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Mapping the Cosmic Web with the largest all-sky surveys

Published online by Cambridge University Press:  12 October 2016

Maciej Bilicki
Affiliation:
Department of Astronomy, University of Cape Town, South Africa, email: [email protected] Kepler Institute of Astronomy, University of Zielona Góra, Poland
John A. Peacock
Affiliation:
Institute for Astronomy, University of Edinburgh, United Kingdom
Thomas H. Jarrett
Affiliation:
Department of Astronomy, University of Cape Town, South Africa, email: [email protected]
Michelle E. Cluver
Affiliation:
Department of Astronomy, University of Cape Town, South Africa, email: [email protected]
Louise Steward
Affiliation:
Department of Astronomy, University of Cape Town, South Africa, email: [email protected]
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Abstract

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Our view of the low-redshift Cosmic Web has been revolutionized by galaxy redshift surveys such as 6dFGS, SDSS and 2MRS. However, the trade-off between depth and angular coverage limits a systematic three-dimensional account of the entire sky beyond the Local Volume (z < 0.05). In order to reliably map the Universe to cosmologically significant depths over the full celestial sphere, one must draw on multiwavelength datasets and state-of-the-art photometric redshift techniques. We have undertaken a dedicated program of cross-matching the largest photometric all-sky surveys – 2MASS, WISE and SuperCOSMOS – to obtain accurate redshift estimates of millions of galaxies. The first outcome of these efforts – the 2MASS Photometric Redshift catalog (2MPZ, Bilicki et al. 2014a) – has been publicly released and includes almost 1 million galaxies with a mean redshift of z=0.08. Here we summarize how this catalog was constructed and how using the WISE mid-infrared sample together with SuperCOSMOS optical data allows us to push to redshift shells of z∼ 0.2 –0.3 on unprecedented angular scales. Our catalogs, with ∼ 20 million sources in total, provide access to cosmological volumes crucial for studies of local galaxy flows (clustering dipole, bulk flow) and cross-correlations with the cosmic microwave background such as the integrated Sachs-Wolfe effect or lensing studies.

Type
Contributed Papers
Copyright
Copyright © International Astronomical Union 2016 

References

Ahn, C. P., Alexandroff, R., Allende Prieto, C., Anders, F., et al. 2014, ApJS, 211, 2 Google Scholar
Alonso, D., Bueno Belloso, A., Sánchez, F. J., García-Bellido, J., et al. 2014, MNRAS, 440, 1 Google Scholar
Appleby, S. & Shafieloo, A. 2014, arXiv:1405.4595 Google Scholar
Bilicki, M., Chodorowski, M., Jarrett, T., & Mamon, G. A. 2011, ApJ, 741, 1 CrossRefGoogle Scholar
Bilicki, M., Jarrett, T. H., Peacock, J. A., Cluver, M. E., & Steward, L. 2014, ApJS, 210, 9 Google Scholar
Bilicki, M., Peacock, J. A., Jarrett, T. H., Maddox, N., Steward, L., & Cluver, M. E. 2014, in prep.Google Scholar
Blake, C. & Bridle, S. 2005, MNRAS, 363, 4 Google Scholar
Branchini, E., Davis, M., & Nusser, A. 2012, MNRAS, 424, 1 Google Scholar
Cluver, M. E., Jarrett, T. H., Hopkins, A. M., Driver, S. P., Liske, J., et al. 2014, ApJ, 782, 2 CrossRefGoogle Scholar
Collister, A. A. & Lahav, O. 2004, PASP, 116, 818 Google Scholar
Driver, S. P., Norberg, P., Baldry, I. K., Bamford, S. P., Hopkins, A. M., et al. 2009, A&G, 50, 5 Google Scholar
Erdogdu, P., Huchra, J. P., Lahav, O., Colless, M., Cutri, R. M., et al. 2006, MNRAS, 368, 4 Google Scholar
Feix, M., Nusser, A., & Branchini, E. 2014, arXiv:1405.6710 CrossRefGoogle Scholar
Francis, C. L. & Peacock, J. A. 2010, MNRAS, 406, 1 Google Scholar
Giannantonio, T. & Percival, W. J. 2014, MNRAS, 441, 1 Google Scholar
Hambly, N. C., MacGillivray, H. T., Read, M. A., Tritton, S. B., et al. 2001, MNRAS, 326, 4 Google Scholar
Hirata, C. M. 2009, JCAP, 09, 011 Google Scholar
Huchra, J. P., Macri, L. M., Masters, K. L., Jarrett, T. H., Berlind, P., et al. 2012, ApJS, 199, 2 Google Scholar
Jarrett, T. H., Chester, T., Cutri, R., Schneider, S., Skrutskie, M., & Huchra, J. P. 2000, AJ, 119, 5 Google Scholar
Kashlinsky, A., Atrio-Barandela, F., Kocevski, D., & Ebeling, H. 2008, ApJ, 686, 2 Google Scholar
Kocevski, D. D. & Ebeling, H. 2006, ApJ, 645, 2 Google Scholar
Krakowski, T., et al. 2014, in prep.Google Scholar
Lavaux, G. & Hudson, M. J. 2011, MNRAS, 416, 4 Google Scholar
Małek, K., Solarz, A., Pollo, A., Fritz, A., Garilli, B., Scodeggio, M., et al. 2013, A&A, 557, A16 Google Scholar
Nusser, A., Branchini, E., & Davis, M. 2011, ApJ, 735, 2 CrossRefGoogle Scholar
Nusser, A. & Davis, M. 2011, ApJ, 736, 2 Google Scholar
Nusser, A., Davis, M., & Branchini, E. 2014, ApJ, 788, 2 Google Scholar
Planck Collaboration: Ade, P. A. R., Aghanim, N., et al. 2013, arXiv:1303.5083 Google Scholar
Pullen, A. R. & Hirata, C. M. 2010, JCAP, 05, 027 Google Scholar
Rowan-Robinson, M., Sharpe, J., Oliver, S. J., Keeble, O., et al. 2000, MNRAS, 314, 2 Google Scholar
Solarz, A., Pollo, A., Takeuchi, T. T., Pępiak, A., Matsuhara, H., et al. 2012, A&A, 541, A50 Google Scholar
Steward, L., Bilicki, M., Peacock, J. A., Jarrett, T. H., & Cluver, M. E. 2014, in prep.Google Scholar
Turnbull, S. J., Hudson, M. J., Feldman, H. A., Hicken, M., et al. 2012, MNRAS, 420, 1 Google Scholar
Watkins, R., Feldman, H. A., & Hudson, M. J. 2009, MNRAS, 392, 2 Google Scholar
Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., Ressler, M. E., et al. 2010, AJ, 140, 6 Google Scholar
Xu, W. W., Wen, Z. L., & Han, J. L. 2014, arXiv:1406.0943 Google Scholar