Biology – Quantitative Biology – Populations and Evolution
Scientific paper
Sep 2004
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2004sptz.prop.3396g&link_type=abstract
Spitzer Proposal ID #3396
Biology
Quantitative Biology
Populations and Evolution
Scientific paper
We propose to make high-quality maps of the FLS extragalactic survey region, and to analyze the Cosmic Far-IR Background (CFIB) fluctuations in the maps to constrain galaxy density and evolution functions, and measure clustering. Deep survey Log N- Log S analysis is usually used to understand galaxy populations and evolution, but the analysis is confusion limited with MIPS at 70 and 160 um. Statistical fluctuations in the background, however, are from sources fainter than the confusion limit, and therefore carry information about the distribution of fainter, more distant objects than identified sources. Not only can we learn about the star formation history of the universe from these fluctuations, we can also learn about structure and galaxy formation, as source clustering would leave a strong signal on map power spectra undetectable with identified sources (due to insufficient statistics). Making large, sensitive, far-IR maps is challenging: detector drift and power law noise can imprint false structure. Though MIPS' calibration flashes remove most of these effects, our analysis of MIPS characterization array data shows that after flash correction, power law noise and drift remain (e.g. 8% median drift per scan at 70 um) that will affect large maps. We have therefore developed a novel application of the COBE matrix method (developed for microwave background analysis), our Clrmap software package, to produce statistically optimal MIPS maps. Maps reduced with our software show significant reduction in noise structure compared to co-added maps. We will use our map power spectrum analysis to evaluate models and measure galaxy distribution & evolution, and clustering parameters. In power spectra of 160 um simulated FLS maps, the predicted clustering signal is 5 times stronger than for random galaxies for k= 0.03-0.2 (1/arcmin). This signal is easily detected with our <30%/bin errors. This measurement will constrain galaxy formation models, one of the most outstanding problems in astrophysics.
Grossan Bruce
Smoot George
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