Abstract
Globular clusters (GCs), spheroidal conglomeration of stars tightly bound together by means of gravitational force, are among the oldest objects that live within our galaxy. A key characteristic of these objects is their high density, significantly greater than the average galactic star density (between $\sim10^4$ to $\sim10^6$ stars within a spheroid of radius up to $\sim100\,pc$, in stark contrast to the local average stellar density of about $\sim1-2\,\frac{\text{stars}}{pc^3}$), so that they can be considered collisional systems. The ESA's Gaia (Global Astrometric Interferometer for Astrophysics) mission, which has mapped nearly 2 billion stars in our galaxy up to its third data release, provides the largest set of high-resolution data available, enabling the detailed study of GCs' internal dynamics.
However, the high density of these regions presents a challenge for Gaia's 1.45-meter primary mirror, often resulting in compromised data quality and insufficient resolution. Consequently, accurately associating stars with clusters becomes difficult due to poor estimates and high errors in the parameters.
Machine Learning (ML) algorithms offer a promising solution to this problem. As demonstrated in referenced paper [1], techniques inspired by ML such as Mixture Modelling, which uses Markov-Chain Monte Carlo, Extreme Deconvolution and Maximum Likelihood Estimation, can be employed to infer the general distribution properties of the cluster, distinguishing them from field star distributions. Enhancing these methodologies with neural networks such as Generative Adversarial Networks, which could be used to simulate stellar populations based on observational data, would allow for the assignment of membership probabilities to each source in the sample, significantly increasing the number of sources available, up to a factor of $10^2$, and thereby enhancing the statistical robustness of subsequent astrophysical analyses.
References
[1] Vasiliev, Baumgardt (2021). \emph{Gaia EDR3 view on Galactic globular clusters}; MNRAS 505, 5978–6002