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arXiv 1206.0665astro-ph.COastro-ph.SR
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来自超新星遗产巡天的Ia型超新星体积速率的演变

Evolution in the Volumetric Type Ia Supernova Rate from the Supernova Legacy Survey

K. Perrett, M. Sullivan, A. Conley, S. Gonzalez-Gaitan, R. Carlberg, D. Fouchez, P. Ripoche, J. D. Neill, P. Astier, D. Balam, C. Balland, S. Basa, J. Guy, D. H… 展开作者

K. Perrett, M. Sullivan, A. Conley, S. Gonzalez-Gaitan, R. Carlberg, D. Fouchez, P. Ripoche, J. D. Neill, P. Astier, D. Balam, C. Balland, S. Basa, J. Guy, D. Hardin, I. M. Hook, D. A. Howell, R. Pain, N. Palanque-Delabrouille, C. Pritchet, N. Regnault, J. Rich, V. Ruhlmann-Kleider, S. Baumont, C. Lidman, S. Perlmutter, E. S. Walker

摘要:

我们提出了一个测量的体积Ia型超新星(SN Ia)率(SNR_Ia)的红移的函数的前四年的数据从加拿大-法国-夏威夷望远镜(CFHT)超新星遗产调查(SNLS)。该分析包括286个光谱确认的和400多个额外的光度确定的超新星Ia,其红移范围为0.1 <z <1.1。体积SNR_Ia演变与z~1.0的上升一致,其遵循形式为(1 + z)^alpha的幂律,其中alpha = 2.11 +/-0.28。在相同的红移范围内,SNLS速率的演化趋势比宇宙恒星形成历史的演化趋势略浅。我们结合SNLS率测量与其他调查,补充SNLS红移范围,并适合各种简单的SNIa延迟时间分布(DTD)模型的组合数据。DTD的简单幂律模型(即,与t ^-β成比例),根据宇宙恒星形成历史的参数化,得出β = 0.98 +/-0.05到1.15 +/-0.08的值。一个两分量模型,其中SNR_Ia依赖于恒星质量(Mstellar)和恒星形成速率(SFR),SNR_Ia(z)= AxMstellar(z)+BxSFR(z),得到系数A = 1.9 +/-0.1 SNe/yr/M_solar和B = 3.3 +/-0.2 SNe/yr/(M_solar/yr)。更一般的双组分模型也能很好地拟合数据,但单高斯或指数DTD的匹配效果明显较差。最后,我们通过光变曲线宽度(拉伸)将SNLS样本分为两个群体,并表明在我们的测量误差范围内,快速下降的SNe Ia(0.8 <s <1.0)的一般行为与z~0.8的慢速物体(1.0 <s <1.3)相似。

英文摘要:

We present a measurement of the volumetric Type Ia supernova (SN Ia) rate (SNR_Ia) as a function of redshift for the first four years of data from the Canada-France-Hawaii Telescope (CFHT) Supernova Legacy Survey (SNLS). This analysis includes 286 spectroscopically confirmed and more than 400 additional photometrically identified SNe Ia within the redshift range 0.1<z<1.1. The volumetric SNR_Ia evolution is consistent with a rise to z~1.0 that follows a power-law of the form (1+z)^alpha, with alpha=2.11+/-0.28. This evolutionary trend in the SNLS rates is slightly shallower than that of the cosmic star-formation history over the same redshift range. We combine the SNLS rate measurements with those from other surveys that complement the SNLS redshift range, and fit various simple SN Ia delay-time distribution (DTD) models to the combined data. A simple power-law model for the DTD (i.e., proportional to t^-beta) yields values from beta=0.98+/-0.05 to beta=1.15+/-0.08 depending on the parameterization of the cosmic star formation history. A two-component model, where SNR_Ia is dependent on stellar mass (Mstellar) and star formation rate (SFR) as SNR_Ia(z)=AxMstellar(z) + BxSFR(z), yields the coefficients A=1.9+/-0.1 SNe/yr/M_solar and B=3.3+/-0.2 SNe/yr/(M_solar/yr). More general two-component models also fit the data well, but single Gaussian or exponential DTDs provide significantly poorer matches. Finally, we split the SNLS sample into two populations by the light curve width (stretch), and show that the general behavior in the rates of faster-declining SNe Ia (0.8<s<1.0) is similar, within our measurement errors, to that of the slower objects (1.0<s<1.3) out to z~0.8.

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