The Role of Ambipolar Di ff usion in GMC Collisions Duncan Christie Department of Astronomy, University of Virginia Jonathan Tan Benjamin Wu University of Virginia National Astronomical Observatory of Japan Chalmers University of Technology
Cloud Collisions Outline Introductory Rambling about Cloud Collisions Ambipolar Di ff usion and the Resistivity Model Simulation Results Summary Why? Cloud collisions provide a mechanism to quickly assemble large amounts of gas. If they occur frequently enough, they may play a role in determining the star formation rate.
Cloud Collisions and Mergers Tasker & Tan (2009) performed simulations of GMC formation and evolution in disks with flat rotation curves, and found a typical cloud undergoes a merger or collision every 1/5 of an orbital time. Li+ (2017) found collisions and mergers to be even more Tasker & Tan (2009) Σ frequent, occurring on the order of every 0.1 to 0.2 of an orbital time. Dobbs+ (2014) found very frequent collision and mergers within their simulations (1 every 1/15 of an orbit for simulations with a spiral potential); however, it is unclear how many of these are the more violent collisions envisioned Li et al. (2017) The estimates for the collision rates requires that the clouds be su ffi ciently long-lived (i.e., bound, and not disrupted by internal star formation). None of these simulations contain contain feedback/star formation which could shorten cloud lifetimes. Dobbs et al. (2014)
Cloud Collisions: Observations Identifying potential interacting clouds is di ffi cult, and is primarily done by looking for bridge features in PV and PPV diagrams. e.g., Fujita et al. (2017) , Furukawa et al. (2009), Fukui+ (2014) Bisbas+ (2017) has argued that CII 158 μ m emission may contain bridge features as the source is the cloud edges/ ambient medium. This is useful as CO lines may not always show indication of the collision. Thomas has a poster here describing this work. W51A, Fujita et al. (2017) The clouds Westerlund 2 and RGC3603 both have stellar clusters associated with them, supporting the idea that cloud collisions may be involved in the cluster formation process Fukui et al. (2014)
Modeling Cloud Collisions x [pc] t =0.6 Myr 1 Colliding Flows 0.60 Myr 0.9 0.8 0.7 Although not exactly colliding clouds, can local 0.6 z [pc] Z [pc] 0.5 simulation of part of a collision, specifically the 0.4 0.3 initial interface between the clouds (with appropriate 0.2 0.1 pre-shock conditions). 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x [pc] X [pc] Chen & Ostriker (2014) Global Simulations of Colliding Clouds The other option is to model two entire clouds colliding. You’re modeling larger scales, requiring larger simulations. The parameter space to be investigated is quite large, with di ff erent possibilities for the cloud masses, impact parameter, Shima et al. (2017) magnetic fields, etc. We opt to investigate the case of two colliding giant molecular clouds of equal mass. Balfour et al. (2017)
GMC COLLISIONS AS TRIGGERS OF STAR FORMATION A Never Ending Series of Papers 2D Investigation of Collisions GMC Collisions as Triggers of Star Formation. I. Parameter Space Exploration with 2D Simulations Benjamin Wu, Sven Van Loo, Jonathan C. Tan, Simon Bruderer 3D Investigation of Collisions GMC Collisions as Triggers of Star Formation. II. 3D Turbulent, Magnetized Simulations Benjamin Wu, Jonathan C. Tan, Fumitaka Nakamura, Sven Van Loo, Duncan Christie, David Collins Star Particle Formation Prescriptions GMC Collisions as Triggers of Star Formation. III. Density and Magnetically Regulated Star Formation Benjamin Wu, Jonathan C. Tan, Duncan Christie, Fumitaka Nakamura, Sven Van Loo, David Collins Ambipolar Di ff usion GMC Collisions As Triggers of Star Formation. IV. The Role of Ambipolar Diffusion Duncan Christie, Benjamin Wu, Jonathan C. Tan Synthetic Observations GMC Collisions as Triggers of Star Formation. V. Observational Signatures Thomas G. Bisbas, Kei E. I. Tanaka, Jonathan C. Tan, Benjamin Wu, Fumitaka Nakamura Internal Turbulence GMC Collisions as Triggers of Star Formation. VI. Collision-Induced Turbulence Benjamin Wu, Jonathan C. Tan, Fumitaka Nakamura, Duncan Christie, Qi Li
Initial Conditions and Parameter Study To study the role of ambipolar diffusion, ideal MHD and non-ideal MHD runs with otherwise identical parameters. v = -5 km/s v = +5 km/s Physical Parameters Cloud Mass-to-flux ratio: 3.8 B=10 uG Cloud mass: 9.3 x 10 4 M sun c p 0 2 Cloud turbulence with M s = 23 n H = 100 cm -3 Numerical Parameters M=1.1x10 5 M ☉ 128 3 pc 3 box with a top grid of resolution 128 3 4 levels of refinement, resolving the Jeans length by 8 zones Smallest grid: Δ x = 0.0625 pc
Chemistry • Chemical model developed in Wu+ (2015) including 31 chemical species • Using PyPDR and CLOUDY , equilibrium chemical abundances are calculated for each value of gas density and temperature. • UV shielding e ff ects included by assuming A V = f(n H ) • This is used to calculate heating and cooling rates for the gas. Limitations • The model doesn’t allow for non-equilibrium chemistry. • UV plays an important role in determining where molecules can form. The fixed prescription for A V precludes variability in where this transition occurs. Equilibrium Temperature Curve Actual Simulation Result
Chemistry • Chemical model developed in Wu+ (2015) including 31 chemical species • Using PyPDR and CLOUDY , equilibrium chemical abundances are calculated for each value of gas density and temperature. • UV shielding e ff ects included by assuming A V = f(n H ) • This is used to calculate heating and cooling rates for the gas. Limitations • The model doesn’t allow for non-equilibrium chemistry. • UV plays an important role in determining where molecules can form. The fixed prescription for A V precludes variability in where this transition occurs. Chemical Abundances at Equilibrium Temperature (Only most important ions and neutrals shown)
Collision Evolution: The Movie The fiducial 10 μ G colliding case with ambipolar di ff usion. Non-Colliding Non-Colliding
Qualitative Results from Ideal Simulations A large parameter study using this setup was performed in Wu+ (2016) in the ideal MHD limit. Collision Speed • Slower collisions (5 km/s) develop more slowly, with a less high density gas and a more extended morphology. • Faster collisions (20 km/s) result in more localized, denser objects which develop more quickly. Higher temperatures are observed around the shocked regions. Field Orientation • For fields parallel to the collision axis ( 𝜾 =0 ° ), gas can easily move along the field lines, resulting in faster collapse and • A Perpendicular field ( 𝜾 =90 ° ) results in a build up of magnetic pressure, slowing collapse.
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