Connecting Star Formation in the Milky Way and Nearby Galaxies -II. An Observationally Driven Analytical Model for Predicting Cloud-Scale Star Formation Rates

TL;DR

This paper presents an observationally driven analytical model that predicts cloud-scale star formation rates by integrating data from the Milky Way and nearby galaxies, offering a theoretical framework consistent with observed SFRs.

Contribution

The paper introduces a novel analytical model that combines observational constraints to predict star formation rates at the cloud scale, bridging observations and theory.

Findings

Model predictions align with observed star formation rates.

The framework clarifies key tasks for future observations.

Supports the plausibility of observationally constrained models.

Abstract

We construct a model by integrating observational constraints from the Milky Way and nearby galaxies to predict cloud-scale star formation rates (SFRs). In the model, we first estimate the initial total mass of clumps in a cloud based on the cloud mass, and then generate the initial clump population of the cloud using the initial clump mass function. Next, we model the star formation histories (SFHs) of the cloud to assign an age to each clump. We then sort out the intermediate-age clumps and calculate the total embedded cluster mass. Finally, we predict the SFR based on the duration of the embedded phase. The model-predicted SFR is broadly consistent with the observed SFR, supporting the plausibility of the model. The model primarily provides a theoretical framework that integrates a wide range of observational results, thereby clarifying the tasks for future observations.