The dark energy survey 5-yr photometrically identified type Ia supernovae
The dark energy survey 5-yr photometrically identified type Ia supernovae
复制标题
暗能量调查 5 年光度鉴定 Ia 型超新星
DOI：
10.1093/mnras/stac1691
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发表时间：
2022
影响因子：
4.8
通讯作者：
Carollo, D
中科院分区：
文献类型：
--
作者：
Möller, A;Smith, M;Sako, M;Sullivan, M;Vincenzi, M;Wiseman, P;Armstrong, P;Asorey, J;Brout, D;Carollo, D
作为暗能量调查(DES)中使用Ia型超新星(SN Ia)宇宙学分析的一部分，我们提供了利用多波段光曲线和宿主星系红移进行光度识别的SN Ia样本。对于这一分析，我们使用光度学分类框架SuperNNovatraven在真实的DES类模拟上。为了可靠地分类，我们处理了DES SN程序(DES-SN)的数据，并对分类器结构进行了改进，在模拟上获得了98%以上的分类精度 %。这是第一个利用集成方法的SN分类，从而产生更稳健的样本。利用光度学、主星系红移和分类概率要求，我们确定了1863个SNE Ia，从中我们选择了1484个宇宙学级别的Sne Ia，红移范围为0.07；z<；1.14。我们发现光度选择的样品的光曲线性质与模拟结果符合得很好。此外，我们使用两种类型的贝叶斯神经网络分类器来创建类似的SN Ia样本，这两种分类器提供了分类概率的不确定性。我们测试了使用这些不确定性作为分布外候选者和模型置信度的指标的可行性。最后，我们讨论了光度样本和分类方法在未来的调查中的意义，如维拉·C·鲁宾天文台的空间和时间调查。
As part of the cosmology analysis using Type Ia Supernovae (SN Ia) in the Dark Energy Survey (DES), we present photometrically identified SN Ia samples using multiband light curves and host galaxy redshifts. For this analysis, we use the photometric classification frameworkSuperNNovatrained on realistic DES-like simulations. For reliable classification, we process the DES SN programme (DES-SN) data and introduce improvements to the classifier architecture, obtaining classification accuracies of more than 98 per cent on simulations. This is the first SN classification to make use of ensemble methods, resulting in more robust samples. Using photometry, host galaxy redshifts, and a classification probability requirement, we identify 1863 SNe Ia from which we select 1484 cosmology-grade SNe Ia spanning the redshift range of 0.07 <z< 1.14. We find good agreement between the light-curve properties of the photometrically selected sample and simulations. Additionally, we create similar SN Ia samples using two types of Bayesian Neural Network classifiers that provide uncertainties on the classification probabilities. We test the feasibility of using these uncertainties as indicators for out-of-distribution candidates and model confidence. Finally, we discuss the implications of photometric samples and classification methods for future surveys such as Vera C. Rubin Observatory Legacy Survey of Space and Time.