The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Smoothing supernova data to reconstruct the expansion history of the Universe and its age
Open accessAbstract
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 Ω0 m is marginalized over. The w-probe can be used to successfully distinguish between Λ cold dark matter and other models of dark energy to a high degree of accuracy.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant
Riess, A. G., Filippenko, A. V., Challis, P., Clocchiatti, A., Diercks, A., Garnavich, P. M., Gilliland, R. L., Hogan, C. J., Jha, S., Kirshner, R. P., et al. (1998) · The Astronomical Journal · doi:10.1086/300499 · Cited by 19,704
Measurements of Ω and Λ from 42 High‐Redshift Supernovae
Perlmutter, S., Aldering, G., Goldhaber, G., Knop, R. A., Nugent, P., Castro, P. G., Deustua, S., Fabbro, S., Goobar, A., Groom, D. E., et al. (1999) · The Astrophysical Journal · doi:10.1086/307221 · Cited by 18,062
Riess, A. G., Strolger, L., Tonry, J., Casertano, S., Ferguson, H. C., Mobasher, B., Challis, P., Filippenko, A. V., Jha, S., Li, W., et al. (2004) · The Astrophysical Journal · doi:10.1086/383612 · Cited by 4,315 · cited 1×
“…present there are about 150 SNe between redshifts of 0 and 1.75, with 10 SNe above a redshift of unity) (Riess et al. 1998; Perlmutter et al. 1999; Knop et al. 2003; Tonry et al. 2003; Riess et al. 2004), the SNe data are still not of a quality to firmly distinguish different models of dark energy.”
BackgroundInfluentialTHE CASE FOR A POSITIVE COSMOLOGICAL Λ-TERM
SAHNI, V., & STAROBINSKY, A. (2000) · International Journal of Modern Physics D · doi:10.1142/s0218271800000542 · Cited by 2,767
Self-citationExploring the Expansion History of the Universe
Linder, E. V. (2003) · Physical Review Letters · doi:10.1103/physrevlett.90.091301 · Cited by 2,526
Cited by
SAHNI, V., & STAROBINSKY, A. (2006) · International Journal of Modern Physics D · doi:10.1142/s0218271806009704 · Cited by 734
Self-citationTwo new diagnostics of dark energy
Sahni, V., Shafieloo, A., & Starobinsky, A. A. (2008) · Physical Review D · doi:10.1103/physrevd.78.103502 · Cited by 709
Self-citationReconstruction of dark energy and expansion dynamics using Gaussian processes
Seikel, M., Clarkson, C., & Smith, M. (2012) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2012/06/036 · Cited by 441
Di Valentino, E., Said, J. L., Riess, A., Pollo, A., Poulin, V., Gómez-Valent, A., Weltman, A., Palmese, A., Huang, C. D., Bruck, C. V. D., et al. (2025) · Physics of the Dark Universe · doi:10.1016/j.dark.2025.101965 · Cited by 319
Observational constraints on dark energy and cosmic curvature
Wang, Y., & Mukherjee, P. (2007) · Physical Review D · doi:10.1103/physrevd.76.103533 · Cited by 264
Metadata
- Authors
- Shafieloo, A., Alam, U., Sahni, V., & Starobinsky, A. A.
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2006-03-01
- Volume
- 366
- Issue
- 3
- Pages
- 1081-1095
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2148120570
- Cited by
- 207
- References
- 5
- FWCI
- 9.83
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Smoothing supernova data to reconstruct the expansion history of the Universe and its age. https://app.cochat.ai/papers/10.1111/j.1365-2966.2005.09911.x
- The paper
- Shafieloo, A., Alam, U., Sahni, V., & Starobinsky, A. A. (2006). Smoothing supernova data to reconstruct the expansion history of the Universe and its age. Monthly Notices of the Royal Astronomical Society. https://doi.org/10.1111/j.1365-2966.2005.09911.x
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
