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- W20226475 abstract "We apply ‘core sampling’ analysis to a standard CDM N-body simulation, in which ‘galaxies’ are identified as high density peaks in the initial density field. The main concern of this paper is in the general insight the model provides into the possible relationship between dark matter (DM) and ‘galaxy’ large-scale structures at the present epoch. The analysis finds the dark matter (DM) distribution to be dominated by a hierarchical distribution in richness of sheetlike structures, i.e. of Zel’dovich DM pancakes, over scales of ∼ 10-100h Mpc. On the other hand, the ‘galaxy’ large-scale structure is found to be bimodal, with the poorer structure dominated by filaments and the richer structure dominated by sheetlike elements. The analysis also estimates a mean separation or characteristic scale for ‘galaxy’ filaments of ≈ 10.9 ± 0.7h Mpc. With such a modest scale size we identify them as elements of ‘Large-Scale Structure’ or LSS. A natural interpretation of this difference in the ‘morphology’ of DMLSS from that of ‘galaxy’ LSS is that galaxy filaments may be expected to trace the high density ridges of DM Zel’dovich pancakes. The rich ‘galaxy’ sheet-like structures are found to have a mean separation of ≈ 75-80h Mpc. We call this ‘Superlarge-Scale Structure’ or SLSS. Also, as large a fraction as≈ 60% of the ‘galaxies’ are in SLSS. These are all in good agreement with the corresponding observational results. However, this particular simulation was not found to possess the stable rich filamentary population seen in the observations. Nevertheless, the above results show that the form of the ‘galaxy’ spatial distribution has, on the whole, many similarities with that of the observed population, yet that of the DM distribution is distinctly different. The simulation thus provides an example of the profound effect biasing could have on the form of the observed large-scale structure in the Universe." @default.
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- W20226475 title "BIASING AND LARGE-SCALE STRUCTURE IN A STANDARD CDM N-BODY SIMULATION" @default.
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