Simon Scheidegger

Gravitational waves from 3D MHD core collapse simulations

We present the gravitational wave analyses from rotating (model s15g) and nearly non-rotating (model s15h) 3D MHD core collapse supernova simulations at bounce and during the first couple of ten milliseconds afterwards. The simulations are launched from 15 M� progenitor models stemming from stellar-evolution calculations. Gravity is implemented by a spherically symmetric effective general relativistic potential. The input physics uses the Lattimer-Swesty equation of state for hot, dense matter and a neutrino parametrisation scheme that is accurate until the first few ms after bounce. The 3D simulations allow us to study features already known from 2D simulations, as well as nonaxisymmetric effects. In agreement with recent results, we find only type I gravitational wave signals at core bounce. In the later stage of the simulations, one of our models (s15g) shows nonaxisymmetric gravitational wave emission caused by a low T/|W| dynamical instability, while the other model radiates gravitational waves due to a convective instability in the protoneutron star. The total energy released in gravitational waves within the considered time intervals is 1.52 × 10 −7 M� (s15g) and 4.72 × 10 −10 M� (s15h). Both core collapse simulations indicate that corresponding events in our Galaxy would be detectable either by the LIGO or Advanced LIGO detector.

The influence of model parameters on the prediction of gravitational wave signals from stellar core collapse

We present a gravitational wave (GW) analysis of an extensive series of three-dimensional magnetohydrodynamical core-collapse simulations. Our 25 models are based on a 15 M ⊙ progenitor stemming from (i) stellar evolution calculations; (ii) a spherically symmetric effective general relativistic potential, either the Lattimer-Swesty (with three possible compressibilities) or the Shen equation of state for hot, dense matter; and (iii) a neutrino parametrisation scheme that is accurate until about 5 ms postbounce. For three representative models, we also included long-term neutrino physics by means of a leakage scheme, which is based on partial implementation of the isotropic diffusion source approximation (IDSA). We systematically investigated the effects of the equation of state, the initial rotation rate, and both the toroidal and the poloidal magnetic fields on the GW signature. We stress the importance of including of postbounce neutrino physics, since it quantitatively alters the GW signature. Slowly rotating models, or those that do not rotate at all, show GW emission caused by prompt and proto-neutron star (PNS) convection. Moreover, the signal stemming from prompt convection allows for the distinction between the two different nuclear equations of state indirectly by different properties of the fluid instabilities. For simulations with moderate or even fast rotation rates, we only find the axisymmetric type I wave signature at core bounce. In line with recent results, we could confirm that the maximum GW amplitude scales roughly linearly with the ratio of rotational to gravitational energy at core bounce below a threshold value of about 10%. We point out that models set up with an initial central angular velocity of 2π rad s -1 or faster show nonaxisymmetric narrow-band GW radiation during the postbounce phase. This emission process is caused by a low T/|W| dynamical instability. Apart from these two points, we show that it is generally very difficult to discern the effects of the individual features of the input physics in a GW signal from a rotating core-collapse supernova that can be attributed unambiguously to a specific model. Weak magnetic fields do not notably influence the dynamical evolution of the core and thus the GW emission. However, for strong initial poloidal magnetic fields (≳ 10 12 G), the combined action of flux-freezing and field winding leads to conditions where the ratio of magnetic field pressure to matter pressure reaches about unity which leads to the onset of a jet-like supernova explosion. The collimated bipolar out-stream of matter is then reflected in the emission of a type IV GW signal. In contradiction to axisymmetric simulations, we find evidence that nonaxisymmetric fluid modes can counteract or even suppress jet formation for models with strong initial toroidal magnetic fields. The results of models with continued neutrino emission show that including of the deleptonisation during the postbounce phase is an indispensable issue for the quantitative prediction of GWs from core-collapse supernovae, because it can alter the GW amplitude up to a factor of 10 compared to a pure hydrodynamical treatment. Our collapse simulations indicate that corresponding events in our Galaxy would be detectable either by LIGO, if the source is rotating, or at least by the advanced LIGO detector, if it is not or only slowly rotating.

