Cosmology with superluminous supernovae
Open accessAbstract
We predict cosmological constraints for forthcoming surveys using superluminous supernovae (SLSNe) as standardizable candles. Due to their high peak luminosity, these events can be observed to high redshift (z ∼ 3), opening up new possibilities to probe the Universe in the deceleration epoch. We describe our methodology for creating mock Hubble diagrams for the Dark Energy Survey (DES), the ‘Search Using DECam for Superluminous Supernovae’ (SUDSS) and a sample of SLSNe possible from the Large Synoptic Survey Telescope (LSST), exploring a range of standardization values for SLSNe. We include uncertainties due to gravitational lensing and marginalize over possible uncertainties in the magnitude scale of the observations (e.g. uncertain absolute peak magnitude, calibration errors). We find that the addition of only ≃100 SLSNe from SUDSS to 3800 Type Ia Supernovae (SNe Ia) from DES can improve the constraints on w and Ωm by at least 20 per cent (assuming a flat wCDM universe). Moreover, the combination of DES SNe Ia and 10 000 LSST-like SLSNe can measure Ωm and w to 2 and 4 per cent, respectively. The real power of SLSNe becomes evident when we consider possible temporal variations in w(a), giving possible uncertainties of only 2, 5 and 14 per cent on Ωm, w0 and wa, respectively, from the combination of DES SNe Ia, LSST-like SLSNe and Planck. These errors are competitive with predicted Euclid constraints, indicating a future role for SLSNe for probing the high-redshift Universe.
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LSST will provide a large, well-characterized sample of superluminous supernovae, including objects at redshift as high as 2.5, large enough to improve cosmological constraints on w and Ωm.
“A large, well-characterized sample of superluminous SNe, including objects at redshift as high as z = 2.5, a sample large enough to be leveraged for cosmology improving constraints on w and Ω m ( Scovacricchi et al. 2016 )”
From LSST: From Science Drivers to Reference Design and Anticipated Data Products (2019) · cites this paper as Scovacricchi et al. 2016
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Ivezić, Ž. (2016) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.44697
Large Synoptic Survey Telescope Town Hall
Willman, B. (2016) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.44768
The Lsst Data Processing Software Stack
Jenness, T. (2016) · American Astronomical Society Meeting Abstracts #227 · doi:10.5281/zenodo.44634
- Authors
- Scovacricchi, D., Nichol, R. C., Bacon, D., Sullivan, M., & Prajs, S.
- Affiliations
- University of Portsmouth (GB)
- University of Southampton (GB)
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-12-24
- Volume
- 456
- Issue
- 2
- Pages
- 1700-1707
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2262973782
- Cited by
- 35
- References
- 49
- Citations by year
- 2024: 2 · 2023: 1 · 2022: 1 · 2021: 2 · 2019: 4 · 2018: 8 · 2017: 9 · 2016: 7 · 2015: 1
- FWCI
- 4.74
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- HighWire Press Open Archive (submittedVersion)
- arXiv (Cornell University)
- Funders
- Science and Technology Facilities Council (ST/K00090X/1)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Gamma-ray bursts and supernovae
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Neutrino Physics Research
- Keywords
- superluminous supernovae, dark energy, cosmological constraints, Hubble diagram, Type Ia supernovae, dark energy equation of state, matter density parameter, gravitational lensing, LSST, Dark Energy Survey
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.
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