اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدAn adaptively refined phase-space element method for cosmological simulations and collisionless dynamics
197
0
0.0
(
0
)
تأليف
Oliver Hahn
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
N-body simulations are essential for understanding the formation and evolution of structure in the Universe. However, the discrete nature of these simulations affects their accuracy when modelling collisionless systems. We introduce a new approach to simulate the gravitational evolution of cold collisionless fluids by solving the Vlasov-Poisson equations in terms of adaptively refineable Lagrangian phase space elements. These geometrical elements are piecewise smooth maps between Lagrangian space and Eulerian phase space and approximate the continuum structure of the distribution function. They allow for dynamical adaptive splitting to accurately follow the evolution even in regions of very strong mixing. We discuss in detail various one-, two- and three-dimensional test problems to demonstrate the performance of our method. Its advantages compared to N-body algorithms are: i) explicit tracking of the fine-grained distribution function, ii) natural representation of caustics, iii) intrinsically smooth gravitational potential fields, thus iv) eliminating the need for any type of ad-hoc force softening. We show the potential of our method by simulating structure formation in a warm dark matter scenario. We discuss how spurious collisionality and large-scale discreteness noise of N-body methods are both strongly suppressed, which eliminates the artificial fragmentation of filaments. Therefore, we argue that our new approach improves on the N-body method when simulating self-gravitating cold and collisionless fluids, and is the first method that allows to explicitly follow the fine-grained evolution in six-dimensional phase space.
قيم البحث
اقرأ أيضاً
Cosmological N-body simulations represent an excellent tool to study the formation and evolution of dark matter (DM) halos and the mechanisms that have originated the universal profile at the largest mass scales in the Universe. In particular, the co
mbination of the velocity dispersion $sigma_mathrm{v}$ with the density $rho$ can be used to define the pseudo-entropy $S(r)=sigma_mathrm{v}^2/rho^{,2/3}$, whose profile is well-described by a simple power-law $Spropto,r^{,alpha}$. We analyze a set of cosmological hydrodynamical re-simulations of massive galaxy clusters and study the pseudo-entropy profiles as traced by different collisionless components in simulated galaxy clusters: DM, stars, and substructures. We analyze four sets of simulations, exploring different resolution and physics (N-body and full hydrodynamical simulations) to investigate convergence and the impact of baryons. We find that baryons significantly affect the inner region of pseudo-entropy profiles as traced by substructures, while DM particles profiles are characterized by an almost universal behavior, thus suggesting that the level of pseudo-entropy could represent a potential low-scatter mass-proxy. We compare observed and simulated pseudo-entropy profiles and find good agreement in both normalization and slope. We demonstrate, however, that the method used to derive observed pseudo-entropy profiles could introduce biases and underestimate the impact of mergers. Finally, we investigate the pseudo-entropy traced by the stars focusing our interest in the dynamical distinction between intracluster light (ICL) and the stars bound to the brightest cluster galaxy (BCG): the combination of these two pseudo-entropy profiles is well-described by a single power-law out to almost the entire cluster virial radius.
We present an implementation of smoothed particle hydrodynamics (SPH) with improved accuracy for simulations of galaxies and the large-scale structure. In particular, we combine, implement, modify and test a vast majority of SPH improvement technique
s in the latest instalment of the GADGET code. We use the Wendland kernel functions, a particle wake-up time-step limiting mechanism and a time-dependent scheme for artificial viscosity, which includes a high-order gradient computation and shear flow limiter. Additionally, we include a novel prescription for time-dependent artificial conduction, which corrects for gravitationally induced pressure gradients and largely improves the SPH performance in capturing the development of gas-dynamical instabilities. We extensively test our new implementation in a wide range of hydrodynamical standard tests including weak and strong shocks as well as shear flows, turbulent spectra, gas mixing, hydrostatic equilibria and self-gravitating gas clouds. We jointly employ all modifications; however, when necessary we study the performance of individual code modules. We approximate hydrodynamical states more accurately and with significantly less noise than standard SPH. Furthermore, the new implementation promotes the mixing of entropy between different fluid phases, also within cosmological simulations. Finally, we study the performance of the hydrodynamical solver in the context of radiative galaxy formation and non-radiative galaxy cluster formation. We find galactic disks to be colder, thinner and more extended and our results on galaxy clusters show entropy cores instead of steadily declining entropy profiles. In summary, we demonstrate that our improved SPH implementation overcomes most of the undesirable limitations of standard SPH, thus becoming the core of an efficient code for large cosmological simulations.
