Supernova Acceleration Probe: Studying Dark Energy with Type Ia Supernovae

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The Supernova Acceleration Probe (SNAP) will use Type Ia supernovae (SNe Ia) as distance indicators to measure the effect of dark energy on the expansion history of the Universe. (SNAP's weak-lensing program is described in a separate White Paper.) The experiment exploits supernova distance measurements up to their fundamental systematic limit; strict requirements on the monitoring of each supernova's properties leads to the need for a space-based mission. Results from pre-SNAP experiments, which characterize fundamental SN Ia properties, will be used to optimize the SNAP observing strategy to yield data, which minimize both systematic and statistical uncertainties. With early R&D ... continued below

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12 pages

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Albert, J.; Aldering, G.; Allam, S.; Althouse, W.; Amanullah, R.; Annis, J. et al. August 8, 2005.

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Description

The Supernova Acceleration Probe (SNAP) will use Type Ia supernovae (SNe Ia) as distance indicators to measure the effect of dark energy on the expansion history of the Universe. (SNAP's weak-lensing program is described in a separate White Paper.) The experiment exploits supernova distance measurements up to their fundamental systematic limit; strict requirements on the monitoring of each supernova's properties leads to the need for a space-based mission. Results from pre-SNAP experiments, which characterize fundamental SN Ia properties, will be used to optimize the SNAP observing strategy to yield data, which minimize both systematic and statistical uncertainties. With early R&D funding, we have achieved technological readiness and the collaboration is poised to begin construction. Pre-JDEM AO R&D support will further reduce technical and cost risk. Specific details on the SNAP mission can be found in Aldering et al. (2004, 2005). The primary goal of the SNAP supernova program is to provide a dataset which gives tight constraints on parameters which characterize the dark-energy, e.g. w{sub 0} and w{sub a} where w(a) = w{sub 0} + w{sub a}(1-a). SNAP data can also be used to directly test and discriminate among specific dark energy models. We will do so by building the Hubble diagram of high-redshift supernovae, the same methodology used in the original discovery of the acceleration of the expansion of the Universe that established the existence of dark energy (Perlmutter et al. 1998; Garnavich et al. 1998; Riess et al. 1998; Perlmutter et al. 1999). The SNAP SN Ia program focuses on minimizing the systematic floor of the supernova method through the use of characterized supernovae that can be sorted into subsets based on subtle signatures of heterogeneity. Subsets may be defined based on host-galaxy morphology, spectral-feature strength and velocity, early-time behavior, inter alia. Independent cosmological analysis of each subset of ''like'' supernovae can be compared to give confidence that the results are free from significant systematics. Conversely, analysis between supernova subsets at the same redshift can identify further systematics controls. While theories of the supernova progenitor and explosion mechanism can guide the establishment of subset criteria, such understanding is not required--only comprehensive measurements are--for robustness of the cosmological results. The level of robustness is tied to the quality of data with which supernovae are distinguished. Statistical mission requirements are fundamentally bound by the systematic limitations of the experiment.

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12 pages

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  • Report No.: SLAC-PUB-11394
  • Grant Number: AC02-76SF00515
  • DOI: 10.2172/878833 | External Link
  • Office of Scientific & Technical Information Report Number: 878833
  • Archival Resource Key: ark:/67531/metadc875275

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  • August 8, 2005

Added to The UNT Digital Library

  • Sept. 21, 2016, 2:29 a.m.

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  • Aug. 1, 2017, 12:03 p.m.

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Albert, J.; Aldering, G.; Allam, S.; Althouse, W.; Amanullah, R.; Annis, J. et al. Supernova Acceleration Probe: Studying Dark Energy with Type Ia Supernovae, report, August 8, 2005; [Menlo Park, California]. (digital.library.unt.edu/ark:/67531/metadc875275/: accessed December 11, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.