Predictions for Cosmological Infrared Surveys from Space with the Multiband Imaging Photometer for SIRTF

We make predictions for the cosmological surveys to be conducted by the Multiband Imaging Photometer for SIRTF (MIPS) at 24, 70, and 160 lm for the Guaranteed Time Observer and the Legacy programs, using the latest knowledge of the instrument. In addition to the detector noise and the cirrus confusion noise, we discuss in detail the derivation of the confusion noise due to extragalactic sources, which depends strongly on the shape of the source counts at a given wavelength and on the telescope and detector pixel sizes. We show that it is wise in general to compare the classical photometric criterion, used for decades, and the so-called source density criterion to predict the confusion levels. We obtain, using the model of Lagache, Dole, and Puget, limiting fluxes of 50 lJy, 3.2 mJy, and 36 mJy at 24, 70, and 160 lm, respectively. After taking into account other known sources of noise that will limit the surveys’ sensitivities, we compute the redshift distributions of the detected sources at each wavelength and show that they extend up to z � 2: 7a t 24lm and up to z � 2:5 at 70 and 160 lm, leading to the resolution of at most 69%, 54%, and 24% of the cosmic infrared background (CIB) at 24, 70, and 160 lm, respectively. We estimate which galaxy populations will be used to derive the luminosity function evolution with redshift. We also give the redshift distributions of the unresolved sources in the far-IR range, which dominates the fluctuations of the CIB, and a predicted power spectrum showing the feasibility of fluctuations (due to both Poissonian and clustered source distributions) measurements. The main conclusion is that MIPS (and SIRTF in general) cosmological surveys will greatly improve our understanding of galaxy evolution by giving data with unprecedented accuracy in the mid-IR and far-IR range. Subject headings: cosmology: observations — infrared: galaxies — galaxies: evolution — methods: observational

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