暗能量调查:利用完整的5年数据集得出的1500∼1500个新的高红移Ia型超新星的宇宙学结果

DES Collaboration: T. M. C. Abbott, M. Acevedo, M. Aguena, A. Alarcon, S. Allam, O. Alves, A. Amon, F. Andrade-Oliveira, J. Annis, P. Armstrong, J. Asorey, S. Avila, D. Bacon, B. A. Bassett, K. Bechtol, P. H. Bernardinelli, G. M. Bernstein, E. Bertin, J. Blazek, S. Bocquet, D. Brooks, D. Brout, E. Buckley-Geer, D. L. Burke, H. Camacho, R. Camilleri, A. Campos, A. Carnero Rosell, D. Carollo, A. Carr, J. Carretero, F. J. Castander, R. Cawthon, C. Chang, R. Chen, A. Choi, C. Conselice, M. Costanzi, L. N. da Costa, M. Crocce, T. M. Davis, D. L. DePoy, S. Desai, H. T. Diehl, M. Dixon, S. Dodelson, P. Doel, C. Doux, A. Drlica-Wagner, J. Elvin-Poole, S. Everett, I. Ferrero, A. Ferté, B. Flaugher, R. J. Foley, P. Fosalba, D. Friedel, J. Frieman, C. Frohmaier, L. Galbany, J. García-Bellido, M. Gatti, E. Gaztanaga, G. Giannini, K. Glazebrook, O. Graur, D. Gruen, R. A. Gruendl, G. Gutierrez, W. G. Hartley, K. Herner, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. Huterer, B. Jain, D. J..
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引用次数: 0

摘要

我们介绍了在暗能量巡天(DES)SN计划的整个5年中发现和测量的Ia型超新星(SNe Ia)样本的宇宙学约束。与之前大多数根据光谱对SNe进行分类的宇宙学样本不同,我们使用一种机器学习算法对DES SNe进行分类,该算法应用于四个测光波段的光变曲线。光谱红移是通过对宿主星系的专门跟踪观测获得的。在考虑了每个SN是SN Ia的可能性之后,我们发现在0.10 < z < 1.13的红移范围内,有1635个DES SNe通过了质量选择标准,足以约束宇宙学参数。与之前Pantheon+的领先汇编相比,高质量z > 0.5的SNe数量增加了五倍,从而实现了迄今为止任何SN数据集所达到的最严格的宇宙学约束。为了得出宇宙学约束,我们将DES的SN数据与高质量的外部低红移样本相结合,该样本由194个SNe Ia组成,跨度为0.025 < z < 0.10。仅使用SN数据并包括系统不确定性,我们在平坦的ΛCDM中发现ΩM = 0.352 ± 0.017。现在仅凭SN数据就需要加速度(在ΛCDM中q0 < 0),置信度超过5σ。我们发现在平坦的wCDM中。对于平坦的w0waCDM,我们发现 ,与恒定的状态方程一致,误差在2σ以内。包括普朗克宇宙微波背景、斯隆数字巡天观测重子声振荡和DES 3 × 2pt数据在内,得出(ΩM,w)=(0.321 ± 0.007,-0.941 ± 0.026)。在所有情况下,暗能量与宇宙常数的一致性都在∼2σ以内。与统计误差相比,宇宙学参数的系统误差是次要的;因此,这些结果为未来的光度分类SN分析铺平了道路。
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The Dark Energy Survey: Cosmology Results with ∼1500 New High-redshift Type Ia Supernovae Using the Full 5 yr Data Set
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 in flat wCDM. For flat w0waCDM, we find , 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.
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