This repository contains the cosmic web catalog data released in the paper "Cosmic Web Catalog on SDSS-IV Data with SCONCE" (preparing).
\n\nThe catalog is constructed on the SDSS-IV galaxies and quasars (QSO) using our proposed Directional Subspace Constrained Mean Shift (DirSCMS) algorithm. We release both the cosmic filaments and local modes (i.e., local maxima of the estimated galaxy/QSO density field, which serves as candidates of galaxy clusters) within 325 thin redshift slices, each of which spans 20Mpc under the Planck15 cosmology. The entire catalog covers the redshift range from \\(z=0\\) to \\(z=3\\).
\n\n1. "Cosmic_filaments_2D_DirSCMS_new1": The file contains some discrete realizations of the estimated cosmic filaments in some particular redshift slices. The meaning of each column in the file is described as follows:
\n\n2. "Cosmic_local_modes_2D_DirMS_unique1": The file contains some discrete realizations of the estimated local modes in some particular redshift slices. The columns are subsumed by the ones in the cosmic filament file and has been described above.
\n\nAdditional notes: We provide both the "csv" and "fits" format for each of the above file.
\n\n\n\n
Please cite the paper when using the data in this repository.
\n\n\u25ba Pure catalog data:
\n\n[1] Cosmic Web Catalog on SDSS-IV Data with SCONCE. (In preparation)
\n\n\u25baMethodology:
\n\n[1] Yikun Zhang, Rafael S. de Souza, and Yen-Chi Chen (2022). SCONCE: A Cosmic Web Finder for Spherical and Conic Geometries. arXiv preprint arXiv:2207.07001
\n\n[2] Yikun Zhang and Yen-Chi Chen (2022) Linear Convergence of the Subspace Constrained Mean Shift Algorithm: From Euclidean to Directional Data. Information and Inference: A Journal of the IMA, iaac005, https://doi.org/10.1093/imaiai/iaac005
\n\n[3] Yikun Zhang and Yen-Chi Chen (2021) Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data. Journal of Machine Learning Research 22(154): 1-92.
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