Regularizing made-to-measure particle models of galaxies
Lucia Morganti, Ortwin Gerhard

TL;DR
This paper introduces a moving prior regularization method for made-to-measure galaxy modeling that improves smoothness and accuracy, especially with noisy or incomplete data, by adaptively updating phase-space priors during particle weight adjustments.
Contribution
The paper presents a novel moving prior regularization technique for particle-based galaxy models, enhancing model smoothness and accuracy over traditional methods.
Findings
MPR converges to the true distribution function with high accuracy.
MPR reduces biases in anisotropy and local fluctuations compared to standard regularization.
Applied to real galaxies, MPR yields smoother dynamical models in dark matter potentials.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