We present maps revealing the expected information content of cosmic large-scale structures concerning cosmological physics. These maps can guide the optimal retrieval of relevant physical information with targeted cosmological searches. This approac
h has become feasible through the recent development of causal inference machinery that is informed on the physics of cosmic structure formation. Specifically, we measure the response of observed cosmic structures to perturbative changes in the cosmological model and chart their respective contributions to the Fisher information. Our physical forward modeling machinery transcends the limitations of contemporary analyses based on statistical summaries to yield detailed characterizations of individual 3D structures. We showcase the potential of our approach by studying the information content of the Coma cluster. We find that regions in the vicinity of the filaments and cluster core, where mass accretion ensues from gravitational infall, are the most informative. The results presented in this work are the first of their kind and elucidate the inhomogeneous distribution of cosmological information in the Universe. This study paves a new way forward to perform efficient targeted searches for the fundamental physics of the Universe, where search strategies are progressively refined with new cosmological data sets within an active learning framework.
The diversity of structures in the Universe (from the smallest galaxies to the largest superclusters) has formed under the pull of gravity from the tiny primordial perturbations that we see imprinted in the cosmic microwave background. A quantitative
description of this process would require description of motion of zillions of dark matter particles. This impossible task is usually circumvented by coarse-graining the problem: one either considers a Newtonian dynamics of particles with macroscopically large masses or approximates the dark matter distribution with a continuous density field. There is no closed system of equations for the evolution of the matter density field alone and instead it should still be discretized at each timestep. In this work we describe a method of solving the full 6-dimensional Vlasov-Poisson equation via a system of auxiliary Schroedinger-like equations. The complexity of the problem gets shifted into the choice of the number and shape of the initial wavefunctions that should only be specified at the beginning of the computation (we stress that these wavefunctions have nothing to do with quantum nature of the actual dark matter particles). We discuss different prescriptions to generate the initial wave functions from the initial conditions and demonstrate the validity of the technique on two simple test cases. This new simulation algorithm can in principle be used on an arbitrary distribution function, enabling the simulation of warm and hot dark matter structure formation scenarios.
By means of high-resolution cosmological hydrodynamical simulations of Milky Way-like disc galaxies, we conduct an analysis of the associated stellar metallicity distribution functions (MDFs). After undertaking a kinematic decomposition of each simul
ation into spheroid and disc sub-components, we compare the predicted MDFs to those observed in the solar neighbourhood and the Galactic bulge. The effects of the star formation density threshold are visible in the star formation histories, which show a modulation in their behaviour driven by the threshold. The derived MDFs show median metallicities lower by 0.2-0.3 dex than the MDF observed locally in the disc and in the Galactic bulge. Possible reasons for this apparent discrepancy include the use of low stellar yields and/or centrally-concentrated star formation. The dispersions are larger than the one of the observed MDF; this could be due to simulated discs being kinematically hotter relative to the Milky Way. The fraction of low metallicity stars is largely overestimated, visible from the more negatively skewed MDF with respect to the observational sample. For our fiducial Milky Way analog, we study the metallicity distribution of the stars born in situ relative to those formed via accretion (from disrupted satellites), and demonstrate that this low-metallicity tail to the MDF is populated primarily by accreted stars. Enhanced supernova and stellar radiation energy feedback to the surrounding interstellar media of these pre-disrupted satellites is suggested as an important regulator of the MDF skewness.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
