2024MNRAS.528.7477B

Observations of star-forming regions provide snapshots in time of the star formation process, and can be compared with simulation data to constrain the initial conditions of star formation. In order to make robust inferences, different metrics must be used to quantify the spatial and kinematic distributions of stars. In this paper, we assess the suitability of the INdex to Define Inherent Clustering And TEndencies (INDICATE) method as a diagnostic to infer the initial conditions of star-forming regions that subsequently undergo dynamical evolution. We use INDICATE to measure the degree of clustering in N-body simulations of the evolution of star-forming regions with different initial conditions. We find that the clustering of individual stars, as measured by INDICATE, becomes significantly higher in simulations with higher initial stellar densities, and is higher in subvirial star-forming regions where significant amounts of dynamical mixing have occurred. We then combine INDICATE with other methods that measure the mass segregation (Λ_MSR), relative stellar surface density ratio (Σ_LDR), and the morphology (Q-parameter) of star-forming regions, and show that the diagnostic capability of INDICATE increases when combined with these other metrics.