Supernova pencil beam survey
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Type Ia supernovae (SNe Ia) can be calibrated to be good standard candles at cosmological distances. We propose a supernova pencil beam survey that could yield between dozens to hundreds of SNe Ia in redshift bins of 0.1 up to $z=1.5$, which would compliment space based SN searches, and enable the proper consideration of the systematic uncertainties of SNe Ia as standard candles, in particular, luminosity evolution and gravitational lensing. We simulate SNe Ia luminosities by adding weak lensing noise (using empirical fitting formulae) and scatter in SN Ia absolute magnitudes to standard candles placed at random redshifts. We show that flux-averaging is powerful in reducing the combined noise due to gravitational lensing and scatter in SN Ia absolute magnitudes. The SN number count is not sensitive to matter distribution in the universe; it can be used to test models of cosmology or to measure the SN rate. The SN pencil beam survey can yield a wealth of data which should enable accurate determination of the cosmological parameters and the SN rate, and provide valuable information on the formation and evolution of galaxies. The SN pencil beam survey can be accomplished on a dedicated 4 meter telescope with a square degree field of view. This telescope can be used to conduct other important observational projects compatible with the SN pencil beam survey, such as QSOs, Kuiper belt objects, and in particular, weak lensing measurements of field galaxies, and the search for gamma-ray burst afterglows.
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Cited by 2 Pith papers
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Lossless Compression of Cosmological Information from Type Ia Supernova Distance Measurements
Compressing SN Ia distance-redshift data to eleven Gaussian log r_p(z) points with covariance is shown to be operationally lossless for cosmological inference across multiple models and datasets.
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Model-Independent Analysis of Type Ia Supernova Datasets and Implications for Dark Energy
Apparent dynamical dark energy signals from SNe Ia with DESI data are consistent with LambdaCDM when accounting for dataset-specific Omega_m inconsistencies rather than requiring evolving dark energy.
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