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The Impact of Cosmic Dust on Supernova Cosmology

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arxiv astro-ph/0603833 v3 pith:FOZRV7R3 submitted 2006-03-30 astro-ph gr-qc

classification astro-phgr-qc
keywords dustextinctiondarkenergycosmicaccuratecurrenteffect
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Extinction by intergalactic gray dust introduces a magnitude redshift dependent offset in the standard-candle relation of SN Ia. This leads to overestimated luminosity distances compared to a dust-free universe. Quantifying the amplitude of this systematic effect is crucial for an accurate determination of the dark energy parameters. In this paper we model the gray dust extinction in terms of the star-formation history of the universe and the physical properties of the dust grains. We focus on a class of cosmic dust models which satisfy current observational constraints. These can produce an extinction as large as 0.08 mag at z=1.7 and potentially disrupt the dark energy parameter inference from future SN surveys. In particular depending on the dust model we find that an unaccounted extinction can bias the estimation of a constant dark energy equation of state w by shifting its best fit value up to 20% from its true value. Near-IR broadband photometry will hardly detect this effect, while the induced decrement of the Balmer lines requires high signal-to-noise spectra. Indeed IR-spectroscopy will be needed for high redshift SNe. Cosmic dust extinction may also cause a detectable violation of the distance-duality relation. A more comprehensive knowledge of the physics of the IGM is necessary for an accurate modeling of intergalactic dust. Due to the large magnitude dispersion current luminosity distance measurements are insensitive to such possible extinction effects. In contrast these must be taken into account if we hope to disclose the true nature of dark energy with the upcoming generation of SN Ia surveys.

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    The inferred initial matter power spectrum from the SELFI algorithm reveals misspecified galaxy bias, selection, mask, redshift, and gravity models, exposing a >2σ cosmological bias before parameter inference.

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