A Reanalysis of IR Background Fluctuations in Spitzer IRAC GOODS fields

Details A Reanalysis of IR Background Fluctuations in Spitzer IRAC GOODS fields A Reanalysis of IR Background Fluctuations in Spitzer IRAC GOODS fields

May 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007sptz.prop40847c&link_type=abstract

Spitzer Proposal ID #40847

Computer Science

Scientific paper

We propose a reanalysis of IRAC GOODS HDF-N and CDF-S data to measure clustering of the unresolved IR background (IRB) present in 'empty' pixels. These clustering measurements will be used to study any indications for the presence of an unresolved, diffuse IR background from redshifts related to reionization and associated with redshifted UV emission from first-light galaxies. We will improve previous analyses by cross-correlating unresolved IR fluctuations between different IRAC channels to determine if the color of unresolved fluctuations are consistent with spectra expected for first-light galaxies containing Population III stars. The cross-correlation analysis will also allow us to separate various noise and systematic effects that are not common to IRAC passbands. A significiant effort in this proposed program will be to understand statistical errors and systematic uncertainties in fluctuation measurements. Given first-light galaxy fluctuations cluster at 10 arcminute angular scales and individual IRAC images are limited to 5 arcminutes, we will remosaic GOODS BCDs by inserting simulated patterns of first-light galaxies and measure clustering in new mosaics to see if we recover the input fluctuations and to establish the accuracy of diffuse backgrounds in the mosaic. For fluctuation measurements, we will also implement a likelihood analysis of IR images to measure the power spectrum of anisotropies in multipole space. These techniques are commonly used in CMB studies and are optimized to handle issues associated with complex mask and window functions that are applied to images. With such a technique we can directly address if our existing procedure based on Fourier transforms leads to biased estimates of clustering or not. Data products involving masked images, new mosaics of GOODS, and software to measure clustering will all be made publicly available, as we have done in the past in our similar studies.

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