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Title: THE CLUSTERING CHARACTERISTICS OF H I-SELECTED GALAXIES FROM THE 40% ALFALFA SURVEY

Journal Article · · Astrophysical Journal
 [1]; ;  [2];  [3]
  1. NASA Postdoctoral Program, NASA Langley Research Center, Hampton, VA 23618 (United States)
  2. Center for Radiophysics and Space Research, Space Sciences Building, Cornell University, Ithaca, NY 14853 (United States)
  3. INAF Osservatorio Astronomico di Brera, Milan (Italy)

The 40% Arecibo Legacy Fast ALFA survey catalog ({alpha}.40) of {approx}10,150 H I-selected galaxies is used to analyze the clustering properties of gas-rich galaxies. By employing the Landy-Szalay estimator and a full covariance analysis for the two-point galaxy-galaxy correlation function, we obtain the real-space correlation function and model it as a power law, {xi}(r) = (r/r{sub 0}){sup -{gamma}}, on scales <10 h{sup -1} Mpc. As the largest sample of blindly H I-selected galaxies to date, {alpha}.40 provides detailed understanding of the clustering of this population. We find {gamma} = 1.51 {+-} 0.09 and r{sub 0} = 3.3 + 0.3, -0.2 h{sup -1} Mpc, reinforcing the understanding that gas-rich galaxies represent the most weakly clustered galaxy population known; we also observe a departure from a pure power-law shape at intermediate scales, as predicted in {Lambda}CDM halo occupation distribution models. Furthermore, we measure the bias parameter for the {alpha}.40 galaxy sample and find that H I galaxies are severely antibiased on small scales, but only weakly antibiased on large scales. The robust measurement of the correlation function for gas-rich galaxies obtained via the {alpha}.40 sample constrains models of the distribution of H I in simulated galaxies, and will be employed to better understand the role of gas in environmentally dependent galaxy evolution.

OSTI ID:
22034587
Journal Information:
Astrophysical Journal, Vol. 750, Issue 1; Other Information: Country of input: International Atomic Energy Agency (IAEA); ISSN 0004-637X
Country of Publication:
United States
Language:
English