Elsevier

New Astronomy Reviews

Volume 68, October 2015, Pages 1-33
New Astronomy Reviews

The modelling of feedback in star formation simulations

https://doi.org/10.1016/j.newar.2015.06.001Get rights and content

Abstract

I review the current state of numerical simulations of stellar feedback in the context of star formation at scales ranging from the formation of individual stars to models of galaxy formation including cosmic reionisation. I survey the wealth of algorithms developed recently to solve the radiative transfer problem and to simulate stellar winds, supernovae and protostellar jets. I discuss the results of these simulations with regard to star formation in molecular clouds, the interaction of different feedback mechanisms with each other and with magnetic fields, and in the wider context of galactic- and cosmological-scale simulations.

Section snippets

Introduction and scope of this review

The formation of stars is arguably the most important process in astrophysics, impacting virtually every theoretical and observational subfield. Despite its prominence and decades of intensive study, there is still much about star formation that is not understood. One reason for this slow progress is the fact that the conversion of gas to stars is a non-linear process. There are a variety of reasons for this, such as the non-linearity of the self-gravitational forces which lead to the collapse

Brief introduction to stellar feedback physics

Stellar feedback involves the insertion of matter, momentum and energy from stars into the surrounding fluid, from which the stars may also still be accreting gas. In terms of material, momentum and energy emitted per star, massive OB-type stars far outweigh their lower-mass brethren in importance. In clouds where there are no O-stars (either because the cloud mass is too small to support massive star formation, or because there has not been time for O-stars to form), feedback from low- and

Brief introduction to astrophysical fluid dynamics codes

Star formation takes place in the interstellar medium (ISM), the thin and usually hot gas which occupies much of the volume of most galaxies. The mean free paths of ions, atoms and molecules in the ISM tend to be small compared with the sizes of the structures which they belong to. It is therefore reasonable to approximate the ISM as a smoothly-varying fluid. However, in order to model the behaviour of a fluid on a computer, it is necessary to discretise it in some way into individual fluid

Feedback algorithms

This section briefly surveys some of the algorithms used to model stellar feedback mechanisms. The focus is on the algorithms themselves and the assumptions that underlie them. The results gained from using them will be discussed in a later section.

What we have learned from including feedback in simulations

The previous section concentrated on technical descriptions of algorithms and is intended to be mainly of use to researchers who are considering writing their own feedback prescription and wish to get an overview of how it has been done before. This section is aimed at a different readership and will concentrate on the science results of simulations run using the algorithms and codes described. There will inevitably be a small amount of repetition and overlap between these two sections, so some

Summary and outlook

The last decade has seen the development of a tremendous variety of sophisticated algorithms to model the various kinds of stellar feedback, and a corresponding wealth of simulations employing these algorithms to answer a wide variety of questions over a huge range of scales. We have learned an enormous amount from these simulations.

Feedback regulates or helps to regulate the rate at which gas is converted to stars at every stage of the star formation process. The background cosmic ionising

Acknowledgements

The author is grateful for the support of the DFG cluster of excellence ‘Origin and Structure of the Universe’. In writing this review, he has made extensive use of the nasa/sao ads literature search engine and of the papers software package (http://www.papersapp.com), without either of which the process would have been much longer and more tedious. The author is also grateful to David Hubber for useful discussions of numerical methods, and to the referee, Ant Whitworth, for a

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