FISH: A THREE-DIMENSIONAL PARALLEL MAGNETOHYDRODYNAMICS CODE FOR ASTROPHYSICAL APPLICATIONS

FISH is a fast and simple ideal magnetohydrodynamics code that scales to ~10,000 processes for a Cartesian computational domain of ~10003 cells. The simplicity of FISH has been achieved by the rigorous application of the operator splitting technique, while second-order accuracy is maintained by the symmetric ordering of the operators. Between directional sweeps, the three-dimensional data are rotated in memory so that the sweep is always performed in a cache-efficient way along the direction of contiguous memory. Hence, the code only requires a one-dimensional description of the conservation equations to be solved. This approach also enables an elegant novel parallelization of the code that is based on persistent communications with MPI for cubic domain decomposition on machines with distributed memory. This scheme is then combined with an additional OpenMP parallelization of different sweeps that can take advantage of clusters of shared memory. We document the detailed implementation of a second-order total variation diminishing advection scheme based on flux reconstruction. The magnetic fields are evolved by a constrained transport scheme. We show that the subtraction of a simple estimate of the hydrostatic gradient from the total gradients can significantly reduce the dissipation of the advection scheme in simulations of gravitationally bound hydrostatic objects. Through its simplicity and efficiency, FISH is as well suited for hydrodynamics classes as for large-scale astrophysical simulations on high-performance computer clusters. In preparation for the release of a public version, we demonstrate the performance of FISH in a suite of astrophysically orientated test cases.fish is a fast and simple ideal magneto-hydrodynamics code that s cales to∼ 10 000 processes for a Cartesian computational domain of ∼ 10003 cells. The simplicity ofish has been achieved by the rigorous application of the operator splitting technique, while second order acc ura y is maintained by the symmetric ordering of the operators. Between directional sweeps, the three-di m nsional data is rotated in memory so that the sweep is always performed in a cache-e fficient way along the direction of contiguous memory. Hence, t h code only requires a one-dimensional description of the con servation equations to be solved. This approach also enable an elegant novel parallelisation of the code tha t is based on persistent communications with MPI for cubic domain decomposition on machines with distrib uted memory. This scheme is then combined with an additional OpenMP parallelisation of di fferent sweeps that can take advantage of clusters of shared memory. We document the detailed implementation of a second order TVD advection scheme based on flux reconstruction. The magnetic fields are evolved by a cons trai ed transport scheme. We show that the subtraction of a simple estimate of the hydrostatic gradien t from the total gradients can significantly reduce the dissipation of the advection scheme in simulations of gr avitationally bound hydrostatic objects. Through its simplicity and efficiency,fish is as well-suited for hydrodynamics classes as for large-sc ale astrophysical simulations on high-performance computer clusters. In pre paration for the release of a public version, we demonstrate the performance of fish in a suite of astrophysically orientated test cases.

Using Adaptive Sparse Grids to Solve High-Dimensional Dynamic Models

We present a flexible and scalable method for computing global solutions of high‐dimensional stochastic dynamic models. Within a time iteration or value function iteration setup, we interpolate functions using an adaptive sparse grid algorithm. With increasing dimensions, sparse grids grow much more slowly than standard tensor product grids. Moreover, adaptivity adds a second layer of sparsity, as grid points are added only where they are most needed, for instance, in regions with steep gradients or at nondifferentiabilities. To further speed up the solution process, our implementation is fully hybrid parallel, combining distributed and shared memory parallelization paradigms, and thus permits an efficient use of high‐performance computing architectures. To demonstrate the broad applicability of our method, we solve two very different types of dynamic models: first, high‐dimensional international real business cycle models with capital adjustment costs and irreversible investment; second, multiproduct menu‐cost models with temporary sales and economies of scope in price setting.

Gravitational waves from supernova matter

We have performed a set of 11 three-dimensional magnetohydrodynamical (MHD) core-collapse supernova simulations in order to investigate the dependences of the gravitational wave signal on the progenitors initial conditions. We study the effects of the initial central angular velocity and different variants of neutrino transport. Our models are started up from a 15M⊙ progenitor and incorporate an effective general relativistic gravitational potential and a finite temperature nuclear equation of state. Furthermore, the electron flavour neutrino transport is tracked by efficient algorithms for the radiative transfer of massless fermions. We find that non- and slowly rotating models show gravitational wave emission due to prompt- and lepton driven convection that reveals details about the hydrodynamical state of the fluid inside the protoneutron stars. Furthermore we show that protoneutron stars can become dynamically unstable to rotational instabilities at T/|W| values as low as ~2% at core bounce. We point out that the inclusion of deleptonization during the postbounce phase is very important for the quantitative gravitational wave (GW) prediction, as it enhances the absolute values of the gravitational wave trains up to a factor of ten with respect to a lepton-conserving treatment.

Scalable high-dimensional dynamic stochastic economic modeling

We present a highly parallelizable and flexible computational method to solve high-dimensional stochastic dynamic economic models. Solving such models often requires the use of iterative methods, like time iteration or dynamic programming. By exploiting the generic iterative structure of this broad class of economic problems, we propose a parallelization scheme that favors hybrid massively parallel computer architectures. Within a parallel nonlinear time iteration framework, we interpolate policy functions partially on GPUs using an adaptive sparse grid algorithm with piecewise linear hierarchical basis functions. GPUs accelerate this part of the computation one order of magnitude thus reducing overall computation time by 50%. The developments in this paper include the use of a fully adaptive sparse grid algorithm and the use of a mixed MPI-Intel TBB-CUDA/Thrust implementation to improve the interprocess communication strategy on massively parallel architectures. Numerical experiments on “Piz Daint” (Cray XC30) at the Swiss National Supercomputing Centre show that high-dimensional international real business cycle models can be efficiently solved in parallel. To the best of our knowledge, this performance on a massively parallel petascale architecture for such nonlinear high-dimensional economic models has not been possible prior to present work.