arXiv2024
The presence of pre-main-sequence and low-mass stars, combined with the new data from Gaia DR3 and with 2MASS, significantly enhances the relevance of studying this cluster. We aim to accurately identify cluster members, determine fundamental parameters, assess mass segregation, and establish precise age and distance using Gaia DR3 and 2MASS data. We employed Bayesian analysis and machine learning techniques, including the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) for member identification, the No-U-Turn Sampler (NUTS) from PyMC for modeling, the Sagitta neural network for the identification and age estimation of pre-main sequence stars, and ASteCA for isochrone fitting. We identified 254 probable cluster members with a mode cluster age of $3.53^{+1.40}_{-1.00}$ Myr and a distance of $1.11\pm 0.06$ kpc. The core and tidal radius were determined to be $1.95 \pm 0.19$ and $40.4 \pm 14.3\,\mathrm{arcmin}$, respectively. The analysis revealed primordial mass segregation among binary stars, indicating that NGC 6383 is not fully relaxed. The color magnitude diagram (CMD) shows a well-defined main sequence and a population of pre-main sequence stars, suggesting recent star formation activity from approximately 1 to 6 Myr ago. The software used for this investigation is released with the paper. Our analysis provides updated parameters for NGC 6383, confirming relative recent star formation and mass segregation, and demonstrating the effectiveness of combining advanced computational techniques with traditional methods for studying stellar clusters.