Smoothing supernova data to reconstruct the expansion history of the universe

By A. Shafieloo, Ujjaini Alam, V. Sahni, A. Starobinsky. 2005

181 citations recorded by the source corpus.

We propose a non-parametric method of smoothing supernova data over redshift using a Gaussian kernel in order to reconstruct important cosmological quantities including H(z) and w(z) in a model-independent manner. This method is shown to be successful in discriminating between different models of dark energy when the quality of data is commensurate with that expected from the future Supernova Acceleration Probe (SNAP). We find that the Hubble parameter is especially well determined and useful for this purpose. The look-back time of the Universe may also be determined to a very high degree of accuracy (≤0.2 per cent) using this method. By refining the method, it is also possible to obtain reasonable bounds on the equation of state of dark energy. We explore a new diagnostic of dark energy - the 'w-probe' - which can be calculated from the first derivative of the data. We find that this diagnostic is reconstructed extremely accurately for different reconstruction methods even if Ω 0m is marginalized over. The w-probe can be used to successfully distinguish between A cold dark matter and other models of dark energy to a high degree of accuracy.

Subfields: Astronomy and Astrophysics

View Paper: https://www.semanticscholar.org/paper/7d86ddcc9448f8ad0da9d1be044050c73b594557

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In Astronomy and Astrophysics

Citations in the corpus

Type Ia Supernova Distances at Redshift >1.5 from the Hubble Space Telescope Multi-cycle Treasury Programs: The Early Expansion Rate (2017, background): “Some analyses have focused on modelindependent reconstruction of an analytical E(z) function or of other dynamical quantities like the deceleration parameter q(z) (Shafieloo et al. 2006; Sahni & Starobinsky 2006; Shafieloo 2007; Ishida & de Souza 2011).”
Improved Constraints on the Acceleration History of the Universe and the Properties of the Dark Energy (2007, unknown): “This includes the work of Huterer & Starkman (2003), Wang & Tegmark (2004, 2005), Shafieloo et al. (2006), Alam et al. (2004), Nesseris & Perivolaropoulos (2004), Huterer & Cooray (2005), Shapiro&Turner (2006), Turner&Huterer (2007), andAlam et al. (2007), for example.”
MODEL-INDEPENDENT EVIDENCE FOR DARK ENERGY EVOLUTION FROM BARYON ACOUSTIC OSCILLATIONS (2014, methodology): “Note that a pole in the equation of state may be possible to determine in future Type Ia supernova (SNIa) data sets using modelindependent reconstruction, as demonstrated in Shafieloo et al. (2006).”
DESI 2024: reconstructing dark energy using crossing statistics with DESI DR1 BAO data (2024, methodology): “Alternatively, a bottom-up approach involves reconstructing its redshift evolution directly from the data using non-parametric methods (see for example [19, 20, 25, 27, 28, 30– 35 ,”
The WiggleZ Dark Energy Survey: measuring the cosmic expansion history using the Alcock-Paczynski test and distant supernovae (2011, methodology): “…accelerating-expansion models are favoured with a statistical significance of more than 3-σ. • We used an adapted version of the reconstruction method of Shafieloo et al. (2006) to model the continuous cosmic expansion history since z = 0.9 using both the Alcock-Paczynski and supernovae datasets.”