# Star Cluster Formation in a Turbulent Molecular Cloud Self-Regulated by Photo-Ionisation Feedback

## TL;DR

This study uses radiation-hydrodynamical simulations to explore how photo-ionisation feedback influences star formation efficiency, cluster properties, and stellar mass distribution in turbulent molecular clouds, aligning well with observations of young clusters.

## Contribution

It introduces detailed radiation-hydrodynamical simulations that demonstrate the impact of feedback on star cluster formation and properties, a step forward from previous models.

## Key findings

- Stronger feedback reduces star formation efficiency.
- Feedback shapes the stellar mass function's high-mass end.
- Feedback leads to lower-density, more stable star clusters.

## Abstract

Most stars in the Galaxy are believed to be formed within star clusters from collapsing molecular clouds. However, the complete process of star formation, from the parent cloud to a gas-free star cluster, is still poorly understood. We perform radiation-hydrodynamical simulations of the collapse of a turbulent molecular cloud using the RAMSES-RT code. Stars are modelled using sink particles, from which we self-consistently follow the propagation of the ionising radiation. We study how different feedback models affect the gas expulsion from the cloud and how they shape the final properties of the emerging star cluster. We find that the star formation efficiency is lower for stronger feedback models. Feedback also changes the high mass end of the stellar mass function. Stronger feedback also allows the establishment of a lower density star cluster, which can maintain a virial or sub-virial state. In the absence of feedback, the star formation efficiency is very high, as well as the final stellar density. As a result, high energy close encounters make the cluster evaporate quickly. Other indicators, such as mass segregation, statistics of multiple systems and escaping stars confirm this picture. Observations of young star clusters are in best agreement with our strong feedback simulation.