COSMOLOGY WITH PHOTOMETRICALLY CLASSIFIED TYPE Ia SUPERNOVAE FROM THE SDSS-II SUPERNOVA SURVEY
COSMOLOGY WITH PHOTOMETRICALLY CLASSIFIED TYPE Ia SUPERNOVAE FROM THE SDSS-II SUPERNOVA SURVEY
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DOI：
10.1088/0004-637x/763/2/88
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发表时间：
2012-11
期刊：
影响因子：
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通讯作者：
H. Campbell;C. D'Andrea;R. Nichol;M. Sako;Mathew Smith;H. Lampeitl;M. Olmstead;B. Bassett;R. Biswas;P. Brown;D. Cinabro;K. Dawson;B. Dilday;R. Foley;J. Frieman;P. Garnavich;R. Hložek;S. Jha;S. Kuhlmann;M. Kunz;J. Marriner;R. Miquel;M. Richmond;A. Riess;D. Schneider;J. Sollerman;Matt G. G. T. Taylor-Matt-G.-G.-T.-Taylor-39069535;Gong-Bo Zhao
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文献类型：
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作者：
H. Campbell;C. D'Andrea;R. Nichol;M. Sako;Mathew Smith;H. Lampeitl;M. Olmstead;B. Bassett;R. Biswas;P. Brown;D. Cinabro;K. Dawson;B. Dilday;R. Foley;J. Frieman;P. Garnavich;R. Hložek;S. Jha;S. Kuhlmann;M. Kunz;J. Marriner;R. Miquel;M. Richmond;A. Riess;D. Schneider;J. Sollerman;Matt G. G. T. Taylor-Matt-G.-G.-T.-Taylor-39069535;Gong-Bo Zhao
我们提出了752颗Ia型超新星（SNe Ia）的宇宙学分析，这些超新星来自完整的斯隆数字巡天II （SDSS-II）超新星巡天，并补充了SDSS-III重子振荡光谱巡天的宿主星系光谱。我们的光度分类方法基于Sako等人的SN分类技术，并辅以宿主星系红移（0.05 < z < 0.55）。我们的方法的超新星分析模拟估计，我们的SN Ia分类效率为70.8%，只有3.9%的污染来自核心坍塌（非Ia） SNe。我们证明，这种程度的污染对我们的宇宙学约束没有影响。我们使用模拟来量化和纠正我们的选择效应（例如，Malmquist偏差）。当拟合平坦的ΛCDM宇宙学模型时，我们发现我们的光度样本单独给出Ωm = 0.24+0.07−0.05（仅统计误差）。如果我们放宽对平面度的限制，那么我们的样本在Ωm和ΩΛ上提供了竞争性的联合统计约束，可与光谱确认的三年超新星遗留调查（SNLS3）得出的结果相媲美。仅使用我们的数据，统计结果支持一个加速的宇宙，有99.96%的置信度。假设恒定的wCDM宇宙学模型，结合H0、宇宙微波背景和发光红星系数据，我们得到w =−0.96+0.10−0.10，Ωm = 0.29+0.02−0.02，Ωk = 0.00+0.03−0.02（仅统计误差），与类似的经光谱证实的Ia型超新星分析结果相比具有竞争力。总的来说，考虑到SDSS-II SN样品的红移杠杆较低（z < 0.55），以及本文使用的光谱确认不足，这一比较令人放心。这些结果证明了光度分类的SN - Ia样品在改善宇宙学约束方面的潜力。
We present the cosmological analysis of 752 photometrically classified Type Ia Supernovae (SNe Ia) obtained from the full Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey, supplemented with host-galaxy spectroscopy from the SDSS-III Baryon Oscillation Spectroscopic Survey. Our photometric-classification method is based on the SN classification technique of Sako et al., aided by host-galaxy redshifts (0.05 < z < 0.55). SuperNova ANAlysis simulations of our methodology estimate that we have an SN Ia classification efficiency of 70.8%, with only 3.9% contamination from core-collapse (non-Ia) SNe. We demonstrate that this level of contamination has no effect on our cosmological constraints. We quantify and correct for our selection effects (e.g., Malmquist bias) using simulations. When fitting to a flat ΛCDM cosmological model, we find that our photometric sample alone gives Ωm = 0.24+0.07−0.05 (statistical errors only). If we relax the constraint on flatness, then our sample provides competitive joint statistical constraints on Ωm and ΩΛ, comparable to those derived from the spectroscopically confirmed Three-year Supernova Legacy Survey (SNLS3). Using only our data, the statistics-only result favors an accelerating universe at 99.96% confidence. Assuming a constant wCDM cosmological model, and combining with H0, cosmic microwave background, and luminous red galaxy data, we obtain w = −0.96+0.10−0.10, Ωm = 0.29+0.02−0.02, and Ωk = 0.00+0.03−0.02 (statistical errors only), which is competitive with similar spectroscopically confirmed SNe Ia analyses. Overall this comparison is reassuring, considering the lower redshift leverage of the SDSS-II SN sample (z < 0.55) and the lack of spectroscopic confirmation used herein. These results demonstrate the potential of photometrically classified SN Ia samples in improving cosmological constraints.