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The initial conditions of observed star clusters - I. Method description and validation

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abstract

We have coupled a fast, parametrized star cluster evolution code to a Markov Chain Monte Carlo code to determine the distribution of probable initial conditions of observed star clusters, which may serve as a starting point for future $N$-body calculations. In this paper we validate our method by applying it to a set of star clusters which have been studied in detail numerically with $N$-body simulations and Monte Carlo methods: the Galactic globular clusters M4, 47 Tucanae, NGC 6397, M22, $\omega$ Centauri, Palomar 14 and Palomar 4, the Galactic open cluster M67, and the M31 globular cluster G1. For each cluster we derive a distribution of initial conditions that, after evolution up to the cluster's current age, evolves to the currently observed conditions. We find that there is a connection between the morphology of the distribution of initial conditions and the dynamical age of a cluster and that a degeneracy in the initial half-mass radius towards small radii is present for clusters which have undergone a core collapse during their evolution. We find that the results of our method are in agreement with $N$-body and Monte Carlo studies for the majority of clusters. We conclude that our method is able to find reliable posteriors for the determined initial mass and half-mass radius for observed star clusters, and thus forms an suitable starting point for modeling an observed cluster\rq{}s evolution.

fields

cs.AI 1

years

2026 1

verdicts

UNVERDICTED 1

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  • Solving Inverse Problems of Chaotic Systems with Bidirectional Conditional Flow Matching cs.AI · 2026-06-23 · unverdicted · none · ref 61 · internal anchor

    Bi-CFM learns bidirectional mappings between initial and final state distributions to solve ill-posed inverse problems in chaotic systems, reporting metric improvements and speedups on Lorenz variants plus conservation-respecting results on three-body and globular cluster data.