We present cosmological constraints from the sample of Type Ia supernovae (SNe Ia) discovered and measured during the full 5 yr of the Dark Energy Survey (DES) SN program. In contrast to most previous cosmological samples, in which SNe are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being an SN Ia, we find 1635 DES SNe in the redshift range 0.10 < z < 1.13 that pass quality selection criteria sufficient to constrain cosmological parameters. This quintuples the number of high-quality z > 0.5 SNe compared to the previous leading compilation of Pantheon+ and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints, we combine the DES SN data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning 0.025 < z < 0.10. Using SN data alone and including systematic uncertainties, we find ΩM = 0.352 ± 0.017 in flat ΛCDM. SN data alone now require acceleration (q0 < 0 in ΛCDM) with over 5σ confidence. We find (Ωm, w) = (0.264-0.096+0.074, 80-0.16+0.14) in flat wCDM. For flat w0waCDM, we find (ΩM,w0, wa) = (0.495-0.043+0.033, -0.36-0.30+0.36 -8.8-4.5+3.7, consistent with a constant equation of state to within ∼2σ. Including Planck cosmic microwave background, Sloan Digital Sky Survey baryon acoustic oscillation, and DES 3 × 2pt data gives (ΩM, w) = (0.321 ± 0.007, -0.941 ± 0.026). In all cases, dark energy is consistent with a cosmological constant to within ∼2σ. Systematic errors on cosmological parameters are subdominant compared to statistical errors; these results thus pave the way for future photometrically classified SN analyses.
The Dark Energy Survey: Cosmology Results with ∼1500 New High-redshift Type Ia Supernovae Using the Full 5 yr Data Set / Abbott, T. M. C.; Acevedo, M.; Aguena, M.; Alarcon, A.; Allam, S.; Alves, O.; Amon, A.; Andrade-Oliveira, F.; Annis, J.; Armstrong, P.; Asorey, J.; Avila, S.; Bacon, D.; Bassett, B. A.; Bechtol, K.; Bernardinelli, P. H.; Bernstein, G. M.; Bertin, E.; Blazek, J.; Bocquet, S.; Brooks, D.; Brout, D.; Buckley-Geer, E.; Burke, D. L.; Camacho, H.; Camilleri, R.; Campos, A.; Carnero Rosell, A.; Carollo, D.; Carr, A.; Carretero, J.; Castander, F. J.; Cawthon, R.; Chang, C.; Chen, R.; Choi, A.; Conselice, C.; Costanzi, M.; Da Costa, L. N.; Crocce, M.; Davis, T. M.; Depoy, D. L.; Desai, S.; Diehl, H. T.; Dixon, M.; Dodelson, S.; Doel, P.; Doux, C.; Drlica-Wagner, A.; Elvin-Poole, J.; Everett, S.; Ferrero, I.; Ferte, A.; Flaugher, B.; Foley, R. J.; Fosalba, P.; Friedel, D.; Frieman, J.; Frohmaier, C.; Galbany, L.; Garcia-Bellido, J.; Gatti, M.; Gaztanaga, E.; Giannini, G.; Glazebrook, K.; Graur, O.; Gruen, D.; Gruendl, R. A.; Gutierrez, G.; Hartley, W. G.; Herner, K.; Hinton, S. R.; Hollowood, D. L.; Honscheid, K.; Huterer, D.; Jain, B.; James, D. J.; Jeffrey, N.; Kasai, E.; Kelsey, L.; Kent, S.; Kessler, R.; Kim, A. G.; Kirshner, R. P.; Kovacs, E.; Kuehn, K.; Lahav, O.; Lee, J.; Lee, S.; Lewis, G. F.; T. S., Li; Lidman, C.; Lin, H.; Malik, U.; Marshall, J. L.; Martini, P.; Mena-Fernandez, J.; Menanteau, F.; Miquel, R.; Mohr, J. J.; Mould, J.; Muir, J.; Moller, A.; Neilsen, E.; Nichol, R. C.; Nugent, P.; Ogando, R. L. C.; Palmese, A.; Pan, Y. -C.; Paterno, M.; Percival, W. J.; Pereira, M. E. S.; Pieres, A.; Plazas Malagon, A. A.; Popovic, B.; Porredon, A.; Prat, J.; Qu, H.; Raveri, M.; Rodriguez-Monroy, M.; Romer, A. K.; Roodman, A.; Rose, B.; Sako, M.; Sanchez, E.; Sanchez Cid, D.; Schubnell, M.; Scolnic, D.; Sevilla-Noarbe, I.; Shah, P.; Allyn Smith, J.; Smith, M.; Soares-Santos, M.; Suchyta, E.; Sullivan, M.; Suntzeff, N.; Swanson, M. E. C.; Sanchez, B. O.; Tarle, G.; Taylor, G.; Thomas, D.; To, C.; Toy, M.; Troxel, M. A.; Tucker, B. E.; Tucker, D. L.; Uddin, S. A.; Vincenzi, M.; Walker, A. R.; Weaverdyck, N.; Wechsler, R. H.; Weller, J.; Wester, W.; Wiseman, P.; Yamamoto, M.; Yuan, F.; Zhang, B.; Zhang, Y.. - In: THE ASTROPHYSICAL JOURNAL LETTERS. - ISSN 2041-8205. - 973:1(2024), pp. L14.--L14.-. [10.3847/2041-8213/ad6f9f]
The Dark Energy Survey: Cosmology Results with ∼1500 New High-redshift Type Ia Supernovae Using the Full 5 yr Data Set
Costanzi M.;
2024-01-01
Abstract
We present cosmological constraints from the sample of Type Ia supernovae (SNe Ia) discovered and measured during the full 5 yr of the Dark Energy Survey (DES) SN program. In contrast to most previous cosmological samples, in which SNe are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being an SN Ia, we find 1635 DES SNe in the redshift range 0.10 < z < 1.13 that pass quality selection criteria sufficient to constrain cosmological parameters. This quintuples the number of high-quality z > 0.5 SNe compared to the previous leading compilation of Pantheon+ and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints, we combine the DES SN data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning 0.025 < z < 0.10. Using SN data alone and including systematic uncertainties, we find ΩM = 0.352 ± 0.017 in flat ΛCDM. SN data alone now require acceleration (q0 < 0 in ΛCDM) with over 5σ confidence. We find (Ωm, w) = (0.264-0.096+0.074, 80-0.16+0.14) in flat wCDM. For flat w0waCDM, we find (ΩM,w0, wa) = (0.495-0.043+0.033, -0.36-0.30+0.36 -8.8-4.5+3.7, consistent with a constant equation of state to within ∼2σ. Including Planck cosmic microwave background, Sloan Digital Sky Survey baryon acoustic oscillation, and DES 3 × 2pt data gives (ΩM, w) = (0.321 ± 0.007, -0.941 ± 0.026). In all cases, dark energy is consistent with a cosmological constant to within ∼2σ. Systematic errors on cosmological parameters are subdominant compared to statistical errors; these results thus pave the way for future photometrically classified SN analyses.| File | Dimensione | Formato | |
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