The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification
The Dark Energy Survey supernova program: cosmological biases from supernova photometric classification
复制标题
暗能量巡天超新星计划：超新星光度分类的宇宙学偏差
DOI：
10.1093/mnras/stac1404
复制
发表时间：
2022
影响因子：
4.8
通讯作者：
Frohmaier, C.
中科院分区：
文献类型：
--
作者：
Vincenzi, M.;Sullivan, M.;Möller, A.;Armstrong, P.;Bassett, B. A.;Brout, D.;Carollo, D.;Carr, A.;Davis, T. M.;Frohmaier, C.
对光度学鉴定的Ia型超新星（SNe Ia）样本的宇宙学分析取决于对核心坍缩和特殊的SNe Ia事件的“污染”影响的理解。我们采用了严格的分析，使用光度分类SuperNNova的国家的最先进的模拟SN样本，以确定宇宙学的偏见，由于这种“非Ia”污染的暗能量调查（DES）5年SN样本。根据SuperNNova训练和测试样本中使用的非Ia SN模型，污染程度从0.8%到3.5%不等，分类效率为97.7%到99.5%。使用贝叶斯估计应用于多个物种（BEAMS）框架及其扩展BBC（“BEAMS与偏差校正”），我们产生了一个红移分仓的哈勃图边缘化的污染和校正的选择效应，并使用它来约束暗能量状态方程，w。假设一个平坦的宇宙，高斯ΩMprior为0.311 ± 0.010，我们表明，当使用SuperNNova时，偏差<0.008，与污染相关的系统不确定性约为DES-SN样本统计不确定性的10%。另一种使用离群值剔除技术（例如Chauvenet标准）代替SuperNNova来剔除污染物的方法导致了较大但仍然适度的偏差（0.015-0.03）。最后，我们测量了由于w 0和wa的污染造成的偏差（假设宇宙是平坦的），发现这些偏差<0.009 inw 0和<0.108 inwa，比DES-SN样本的统计不确定性小5到10倍。
Cosmological analyses of samples of photometrically identified type Ia supernovae (SNe Ia) depend on understanding the effects of ‘contamination’ from core-collapse and peculiar SN Ia events. We employ a rigorous analysis using the photometric classifier SuperNNova on state-of-the-art simulations of SN samples to determine cosmological biases due to such ‘non-Ia’ contamination in the Dark Energy Survey (DES) 5-yr SN sample. Depending on the non-Ia SN models used in the SuperNNova training and testing samples, contamination ranges from 0.8 to 3.5 per cent, with a classification efficiency of 97.7–99.5 per cent. Using the Bayesian Estimation Applied to Multiple Species (BEAMS) framework and its extension BBC (‘BEAMS with Bias Correction’), we produce a redshift-binned Hubble diagram marginalized over contamination and corrected for selection effects, and use it to constrain the dark energy equation-of-state,w. Assuming a flat universe with Gaussian ΩMprior of 0.311 ± 0.010, we show that biases onware <0.008 when using SuperNNova, with systematic uncertainties associated with contamination around 10 per cent of the statistical uncertainty onwfor the DES-SN sample. An alternative approach of discarding contaminants using outlier rejection techniques (e.g. Chauvenet’s criterion) in place of SuperNNova leads to biases onwthat are larger but still modest (0.015–0.03). Finally, we measure biases due to contamination onw0andwa(assuming a flat universe), and find these to be <0.009 inw0and <0.108 inwa, 5 to 10 times smaller than the statistical uncertainties for the DES-SN sample.