Extragalactic abstracts - IRSA - California Institute of Technology

Spitzer_Approved_Extragalactic
Mar 25, 10 16:24 Page 703/742
Spitzer Space Telescope − Archive Research Proposal #3396
Cosmic Far−IR Background Fluctuation Studies of the FLS
Principal Investigator: Bruce Grossan
Institution: LBNL
Technical Contact: Bruce Grossan, LBNL
Co−Investigators:
George Smoot, U.C. Berkeley Space Sciences Laboratory
Science Category: Cosmic infrared background
Dollars Approved: 59500.0
Abstract:
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