Supernova feedback in porous photoionized Giant Molecular Clouds
Cheryl S. C. Lau, Ian A. Bonnell, Yueh-Ning Lee, Ke-Jung Chen
astro-ph.GA
Submitted: 2026-08-01
Comments: 24 pages, 28 figures
Code: https://github.com/Cheryl-Lau/phantom
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
The gist: We present a new suite of numerical simulations of Type II supernovae (SNe) detonating in Giant Molecular Clouds with a variety of density structures shaped by photoionization feedback.
Terminology
Abstract
We present a new suite of numerical simulations of Type II supernovae (SNe) detonating in Giant Molecular Clouds with a variety of density structures shaped by photoionization feedback. Ionizing radiation sculpts cavities and channels that guide SN energy to emerge from the cloud as shock-driven blowouts, rather than as a coherent spherically expanding shell as assumed in most sub-grid SN models adopted in galaxy or cosmological simulations. We investigate how such outflows differ to the 1-D descriptions, and whether or not the perturbations induced by the blowouts are sensitive to the host cloud's structure. A channelling parameter P chnl is introduced to characterise the cloud's porosity and boundness using the morphology of the ionized channels. Our results reveal that the outflow velocities, whilst consistently higher than that of the spherical blasts, are in fact rather independent of the porosity of its local environment. The total kinetic energy and momentum deposited also appear similar across all runs. What is most sensitive to P chnl is the mass of the materials carried in the outflows and their migration distances. It implies that SNe exploding in compact clouds with distinctive channel structures may have more confined metal injection radii and shortened turbulent driving scales, which consequently lead to a clumpier interstellar medium with higher density and metallicity fluctuations. We argue that molecular cloud structures play an equally important role to SN rates and energy budgets in stellar feedback sub-grid modelling.
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