publication . Preprint . 2020

First Hubble diagram and cosmological constraints using superluminous supernova

Inserra, C.; Sullivan, M.; Angus, C. R.; Macaulay, E.; Nichol, R. C.; Smith, M.; Frohmaier, C.; Gutiérrez, C. P.; Möller, A.; Brout, D.; ...
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Open Access English
  • Published: 13 May 2020
  • Publisher: HAL CCSD
  • Country: France
Abstract
We present the first Hubble diagram of superluminous supernovae (SLSNe) out to a redshift of two, together with constraints on the matter density, $\Omega_{\rm M}$, and the dark energy equation-of-state parameter, $w(\equiv p/\rho)$. We build a sample of 20 cosmologically useful SLSNe~I based on light curve and spectroscopy quality cuts. We confirm the robustness of the peak decline SLSN~I standardization relation with a larger dataset and improved fitting techniques than previous works. We then solve the SLSN model based on the above standardisation via minimisation of the $\chi^2$ computed from a covariance matrix which includes statistical and systematic unce...
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Subjects
arXiv: Astrophysics::Cosmology and Extragalactic Astrophysics
free text keywords: [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Related Organizations
View more
Funded by
EC| SPCND
Project
SPCND
Supernovae: Physics and Cosmology in the Next Decade
  • Funder: European Commission (EC)
  • Project Code: 615929
  • Funding stream: FP7 | SP2 | ERC
, NSF| Collaborative Research: The Dark Energy Survey Data Management Operations
Project
Collaborative Research: The Dark Energy Survey Data Management Operations
  • Funder: National Science Foundation (NSF)
  • Project Code: 1138766
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Astronomical Sciences
, EC| TESTDE
Project
TESTDE
Testing the Dark Energy Paradigm and Measuring Neutrino Mass with the Dark Energy Survey
  • Funder: European Commission (EC)
  • Project Code: 291329
  • Funding stream: FP7 | SP2 | ERC
, EC| COGS
Project
COGS
Capitalizing on Gravitational Shear
  • Funder: European Commission (EC)
  • Project Code: 240672
  • Funding stream: FP7 | SP2 | ERC
, EC| COSMICDAWN
Project
COSMICDAWN
Understanding the Origin of Cosmic Structure
  • Funder: European Commission (EC)
  • Project Code: 306478
  • Funding stream: FP7 | SP2 | ERC
View more
Download fromView all 5 versions
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Preprint . 2020
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Mémoires en Sciences de l'Information et de la Communication
Preprint . 2020
Provider: Mémoires en Sciences de l'Information et de la Communication
92 references, page 1 of 7

Abbott T. M. C., et al., 2019, ApJ, 872, L30

Albrecht A., et al., 2006, arXiv e-prints, pp astro-ph/0609591

Anderson L., et al., 2014, MNRAS, 441, 24

Angus C. R., Levan A. J., Perley D. A., Tanvir N. R., Lyman J. D., Stanway E. R., Fruchter A. S., 2016, MNRAS, 458, 84

Angus C. R., et al., 2019, MNRAS, 487, 2215

Arnouts S., Cristiani S., Moscardini L., Matarrese S., Lucchin F., Fontana A., Giallongo E., 1999, MNRAS, 310, 540

Ata M., et al., 2017, MNRAS, 467, 3993

Barbary K., et al., 2009, ApJ, 690, 1358

Bellm E., 2014, in Wozniak P. R., Graham M. J., Mahabal A. A., Seaman R., eds, The Third Hot-wiring the Transient Universe Workshop. pp 27-33 (arXiv:1410.8185)

Betoule M., et al., 2014, A&A, 568, A22

Bishop C., 2006, Pattern Recognition and Machine Learning. 1613-9011, Springer-Verlag New York

Cardelli J. A., Clayton G. C., Mathis J. S., 1989, ApJ, 345, 245

Chabrier G., 2003, PASP, 115, 763

Chen T.-W., Smartt S. J., Yates R. M., Nicholl M., Krühler T., Schady P., Dennefeld M., Inserra C., 2017a, MNRAS, 470, 3566

