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Cosmological baryonic structure formation simulations at ECCA

(Staff: Avery Meiksin, Eric Tittley)

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The Lyman-α forest
CGM simulations
Baryonic power spectrum
Metagalactic UV background
IGM transmissivity
IGM metallicity
21cm signature


ECCA Research


Numerical simulations have played a key role in understanding the development of large-scale structure in the Universe, from the distribution of galaxies to the formation of galaxy clusters. Most of this work was accomplished with pure gravity codes. Only recently has it become possible to begin incorporating the needed physics to compute the formation of the objects themselves. Baryons are a vital component of galaxies. No complete understanding of the formation of the stars and planets within galaxies is possible without first understanding the history of the baryons since the Big Bang, for they formed the first generation of stars and continue to play a role in galactic star formation.

Intergalactic Medium simulations
Simulations show that most of the baryons in the Universe are dispersed throughout intergalactic space forming a filamentary network extending between galaxies. Their geometry has been compared to a cosmic web (Bond, Kofman & Pogosyan 1996). This gaseous network is the arena in which galaxies form and interact, not only by accreting the low density intergalactic material, but through feedback effects like supernovae and jets from Active Galactic Nuclei (AGN). The relationship between the galaxies and the Intergalactic Medium (IGM) is not a quiescent one, but a dynamic one with substantial exchange of mass and energy. As such, galaxies leave behind their imprint on the IGM during their formation, particularly in the temperature and metal content (such as C, N, O and heavier elements) of the IGM. The IGM is thus a valuable repository of the history of the early stages of galaxy formation.

Previous Numerical Methods Employed

Most of the early simulations of structure formation in the IGM have utilised gravity codes combined with hydrodynamics. The simulations of Zhang, Anninos & Norman (1995), Zhang, Anninos, Norman & Meiksin (1997) and Zhang, Meiksin, Anninos & Norman (1998) used a combined N-body Particle-Mesh (PM) code combined with a finite-difference hydrodynamics scheme on a fixed comoving grid (Anninos, Norman & Clarke 1994). Parallel simulations were performed by Cen, Miralda-Escude, Ostriker & Rauch (1994) using an alternative finite-difference scheme, and by Hernquist, Katz, Weinberg & Miralda-Escude (1996) and Theuns, Leonard & Efstathiou (1998) using Smoothed Particle Hydrodynamics (SPH), for solving the fluid equations. The SPH code has the advantage of resolving dense structures by automatically moving the fluid particles to the dense regions. The price paid is sparser resolution in regions that are of moderate to low density. Since most of the volume of the Universe contains baryons of moderate overdensities are less, a finite-difference scheme on a fixed comoving grid is more efficient at resolving the structures relevant to the bulk of the IGM. Finite-difference schemes also are an order of magnitude or more faster at achieving the same spatial resolution as an SPH code, provided there are no large density contrasts in the systems desired to be resolved. When there are, a more sophisticated finite-difference approach may be used based on Adaptive Mesh Refinement.

Solving the hydrodynamics equations along with the gravity, however, incurs a considerable computational overhead in computing time. Combined gravity and hydrodynamics simulations of the IGM have shown that the gas density is well modelled by equating the local gas cosmological overdensity to the local dark matter overdensity (Zhang et al. 1998). This suggests a more efficient approach in which only the gravity is solved for, and the gas density is scaled according to the dark matter overdensity (Gnedin & Hui 1998). Such an approach has the advantage of permitting many more simulations to be performed, allowing a broad range of cosmological parameters to be tried as well as allowing an assessment of the impact of additional effects like discreteness of the sources or varying equations of state of the gas. The method, however, works less well in underdense regions, is not exact, and does not allow for proper hydrodynamical reaction (pressure forces) to the photoionization heating, degrading the accuracy of the predictions (Meiksin & White 2001). For all these reasons, it is desirable for detailed computations and comparisons with observations to use a combined gravity-hydrodynamics code.

Current Numerical Methods

The new direction of structure formation in the IGM invovles simulations with the radiative transfer coupled to a gravity-hydrodynamics code. The latter is particularly important for tying the results to galaxy formation and small sub-galactic systems, many of which appear as Damped Lyman-α Absorbers. Simulations of Tittley & Meiksin (2007) and Meiksin, Tittley & Brown (2010) were performed using radiative transfer coupled to a pure gravity N-body Particle-Mesh (PM) code. The first IGM simulations with radiative transfer coupled to a hydrodynamics code (a true radiative-hydrodynamics computation) was reported by Meiksin & Tittley (2012). This simulation traced the back-reaction of gas clumps heated by helium reionization on the propagation of the helium ionization fronts themselves.

For further information....

Further information on the Lyα forest, and its connection to cosmology may be found here and to reionisation here. Review articles may be found here and here.


Anninos P., Norman M.L., Clarke D.A., 1994, ApJ, 436, 11

Bond J.R., Kofman L., Pogosyan D., 1996, Nature, 380, 603

Cen R., Miralda-Escude J., Ostriker J.P., Rauch M., 1994, ApJ, 437, L9

Gnedin N.Y., Hui L., 1998, MNRAS, 296, 44

Hernquist L., Katz N., Weinberg D.H., Miralda-Escude J., 1996, ApJ, 457, L51

Meiksin A., Tittley E.R., 2012, MNRAS, 423, 7

Meiksin A., Tittley E.R., Brown, C.K., 2010, MNRAS, 401, 77

Meiksin A., White M., 2001, MNRAS, 324, 141

Theuns T., Leonard A., Efstathiou G., 1998, MNRAS, 297, L49

Tittley E.R., Meiksin A., 2007, MNRAS, 380, 1369

Zhang Y., Anninos P., Norman M.L., 1995, ApJ, 453, L57

Zhang Y., Anninos P., Norman M.L., Meiksin A., 1997, ApJ, 485, 496

Zhang Y., Meiksin A., Anninos P., Norman M.L., 1998, ApJ, 495, 63

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