Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-29T03:56:27.030Z Has data issue: false hasContentIssue false
  • English
  • Français

Lyα Forest Tomography of the z > 2 Cosmic Web

Published online by Cambridge University Press:  12 October 2016

Khee-Gan Lee
Khee-Gan Lee*
Affiliation:
Max-Planck Institut für Astronomie, Königstuhl 17, Heidelberg, 69117, Germany
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

The hydrogen Lyα forest is an important probe of the z > 2 Universe that is otherwise challenging to observe with galaxy redshift surveys, but this technique has traditionally been limited to 1D studies in front of bright quasars. However, by pushing to faint magnitudes (g > 23) with 8-10m large telescopes it becomes possible to exploit the high area density of high-redshift star-forming galaxies to create 3D tomographic maps of large-scale structure in the foreground. I describe the first pilot observations using this technique, as well discuss future surveys and the resulting science possibilities for galaxy evolution and cosmology.

Keywords

cosmology: observations – galaxies: high-redshift – intergalactic medium – quasars: absorption lines – surveys – techniques: spectroscopic
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 11 , Symposium S308: The Zeldovich Universe: Genesis and Growth of the Cosmic Web , June 2014 , pp. 360 - 363
Copyright
Copyright © International Astronomical Union 2016 

References

Berry, M., Gawiser, E., Guaita, L., et al. 2012, ApJ, 749, 4 Google Scholar
Bi, H., Ge, J., & Fang, L.-Z. 1995, ApJ, 452, 90 Google Scholar
Caucci, S., Colombi, S., Pichon, C., et al. 2008, MNRAS, 386, 211 CrossRefGoogle Scholar
Cen, R., Miralda-Escudé, J., Ostriker, J. P., & Rauch, M. 1994, ApJL, 437, L9 CrossRefGoogle Scholar
Chiang, Y.-K., Overzier, R., & Gebhardt, K. 2013, ApJ, 779, 127 CrossRefGoogle Scholar
Cisewski, J., Croft, R. A. C., Freeman, P. E., et al. 2014, MNRAS, 440, 2599 Google Scholar
Delubac, T., Bautista, J. E., Busca, N. G., et al. 2014, ArXiv e-prints, arXiv:1404.1801Google Scholar
D'Odorico, V., Viel, M., Saitta, F., et al. 2006, MNRAS, 372, 1333 Google Scholar
Heckman, T. M., Borthakur, S., Overzier, R., et al. 2011, ApJ, 730, 5 Google Scholar
Le Fèvre, O., Saisse, M., Mancini, D., et al. 2003, in SPIE Conference Series, Vol. 4841, ed. Iye, M. & Moorwood, A. F. M., 1670–1681 Google Scholar
Lee, K.-G., Hennawi, J. F., White, M., Croft, R. A. C., & Ozbek, M. 2014a, ApJ, 788, 49 CrossRefGoogle Scholar
Lee, K.-G., Bailey, S., Bartsch, L. E., et al. 2013, AJ, 145, 69 CrossRefGoogle Scholar
Lee, K.-G., Hennawi, J. F., Stark, C., et al. 2014b, ArXiv e-prints, arXiv:1409.5632Google Scholar
Lilly, S. J., Le Fèvre, O., Renzini, A., et al. 2007, ApJS, 172, 70 Google Scholar
Palanque-Delabrouille, N., Magneville, C., Yèche, C., et al. 2013, A&A, 551, A29 Google Scholar
Pichon, C., Vergely, J. L., Rollinde, E., Colombi, S., & Petitjean, P. 2001, MNRAS, 326, 597 CrossRefGoogle Scholar
Reddy, N. A., Steidel, C. C., Pettini, M., et al. 2008, ApJS, 175, 48 Google Scholar
Savaglio, S., Panagia, N., & Padovani, P. 2002, ApJ, 567, 702 Google Scholar
Scoville, N., Aussel, H., Brusa, M., et al. 2007, ApJS, 172, 1 Google Scholar
Shapley, A. E., Steidel, C. C., Pettini, M., & Adelberger, K. L. 2003, ApJ, 588, 65 Google Scholar
Slosar, A., Font-Ribera, A., Pieri, M. M., et al. 2011, JCAP, 9, 1 Google Scholar
Slosar, A., Iršič, V., Kirkby, D., et al. 2013, JCAP, 4, 26 Google Scholar
Wiener, N. 1942, ‘Interpolation, extrapolation and smoothing of stationary time series’ (MIT)Google Scholar