论文标题

带有宿主星系光度红移的IA类型IA超新星哈勃图表

Type Ia supernova Hubble diagrams with host galaxy photometric redshifts

论文作者

Ruhlmann-Kleider, V., Lidman, C., Möller, A.

论文摘要

研究了使用光度SN宿主星系红移从光度法选择样品中的SN IA Hubble图的情况。主机红移不确定性和核心崩溃SNE的污染被解决。作为一项测试,我们使用了由437个物体在红移中的437个物体制成的Supernova Legacy调查(SNLS)的3年光度SN IA样品(SNLS)。我们将该样品与JLA光谱样品的非SNLS对象相结合,该样品由501个对象制成,主要低于Redshift。我们研究了光度样品红移的起源的两种选择,这些选项完全是由宿主照射红移目录提供的,或者是混合起源,其中75%的样品可以分配光谱红移。使用光曲线模拟与数据相同的光度选择,我们研究了从此类组合样品中的Hubble图表的平面$λCDM$拟合平面$λCDM$拟合的影响。我们发现光度降轴和污染导致宇宙学参数有偏见。对于两个红移选项,偏差的大小相似。如果计算因选择效应而计算SN幅度偏置校正时,则可以考虑到光度红移的不确定性和污染,从而在很大程度上解释了这种偏差。为了进一步减少宇宙学偏见,我们通过使用宇宙学的光度红移或通过采样红移分辨率函数来探索两种将红移不确定性传播到宇宙学可能计算中的方法。红移改装均无法纠正宇宙学偏见,无论红移选项如何,在两种情况下进行采样都会略微降低。对于实际数据,我们发现与JLA的结果兼容结果,用于混合光度和光谱红移,而当包含所有不确定性时,完整的光度法选项却是偏差,但与JLA一致。

The case of SN Ia Hubble diagrams from photometrically selected samples using photometric SN host galaxy redshifts is investigated. The host redshift uncertainties and the contamination by core collapse SNe are addressed. As a test, we use the 3-year photometric SN Ia sample of the SuperNova Legacy Survey (SNLS), made of 437 objects between 0.1 and 1.05 in redshift. We combine this sample with non-SNLS objects of the JLA spectroscopic sample, made of 501 objects mostly below 0.4 in redshift. We study two options for the origin of the redshifts of the photometric sample, either provided entirely from the host photometric redshift catalogue or a mixed origin where 75% of the sample can be assigned spectroscopic redshifts. Using light curve simulations subject to the same photometric selection as data, we study the impact of photometric redshift uncertainties and contamination on flat $ΛCDM$ fits to Hubble diagrams from such combined samples. We find that photometric redshifts and contamination lead to biased cosmological parameters. The magnitude of the bias is similar for both redshift options. This bias can be largely accounted for if photometric redshift uncertainties and contamination are taken into account when computing the SN magnitude bias correction due to selection effects. To reduce the cosmological bias further, we explore two methods to propagate redshift uncertainties into the cosmological likelihood computation, either by refitting photometric redshifts with cosmology or by sampling the redshift resolution function. Redshift refitting fails at correcting the cosmological bias whatever the redshift option, while sampling slightly reduces it in both cases. For actual data, we find compatible results with the JLA ones for mixed photometric and spectroscopic redshifts, while the full photometric option is biased but consistent with JLA when all uncertainties are included.

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