2026 workshop program
From Clouds to Stars: Determining Star Formation Efficiency via Stochastic Modeling and Machine Learning
The work
Abstract
Star formation efficiency (SFE) is essential for understanding how dense molecular clumps convert gas into stars, but it is difficult to measure directly because young stellar populations are often deeply embedded and unresolved. In this project, we estimate SFE using a stochastic modeling approach that samples the Initial Mass Function (IMF) to generate possible protostellar populations whose combined luminosity matches the observed clump luminosity. The resulting stellar mass is compared with the clump mass to estimate SFE, with repeated simulations used to account for statistical uncertainty. Building on these estimates, we will apply machine-learning algorithms to test whether SFE and other properties of star-forming regions can be predicted from observable clump characteristics. This combined approach links statistical modeling with data-driven analysis to better understand the physical conditions that shape star formation.