### ERC Starting Grant RADFEEDBACK (grant no. 679852)

#### Star formation and feedback in molecular clouds

Prof. Stefanie Walch

Within the SILCC-Zoom project, we study the early impact of ionizing radiation on forming molecular clouds. In Haid et al. (2019) we present our first sub-parsec resolution radiation-hydrodynamic simulations of two molecular clouds self-consistently forming from a turbulent, multiphase ISM. The clouds have similar initial masses of few $10^4$ $M_{\odot}$, escape velocities of $\sim 5$ $km$ $s^{-1}$, and a similar initial energy budget. We follow the formation of star clusters with a sink-based model and the impact of radiation from individual massive stars with the tree-based radiation transfer module TREERAY (Wünsch et al., in prep., and Wünsch et al., 2018, MNRAS, 475, 3393). Photoionizing radiation is coupled to a chemical network to follow gas heating, cooling, and molecule formation and dissociation. For the first 3 Myr of cloud evolution, we find that the overall star formation efficiency is considerably reduced by a factor of ˜4 to global cloud values of $\lt 10$ percent as the mass accretion of sinks that host massive stars is terminated after $\leq 1$ Myr. Despite the low efficiency, star formation is triggered across the clouds. Therefore, a much larger region of the cloud is affected by radiation and the clouds begin to disperse. The time-scale on which the clouds are dispersed sensitively depends on the cloud sub-structure and in particular on the amount of gas at high visual extinction. The damage of radiation done to the highly shielded cloud is delayed. We also show that the radiation input can sustain the thermal and kinetic energy of the clouds at a constant level. Our results strongly support the importance of ionizing radiation from massive stars for explaining the low-observed star formation efficiency of molecular clouds.

#### Non equilibrium chemistry and destruction of CO by X-ray flares

Sources of X-rays such as active galaxies and X-ray binaries are often variable by orders of magnitude in luminosity over time scales of years. During these flares and for some time afterwards, the surrounding gas is out of chemical and thermal equilibrium. We introduce a new implementation of X-ray radiative transfer coupled to a time dependent chemical network for use in 3D magnetohydrodynamic simulations. ( J. Mackey, S. Walch et al., 2018)

#### Bipolar outflow from protostar formation

We study the influence of episodic outflow feedback on a turbulent, over critical Bonnor-Ebert sphere with a mass of $M_{\mathrm{BES}} = 2.7 \, \mathrm{M}_{\odot}$ and a radius of $r_{\mathrm{BES}} = 0.056$ pc. The mass resolution is $4.5 \times 10^{-6} \mathrm{M}_{\odot}$ per SPH particle.
We follow the collapse of the Bonnor-Ebert sphere until $t_{\mathrm{end}} = 180$ kyr. A single protostar with a final mass of $M_{\star} = 0.45 \, \mathrm{M}_{\odot}$ forms at $t = 90$ kyr. This protostar launches an S-shaped chain of outflow bullets, called Herbig-Haro objects. This S-shaped form arises due to the varying orientation of the angular momentum axis of the inner accretion disk (IAD), $L_{\mathrm{IAD}}$, caused by anisotropic accretion of the sink particle from the turbulent envelope and outer disk. (Rohde et al., in prep)

#### Velocity channel map of C$^{18}$O(1-0) observations of a fragmenting filament

Recent observations of star-forming filaments show that they are split into smaller velocity-coherent filaments, termed fibres. However, these fibres are defined in position-position-velocity (PPV) space, and so from observations it is unclear exactly how they relate to physically cohesive structures in position-position-position (PPP) space. Using simulations of fragmenting non-equilibrium filaments, we produce synthetic observations of the C$^{18}$O (1-0) line and analysis the resulting PPV cube in the same manner as observers have previously done. As a result we find numerous fibres. Having access to the PPP data from the simulation we show that the fibres do not come from physically cohesive and distinct structures. Rather we show that the mapping from PPV to PPP (and vice-versa) is complicated by the presence of internal shocks within the filament, driven by the filaments accretion from its surrounding. (Clarke et al., 2018)

#### Synthetic column density map based on dust

We compare synthetic observations of dust emission and several molecular line tracers (for example $^{12}$CO, $^{13}$CO and HI) using zoom-in simulations of molecular cloud formation. We use the SILCC zoom-in simulations presented in Seifried et al. (2017) and new simulations including magnetic fields presented in Seifried et al. (2018). These simulations connect, for the first time simultaneously, the larger scale environment, chemical network, self-gravity and high spatial resolution. The formation of molecular clouds is simulated together with their surroundings, providing more realistic initial conditions for cloud formation than just using an isolated box. The chemical components of the gas are coupled together and simulated simultaneously with the cloud dynamics. The zoom-in simulations reach a resolution of approximately 0.1 pc, showing also the evolution of filamentary structures within the clouds. These simulations give a realistic starting point for making synthetic observations of cloud evolution from large scale structures to molecular cloud and filament scales, and comparing to real observations using several tracers. This enables us to compare, for example, the differences between cloud structures as revealed by different tracers, and their correlation. As an example, the above figure shows a synthetic column density map based on dust emission. We also study the velocity structure of the cloud based on the synthetic line data.

## Acknowledgments

This work is supported by
 SFB 956 Cologne Bonn project C5 ERC Starting Grant 679852 'The Radiative Interstellar Medium' The DFG Priority Programme 1573 'Physics of the ISM' Leibniz-Rechenzentrum Garching Gauss Center for Supercomputing: Link to a short project description on the GCS site Max Planck Computing and Data Facility (MPCDF)