The relative impact of photoionizing radiation and stellar winds on different environments

Abstract

Photoionizing radiation and stellar winds from massive stars deposit energy and momentum into the interstellar medium (ISM). They might disperse the local ISM, change its turbulent multi-phase structure, and even regulate star formation. Ionizing radiation dominates the massive stars' energy output, but the relative effect of winds might change with stellar mass and the properties of the ambient ISM. We present simulations of the interaction of stellar winds and ionizing radiation of 12, 23, and 60 M stars within a cold neutral (CNM, n 0 = 100 cm −3 ), warm neutral (WNM, n 0 = 1, 10 cm −3 ) or warm ionized (WIM, n 0 = 0.1 cm −3 ) medium. The FLASH simulations adopt the novel tree-based radiation transfer algorithm TreeRay. With the On-the-Spot approximation and a temperature-dependent recombination coefficient, it is coupled to a chemical network with radiative heating and cooling. In the homogeneous CNM, the total momentum injection ranges from 1.6×10 4 to 4×10 5 M km s −1 and is always dominated by the expansion of the ionized H II region. In the WIM, stellar winds dominate (2×10 2 to 5×10 3 M km s −1 ), while the input from radiation is small (∼ 10 2 M km s −1 ). The WNM (n 0 = 1 cm −3 ) is a transition regime. Energetically, stellar winds couple more efficiently to the ISM (∼ 0.1 percent of wind luminosity) than radiation (< 0.001 percent of ionizing luminosity). For estimating the impact of massive stars, the strongly mass-dependent ratios of wind to ionizing luminosity and the properties of the ambient medium have to be considered.

SILCC-Zoom: H2 and CO-dark gas in molecular clouds – the impact of feedback and magnetic fields

“…Each massive star follows its individual, mass- MC1-HD 11.9 t 0 + 4.0 no B, no FB (1) MC2-HD 11.9 t 0 + 4.0 no B, no FB (1) MC1-HD-FB 11.9 t 0 + 4.0 no B, FB (2) MC2-HD-FB 11.9 t 0 + 4.0 no B, FB dependent stellar evolutionary track (Ekström et al 2012;Gatto et al 2017;Peters et al 2017) where we follow in detail the amount of photoionizing radiation released by each star (Haid et al 2018(Haid et al , 2019. The radiative feedback is treated with a backwards ray-tracing algorithm TreeRay (Wünsch et al 2018, Wünsch et al, in prep.…”

“…The radiative transport equation is solved for hydrogen-ionizing EUV radiation assuming the On-the-Spot approximation with a temperature dependent case B recombination coefficient (Draine 2011). The resulting number of hydrogen-ionizing photons and the associated heating rate are processed within the chemical network (Haid et al 2018).…”

SILCC-Zoom: The early impact of ionizing radiation on forming molecular clouds

“…A massive star with M * ≈ 23 M emits a factor of ∼100 less energy in a wind than it releases in radiative energy (Matzner 2002) but higher/lower mass stars have stronger/weaker winds relative to radiation. Furthermore, stellar winds are inefficiently coupled to dense environment (Haid et al 2018). Therefore, in massive MCs (M 10 5 M ), the impact of stellar winds seems negligible (Dale et al 2014;Geen et al 2015;Ngoumou et al 2015;Howard et al 2017).…”

trevr: A generalNlog2Nradiative transfer algorithm

“…In this case the radiation field can be computed instantaneously as a geometric problem. Computational methods in this category include forward raytracers such as C 2 Ray (Mellema et al 2006), Moray (Wise & Abel 2011) and Fervent (Baczynski et al 2015) as well as reverse raytracers such as TreeCol (Clark et al 2012), URCHIN (Altay & Theuns 2013) and TREERAY (Wünsch et al 2018;Haid et al 2018).…”

“…Recently there has been some focus on reverse ray tracing methods by Clark et al (2012), Altay & Theuns (2013), Woods (2015) (applied in Kannan et al 2014) and Haid et al (2018). The first two methods listed are not general, as they are designed to compute external radiation (e.g.…”