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SPECTRAL LINE DE-CONFUSION IN AN INTENSITY MAPPING SURVEY

Journal Article · · Astrophysical Journal
; ;  [1];  [2];  [3]
  1. California Institute of Technology, 1200 E. California Boulevard, Pasadena, CA 91125 (United States)
  2. Institute of Astronomy and Astrophysics, Academia Sinica, 1 Roosevelt Road, Section 4, Taipei, 10617, Taiwan (China)
  3. Department of Physics and Astronomy, University of California, Irvine, CA 92697 (United States)
Spectral line intensity mapping (LIM) has been proposed as a promising tool to efficiently probe the cosmic reionization and the large-scale structure. Without detecting individual sources, LIM makes use of all available photons and measures the integrated light in the source confusion limit to efficiently map the three-dimensional matter distribution on large scales as traced by a given emission line. One particular challenge is the separation of desired signals from astrophysical continuum foregrounds and line interlopers. Here we present a technique to extract large-scale structure information traced by emission lines from different redshifts, embedded in a three-dimensional intensity mapping data cube. The line redshifts are distinguished by the anisotropic shape of the power spectra when projected onto a common coordinate frame. We consider the case where high-redshift [C ii] lines are confused with multiple low-redshift CO rotational lines. We present a semi-analytic model for [C ii] and CO line estimates based on the cosmic infrared background measurements, and show that with a modest instrumental noise level and survey geometry, the large-scale [C ii] and CO power spectrum amplitudes can be successfully extracted from a confusion-limited data set, without external information. We discuss the implications and limits of this technique for possible LIM experiments.
OSTI ID:
22660981
Journal Information:
Astrophysical Journal, Journal Name: Astrophysical Journal Journal Issue: 2 Vol. 832; ISSN ASJOAB; ISSN 0004-637X
Country of Publication:
United States
Language:
English

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