Home > Research > Publications & Outputs > The Dark Energy Survey supernova program

Research

Links

Text available via DOI:

View graph of relations

The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

Standard

The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification. / Dark Energy Survey Collaboration.
In: Monthly Notices of the Royal Astronomical Society, Vol. 518, No. 1, 31.01.2023, p. 1106-1127.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Dark Energy Survey Collaboration 2023, 'The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification', Monthly Notices of the Royal Astronomical Society, vol. 518, no. 1, pp. 1106-1127. https://doi.org/10.1093/mnras/stac1404

APA

Dark Energy Survey Collaboration (2023). The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification. Monthly Notices of the Royal Astronomical Society, 518(1), 1106-1127. https://doi.org/10.1093/mnras/stac1404

Vancouver

Dark Energy Survey Collaboration. The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification. Monthly Notices of the Royal Astronomical Society. 2023 Jan 31;518(1):1106-1127. Epub 2022 Jun 3. doi: 10.1093/mnras/stac1404

Author

Dark Energy Survey Collaboration. / The Dark Energy Survey supernova program : cosmological biases from supernova photometric classification. In: Monthly Notices of the Royal Astronomical Society. 2023 ; Vol. 518, No. 1. pp. 1106-1127.

Bibtex

@article{3bc7debef9404bc5aa8fde9f10f83ef2,
title = "The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification",
abstract = "Cosmological analyses of samples of photometrically identified type Ia supernovae (SNe Ia) depend on understanding the effects of 'contamination' from core-collapse and peculiar SN Ia events. We employ a rigorous analysis using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such 'non-Ia' contamination in the Dark Energy Survey (DES) 5-yr SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8 to 3.5 per cent, with a classification efficiency of 97.7-99.5 per cent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC ('BEAMS with Bias Correction'), we produce a redshift-binned Hubble diagram marginalized over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian ΩM prior of 0.311 ± 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 per cent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenet's criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.015-0.03). Finally, we measure biases due to contamination on w0 and wa (assuming a flat universe), and find these to be <0.009 in w0 and <0.108 in wa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.",
author = "{Dark Energy Survey Collaboration} and M. Vincenzi and M. Sullivan and A. M{\"o}ller and P. Armstrong and Bassett, {B. A.} and D. Brout and D. Carollo and A. Carr and Davis, {T. M.} and C. Frohmaier and L. Galbany and K. Glazebrook and O. Graur and L. Kelsey and R. Kessler and E. Kovacs and Lewis, {G. F.} and C. Lidman and U. Malik and Nichol, {R. C.} and B. Popovic and M. Sako and D. Scolnic and M. Smith and G. Taylor and Tucker, {B. E.} and P. Wiseman and M. Aguena and S. Allam and J. Annis and J. Asorey and D. Bacon and E. Bertin and D. Brooks and Burke, {D. L.} and {Carnero Rosell}, A. and J. Carretero and Castander, {F. J.} and M. Costanzi and {da Costa}, {L. N.} and Pereira, {M. E. S.} and {De Vicente}, J. and S. Desai and Diehl, {H. T.} and P. Doel and S. Everett and I. Ferrero and B. Flaugher and P. Fosalba and J. Frieman",
year = "2023",
month = jan,
day = "31",
doi = "10.1093/mnras/stac1404",
language = "English",
volume = "518",
pages = "1106--1127",
journal = "Monthly Notices of the Royal Astronomical Society",
issn = "0035-8711",
publisher = "OXFORD UNIV PRESS",
number = "1",

}

RIS

TY - JOUR

T1 - The Dark Energy Survey supernova program

T2 - cosmological biases from supernova photometric classification

AU - Dark Energy Survey Collaboration

AU - Vincenzi, M.

AU - Sullivan, M.

AU - Möller, A.

AU - Armstrong, P.

AU - Bassett, B. A.

AU - Brout, D.

AU - Carollo, D.

AU - Carr, A.

AU - Davis, T. M.

AU - Frohmaier, C.

AU - Galbany, L.

AU - Glazebrook, K.

AU - Graur, O.

AU - Kelsey, L.

AU - Kessler, R.

AU - Kovacs, E.

AU - Lewis, G. F.

AU - Lidman, C.

AU - Malik, U.

AU - Nichol, R. C.

AU - Popovic, B.

AU - Sako, M.

AU - Scolnic, D.

AU - Smith, M.

AU - Taylor, G.

AU - Tucker, B. E.

AU - Wiseman, P.

AU - Aguena, M.

AU - Allam, S.

AU - Annis, J.

AU - Asorey, J.

AU - Bacon, D.

AU - Bertin, E.

AU - Brooks, D.

AU - Burke, D. L.

AU - Carnero Rosell, A.

AU - Carretero, J.

AU - Castander, F. J.

AU - Costanzi, M.

AU - da Costa, L. N.

AU - Pereira, M. E. S.

AU - De Vicente, J.

AU - Desai, S.

AU - Diehl, H. T.

AU - Doel, P.

AU - Everett, S.

AU - Ferrero, I.

AU - Flaugher, B.

AU - Fosalba, P.

AU - Frieman, J.

PY - 2023/1/31

Y1 - 2023/1/31

N2 - Cosmological analyses of samples of photometrically identified type Ia supernovae (SNe Ia) depend on understanding the effects of 'contamination' from core-collapse and peculiar SN Ia events. We employ a rigorous analysis using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such 'non-Ia' contamination in the Dark Energy Survey (DES) 5-yr SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8 to 3.5 per cent, with a classification efficiency of 97.7-99.5 per cent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC ('BEAMS with Bias Correction'), we produce a redshift-binned Hubble diagram marginalized over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian ΩM prior of 0.311 ± 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 per cent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenet's criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.015-0.03). Finally, we measure biases due to contamination on w0 and wa (assuming a flat universe), and find these to be <0.009 in w0 and <0.108 in wa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.

AB - Cosmological analyses of samples of photometrically identified type Ia supernovae (SNe Ia) depend on understanding the effects of 'contamination' from core-collapse and peculiar SN Ia events. We employ a rigorous analysis using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such 'non-Ia' contamination in the Dark Energy Survey (DES) 5-yr SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8 to 3.5 per cent, with a classification efficiency of 97.7-99.5 per cent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC ('BEAMS with Bias Correction'), we produce a redshift-binned Hubble diagram marginalized over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state, w. Assuming a flat universe with Gaussian ΩM prior of 0.311 ± 0.010, we show that biases on w are <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 per cent of the statistical uncertainty on w for the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenet's criterion) in place of SuperNNova leads to biases on w that are larger but still modest (0.015-0.03). Finally, we measure biases due to contamination on w0 and wa (assuming a flat universe), and find these to be <0.009 in w0 and <0.108 in wa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.

U2 - 10.1093/mnras/stac1404

DO - 10.1093/mnras/stac1404

M3 - Journal article

VL - 518

SP - 1106

EP - 1127

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

IS - 1

ER -