Filament and Shape Statistics: A Quantitative Comparison of Cold + Hot   and Cold Dark Matter Cosmologies vs. CfA1 Data
  A new class of geometric statistics for analyzing galaxy catalogs is presented. Filament statistics quantify filamentarity and planarity in large scale structure in a manner consistent with catalog visualizations. These statistics are based on sequences of spatial links which follow local high-density structures. From these link sequences we compute the discrete curvature, planarity, and torsion. Filament statistics are applied to CDM and CHDM ($\Omega_\nu = 0.3$) simulations of Klypin \etal (1996), the CfA1-like mock redshift catalogs of Nolthenius, Klypin and Primack (1994, 1996), and the CfA1 catalog. We also apply the moment-based shape statistics developed by Babul \& Starkman (1992), Luo \& Vishniac (1995), and Robinson \& Albrecht (1996) to these same catalogs, and compare their robustness and discriminatory power versus filament statistics. For 100 Mpc periodic simulation boxes ($H_0 = 50$ km s$^{-1}$ Mpc$^{-1}$), we find discrimination of $\sim 4\sigma$ (where $\sigma$ represents resampling errors) between CHDM and CDM for selected filament statistics and shape statistics, including variations in the galaxy identification scheme. Comparing the CfA1 data versus the models does not yield a conclusively favored model; no model is excluded at more than a $\sim 2\sigma$ level for any statistic, not including cosmic variance which could further degrade the discriminatory power. We find that CfA1 discriminates between models poorly mainly due to its sparseness and small number of galaxies, not due to redshift distortion, magnitude limiting, or geometrical effects. We anticipate that the proliferation of large redshift surveys and simulations will enable the statistics presented here to provide robust discrimination between large-scale structure in various cosmological models. 
Romeel Dav\'e (UCSC), Doug Hellinger (UCSC), Joel Primack (UCSC),   Richard Nolthenius (UCSC) and Anatoly Klypin (NMSU)