• Corpus ID: 118610375

Spectral Confusion for Cosmological Surveys of Redshifted CII Observations

@article{Kogut2015SpectralCF,
  title={Spectral Confusion for Cosmological Surveys of Redshifted CII Observations},
  author={Alan J. Kogut and Eli Dwek and Samuel H. Moseley},
  journal={arXiv: Astrophysics of Galaxies},
  year={2015}
}

• A. Kogut, E. Dwek, S. Moseley
• Published 1 May 2015
• Physics
• arXiv: Astrophysics of Galaxies

Far infrared cooling lines are ubiquitous features in the spectra of star forming galaxies. Surveys of redshifted fine-structure lines provide a promising new tool to study structure formation and galactic evolution at redshifts including the epoch of reionization as well as the peak of star formation. Unlike neutral hydrogen surveys, where the 21 cm line is the only bright line, surveys of red-shifted fine-structure lines suffer from confusion generated by line broadening, spectral overlap of different lines, and the crowding of sources with redshift. We use simulations to investigate the resulting spectral confusion and derive observing parameters to minimize these effects in pencil-beam surveys of red-shifted far-IR line emission. We generate simulated spectra of the 17 brightest far-IR lines in galaxies, covering the 150 to 1300 micron wavelength region corresponding to redshifts 0 < z < 7, and develop a simple iterative algorithm that successfully identifies the 158 micron [CII] line and other lines. Although the [CII] line is a principal coolant for the interstellar medium, the assumption that the brightest observed lines in a given line of sight are always [CII] lines is a poor approximation to the simulated spectra once other lines are included. Blind line identification requires detection of fainter companion lines from the same host galaxies, driving survey sensitivity requirements. The observations require moderate spectral resolution 700 < R < 4000 with angular resolution between 20 arcsec and 10 armin, sufficiently narrow to minimize confusion yet sufficiently large to include a statistically meaningful number of sources.