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Title: Efficient early stellar feedback can suppress galactic outflows by reducing supernova clustering
ABSTRACT We present a novel set of stellar feedback models, implemented in the moving-mesh code arepo, designed for galaxy formation simulations with near-parsec (or better) resolution. These include explicit sampling of stars from the IMF, allowing feedback to be linked to individual massive stars, an improved method for the modelling of H ii regions, photoelectric (PE) heating from a spatially varying FUV field and supernova feedback. We perform a suite of 32 simulations of isolated $$M_\mathrm{vir} = 10^{10}\, \mathrm{M_\odot }$$ galaxies with a baryonic mass resolution of $$20\, \mathrm{M_\odot }$$ in order to study the non-linear coupling of the different feedback channels. We find that photoionization (PI) and supernova feedback are both independently capable of regulating star formation to the same level, while PE heating is inefficient. PI produces a considerably smoother star formation history than supernovae. When all feedback channels are combined, the additional suppression of star formation rates is minor. However, outflow rates are substantially reduced relative to the supernova only simulations. We show that this is directly caused by a suppression of supernova clustering by the PI feedback, disrupting star-forming clouds prior to the first supernovae. We demonstrate that our results are robust to variations of our star formation prescription, feedback models and the baryon fraction of the galaxy. Our results also imply that the burstiness of star formation and the mass loading of outflows may be overestimated if the adopted star particle mass is considerably larger than the mass of individual stars because this imposes a minimum cluster size.  more » « less
Award ID(s):
1835509
PAR ID:
10355199
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Monthly Notices of the Royal Astronomical Society
Volume:
506
Issue:
3
ISSN:
0035-8711
Page Range / eLocation ID:
3882 to 3915
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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