Dynamical modelling of dwarf-spheroidal galaxies using Gaussian-process emulation

TL;DR

This paper introduces a Gaussian-process emulation method for efficiently fitting dynamical models to dwarf spheroidal galaxy data, significantly reducing computational costs and enabling complex modeling.

Contribution

The paper presents a novel automated GPE-based approach for dynamical modeling of dSph galaxies, capable of handling computationally expensive models with fewer evaluations.

Findings

Successfully recovered confidence regions with fewer than 100 model evaluations.

Validated the GPE method with synthetic data demonstrating robustness.

Applicable to high-dimensional, time-dependent models in astrophysics.

Abstract

We present a novel and efficient method for fitting dynamical models of stellar kinematic data in dwarf spheroidal galaxies (dSph). Our approach is based on Gaussian-process emulation (GPE), which is a sophisticated form of curve fitting that requires fewer training data than alternative methods. We use a set of validation tests and diagnostic criteria to assess the performance of the emulation procedure. We have implemented an algorithm in which both the GPE procedure and its validation are fully automated. Applying this method to synthetic data, with fewer than 100 model evaluations we are able to recover a robust confidence region for the three-dimensional parameter vector of a toy model of the phase-space distribution function of a dSph. Although the dynamical model presented in this paper is low-dimensional and static, we emphasize that the algorithm is applicable to any scheme that involves the evaluation of computationally expensive models. It therefore has the potential to render tractable previously intractable problems, for example, the modelling of individual dSphs using high-dimensional, time-dependent N-body simulations.