The role of previous generations of stars in triggering star formation and driving gas dynamics

Authors: Nicholas P. Herrington, Clare L. Dobbs, Thomas J. R. Bending

MNRAS, V521, I4, Y2023
arXiv: 2304.06659v1 - DOI (astro-ph.GA)
12 pages, 10 figures
License: CC BY 4.0

Abstract: We present hydrodynamic and magnetohydrodynamic (MHD) simulations of sub galactic regions including photoionising and supernova feedack. We aim to improve the initial conditions of our region extraction models by including an initial population of stars. We also investigate the reliability of extracting regions in simulations, and show that with a good choice of region, results are comparable with using a larger region for the duration of our simulations. Simulations of star formation on molecular cloud scales typically start with a turbulent cloud of gas, from which stars form and then undergo feedback. In reality, a typical cloud or region within a galaxy may already include, or reside near some population of stars containing massive stars undergoing feedback. We find the main role of a prior population is triggering star formation, and contributing to gas dynamics. Early time supernova from the initial population are important in triggering new star formation and driving gas motions on larger scales above 100 pc, whilst the ionising feedback contribution from the initial population has less impact, since many members of the initial population have cleared out gas around them in the prior model. In terms of overall star formation rates though, the initial population has a relatively small effect, and the feedback does not for example suppress subsequent star formation. We find that MHD has a relatively larger impact than initial conditions, reducing the star formation rate by a factor of 3 at later times.

Submitted to arXiv on 13 Apr. 2023

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.