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Magnetic fields are of critical importance for our understanding of the origin and long-term evolution of the Milky Way. This is due to their decisive role in the dynamical evolution of the interstellar medium (ISM) and their influence on the star-formation process. Faraday rotation measures (RM) along many different sightlines across the Galaxy are a primary means to infer the magnetic field topology and strength from observations. However, the interpretation of the data has been hampered by the failure of previous attempts to explain the observations in theoretical models and to synthesize a realistic multi-scale all-sky RM map. We here utilize a cosmological magnetohydrodynamic (MHD) simulation of the formation of the Milky Way, augment it with a novel star cluster population synthesis model for a more realistic structure of the local interstellar medium, and perform detailed polarized radiative transfer calculations on the resulting model. This yields a faithful first principles prediction of the Faraday sky as observed on Earth. The results reproduce the observations of the Galaxy not only on global scales, but also on local scales of individual star-forming clouds. They also imply that the Local Bubble containing our Sun dominates the RM signal over large regions of the sky. Modern cosmological MHD simulations of the Milky Way's formation, combined with a simple and plausible model for the fraction of free electrons in the ISM, explain the RM observations remarkably well, thus indicating the emergence of a firm theoretical understanding of the genesis of magnetic fields in our Universe across cosmic time., Comment: 14 pages, 3 figures

We study the evolution of embedded star clusters as possible progenitors to reproduce 30 Doradus, specifically the compact star cluster known as R136 and its surrounding stellar family, which is believed to be part of an earlier star formation event. We employ the high-precision stellar dynamics code NBODY6++GPU to calculate the dynamics of the stars embedded in different evolving molecular clouds modelled by the 1D cloud/clusters evolution code WARPFIELD. We explore clouds with initial masses of $M_\text{cloud}=3.16 \times 10^{5}$ M$_\odot$ that (re)-collapse allowing for the birth of a second generation of stars. We explore different star formation efficiencies in order to find the best set of parameters that can reproduce the observation measurements. Our best-fit models correspond to a first stellar generation with masses between $1.26 \times 10^4$ - $2.85 \times 10^4 $M$_\odot$ and for the second generation we find a $M \approx 6.32\times 10^4$ M$_\odot$. Our models can match the observed stellar ages, cloud shell radius, and the fact that the second generation of stars is more concentrated than the first one. This is found independently of the cluster starting initially with mass segregation or not. By comparing our results with recent observational measurements of the mass segregation and density profile of the central zone we find close agreement, and thus provide supporting evidence for a centrally focused (re)-collapse origin to the multiple ages., Comment: 13 pages, 7 figures, 2 tables

We present the {\sc warpfield} emission predictor, {\sc warpfield-emp}, which couples the 1D stellar feedback code {\sc warpfield} with the {\sc cloudy} \hii region/PDR code and the {\sc polaris} radiative transfer code, in order to make detailed predictions for the time-dependent line and continuum emission arising from the H{\sc ii} region and PDR surrounding an evolving star cluster. {\sc warpfield-emp} accounts for a wide range of physical processes (stellar winds, supernovae, radiation pressure, gravity, thermal conduction, radiative cooling, dust extinction etc.) and yet runs quickly enough to allow us to explore broad ranges of different cloud parameters. We compare the results of an extensive set of models with SITELLE observations of a large sample of \hii regions in NGC~628 and find very good agreement, particularly for the highest signal-to-noise observations. We show that our approach of modeling individual clouds from first principles (instead of in terms of dimensionless quantities such as the ionization parameter) allows us to avoid long-standing degeneracies in the interpretation of \hii region diagnostics and enables us to relate these diagnostics to important physical parameters such as cloud mass or cluster age. Finally, we explore the implications of our models regarding the reliability of simple metallicity diagnostics, the properties of long-lived embedded clusters, and the role played by winds and supernovae in regulating \hii region and PDR line emission., Comment: 24 pages, 12 figures, submitted to MNRAS 18.09.2019

