Publication Abstracts

Way et al. 2011

Way, M.J., P.R. Gazis, and J.D. Scargle, 2011: Structure in the 3D galaxy distribution: I. Methods and example results. Astrophys. J., 727, 48, doi:10.1088/0004-637X/727/1/48.

Three methods for detecting and characterizing structure in point data, such as that generated by redshift surveys, are described: classification using self-organizing maps, segmentation using Bayesian blocks, and density estimation using adaptive kernels. The first two methods are new, and allow detection and characterization of structures of arbitrary shape and at a wide range of spatial scales. They elucidate not only clusters, but also sheets, filaments, and the even more general morphologies comprising the Cosmic Web. The methods are demonstrated and compared in application to three data sets: a carefully selected volume-limited sample from the Sloan Digital Sky Survey (SDSS) redshift data, a similarly selected sample from the Millennium Simulation, and a set of points independently drawn from a uniform probability distribution — a so-called Poisson distribution. We demonstrate a few of the many ways in which these methods elucidate large scale structure in the distribution of galaxies in the nearby Universe.

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BibTeX Citation

  author={Way, M. J. and Gazis, P. R. and Scargle, J. D.},
  title={Structure in the 3D galaxy distribution: I. Methods and example results},
  journal={Astrophys. J.},

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RIS Citation

ID  - wa07300k
AU  - Way, M. J.
AU  - Gazis, P. R.
AU  - Scargle, J. D.
PY  - 2011
TI  - Structure in the 3D galaxy distribution: I. Methods and example results
JA  - Astrophys. J.
VL  - 727
SP  - 48
DO  - 10.1088/0004-637X/727/1/48
ER  -

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