method (MPR). The basic idea is to update the prior distribution needed by standard entropy regularization in parallel of the weight adaptation. The prior distribution is determined from the distribution of particles in phase-space. This allows one to construct smooth models from noisy data without erasing global phase-space gradients.
Particle model fits to the SAURON integral field kinematic data for NGC 3379. Top rows are the symmetrized SAURON data, middle rows are the best NMAGIC fit with classical entropy regularization and bottom rows are the best NMAGIC fit using Moving Priors entropy Regularization.
Particle model fits to the SAURON integral field kinematic data for NGC 3379. Top rows are the symmetrized SAURON data, middle rows are the best NMAGIC fit with classical entropy regularization and bottom rows are the best NMAGIC fit using Moving Priors entropy Regularization.
Typical particle weight distribution after a classical entropy regularization fit (black) and after Moving Priors entropy Regularization (red). MPR method avoid trails of extremely increased or decreased weights.
Typical particle weight distribution after a classical entropy regularization fit (black) and after Moving Priors entropy Regularization (red). MPR method avoid trails of extremely increased or decreased weights.