Star formation is an inefficient process and in general only a small fraction of the gas in a giant molecular cloud (GMC) is turned into stars. This is partly due to the negative effect of stellar feedback from young massive star clusters. In a recent paper, we introduced a novel 1D numerical treatment of the effects of stellar feedback from young massive clusters on their natal clouds, which we named WARPFIELD. Here, we present version 2 of the WARPFIELD code, containing improved treatments of the thermal evolution of the gas and the fragmentation of the feedback-driven shell. As part of this update, we have produced new cooling and heating tables that account for the combined effects of photoionization and collisional ionization on the cooling rate of the gas, which we now make publically available. We employ our updated version of WARPFIELD to investigate the impact of stellar feedback on GMCs with a broad range of masses and surface densities and a variety of density profiles. We show that the minimum star formation efficiency $\epsilon_{\mathrm{min}}$, i.e. the star formation efficiency above which the cloud is destroyed by feedback and further star formation is shut off, is mainly set by the average cloud surface density. A star formation efficiency of 1-6 % is generally sufficient to destroy a GMC. We also find star formation efficiencies per free-fall time $\epsilon_{\mathrm{ff}} \sim 0.3$ %, in good agreement with recent observations. Our results imply that stellar feedback alone is sufficient to explain the low observed star formation efficiencies of GMCs. Finally, we show that very massive clouds with steep density profiles - possible proxies of the giant clumps observed in galaxies at $z \approx 2$ - are more resilient to feedback than typical GMCs, with $\epsilon_{\mathrm{min}}$ between 1 and 12 %., Comment: 15 pages, 6 figures

In many tasks, in particular in natural science, the goal is to determine hidden system parameters from a set of measurements. Often, the forward process from parameter- to measurement-space is a well-defined function, whereas the inverse problem is ambiguous: one measurement may map to multiple different sets of parameters. In this setting, the posterior parameter distribution, conditioned on an input measurement, has to be determined. We argue that a particular class of neural networks is well suited for this task -- so-called Invertible Neural Networks (INNs). Although INNs are not new, they have, so far, received little attention in literature. While classical neural networks attempt to solve the ambiguous inverse problem directly, INNs are able to learn it jointly with the well-defined forward process, using additional latent output variables to capture the information otherwise lost. Given a specific measurement and sampled latent variables, the inverse pass of the INN provides a full distribution over parameter space. We verify experimentally, on artificial data and real-world problems from astrophysics and medicine, that INNs are a powerful analysis tool to find multi-modalities in parameter space, to uncover parameter correlations, and to identify unrecoverable parameters.

The 30 Doradus Nebula in the Large Magellanic Cloud (LMC) contains the massive starburst cluster NGC 2070 with a massive and probably younger stellar sub clump at its center: R136. It is not clear how such a massive inner cluster could form several million years after the older stars in NGC 2070, given that stellar feedback is usually thought to expel gas and inhibit further star formation. Using the recently developed 1D feedback scheme WARPFIELD to scan a large range of cloud and cluster properties, we show that an age offset of several million years between the stellar populations is in fact to be expected given the interplay between feedback and gravity in a giant molecular cloud (GMC) with a density $\gtrsim 500$ cm$^{-3}$ due to re-accretion of gas onto the older stellar population. Neither capture of field stars nor gas retention inside the cluster have to be invoked in order to explain the observed age offset in NGC 2070 as well as the structure of the interstellar medium (ISM) around it., Comment: 5 pages, 3 figures, accepted to MNRAS

Star clusters interact with the interstellar medium (ISM) in various ways, most importantly in the destruction of molecular star-forming clouds, resulting in inefficient star formation on galactic scales. On cloud scales, ionizing radiation creates \hii regions, while stellar winds and supernovae drive the ISM into thin shells. These shells are accelerated by the combined effect of winds, radiation pressure and supernova explosions, and slowed down by gravity. Since radiative and mechanical feedback is highly interconnected, they must be taken into account in a self-consistent and combined manner, including the coupling of radiation and matter. We present a new semi-analytic one-dimensional feedback model for isolated massive clouds ($\geq 10^5\,M_{\odot}$) to calculate shell dynamics and shell structure simultaneously. It allows us to scan a large range of physical parameters (gas density, star formation efficiency, metallicity) and to estimate escape fractions of ionizing radiation $f_{\rm{esc,i}}$, the minimum star formation efficiency $\epsilon_{\rm{min}}$ required to drive an outflow, and recollapse time scales for clouds that are not destroyed by feedback. Our results show that there is no simple answer to the question of what dominates cloud dynamics, and that each feedback process significantly influences the efficiency of the others. We find that variations in natal cloud density can very easily explain differences between dense-bound and diffuse-open star clusters. We also predict, as a consequence of feedback, a $4-6$ Myr age difference for massive clusters with multiple generations., Comment: 17 pages, 11 figures (additional 4 pages and 33 figures in appendix), Accepted MNRAS June 15, 2017