Chen T.-W., et al., 2017b, ApJ, 849, L4

92 references, page 1 of 7
Related research
Abstract
We present the first Hubble diagram of superluminous supernovae (SLSNe) out to a redshift of two, together with constraints on the matter density, $\Omega_{\rm M}$, and the dark energy equation-of-state parameter, $w(\equiv p/\rho)$. We build a sample of 20 cosmologically useful SLSNe~I based on light curve and spectroscopy quality cuts. We confirm the robustness of the peak decline SLSN~I standardization relation with a larger dataset and improved fitting techniques than previous works. We then solve the SLSN model based on the above standardisation via minimisation of the $\chi^2$ computed from a covariance matrix which includes statistical and systematic unce...
Read more
Subjects
arXiv: Astrophysics::Cosmology and Extragalactic Astrophysics
free text keywords: [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Related Organizations
View more
Funded by
EC| SPCND
Project
SPCND
Supernovae: Physics and Cosmology in the Next Decade
  • Funder: European Commission (EC)
  • Project Code: 615929
  • Funding stream: FP7 | SP2 | ERC
, NSF| Collaborative Research: The Dark Energy Survey Data Management Operations
Project
Collaborative Research: The Dark Energy Survey Data Management Operations
  • Funder: National Science Foundation (NSF)
  • Project Code: 1138766
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Astronomical Sciences
, EC| TESTDE
Project
TESTDE
Testing the Dark Energy Paradigm and Measuring Neutrino Mass with the Dark Energy Survey
  • Funder: European Commission (EC)
  • Project Code: 291329
  • Funding stream: FP7 | SP2 | ERC
, EC| COGS
Project
COGS
Capitalizing on Gravitational Shear
  • Funder: European Commission (EC)
  • Project Code: 240672
  • Funding stream: FP7 | SP2 | ERC
, EC| COSMICDAWN
Project
COSMICDAWN
Understanding the Origin of Cosmic Structure
  • Funder: European Commission (EC)
  • Project Code: 306478
  • Funding stream: FP7 | SP2 | ERC
View more
Download fromView all 5 versions
Hyper Article en Ligne
Preprint . 2020
Provider: Hyper Article en Ligne
HAL Clermont Université
Preprint . 2020
Provider: HAL Clermont Université
Hal-Diderot
Preprint . 2020
Provider: Hal-Diderot
Mémoires en Sciences de l'Information et de la Communication
Preprint . 2020
Provider: Mémoires en Sciences de l'Information et de la Communication
92 references, page 1 of 7

Abbott T. M. C., et al., 2019, ApJ, 872, L30

Albrecht A., et al., 2006, arXiv e-prints, pp astro-ph/0609591

Anderson L., et al., 2014, MNRAS, 441, 24

Angus C. R., Levan A. J., Perley D. A., Tanvir N. R., Lyman J. D., Stanway E. R., Fruchter A. S., 2016, MNRAS, 458, 84

Angus C. R., et al., 2019, MNRAS, 487, 2215

Arnouts S., Cristiani S., Moscardini L., Matarrese S., Lucchin F., Fontana A., Giallongo E., 1999, MNRAS, 310, 540

Ata M., et al., 2017, MNRAS, 467, 3993

Barbary K., et al., 2009, ApJ, 690, 1358

Bellm E., 2014, in Wozniak P. R., Graham M. J., Mahabal A. A., Seaman R., eds, The Third Hot-wiring the Transient Universe Workshop. pp 27-33 (arXiv:1410.8185)

Betoule M., et al., 2014, A&A, 568, A22

Bishop C., 2006, Pattern Recognition and Machine Learning. 1613-9011, Springer-Verlag New York

Cardelli J. A., Clayton G. C., Mathis J. S., 1989, ApJ, 345, 245

Chabrier G., 2003, PASP, 115, 763

Chen T.-W., Smartt S. J., Yates R. M., Nicholl M., Krühler T., Schady P., Dennefeld M., Inserra C., 2017a, MNRAS, 470, 3566

Chen T.-W., et al., 2017b, ApJ, 849, L4

92 references, page 1 of 7
Related research
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