暗能量巡天超新星计划:超新星测光分类引起的宇宙学偏差
宇宙学与河外天体物理
2022-06-15 v1
摘要
对测光识别的 Ia 型超新星(SNe Ia)样本的宇宙学分析依赖于理解来自核坍缩与特殊 SN Ia 事件的‘污染’效应。我们在最先进的测光识别 SN Ia 样本模拟上采用严格分析,确定了暗能量巡天(DES)5 年 SN 样本中此类‘非 Ia’污染导致的宇宙学偏差。作为分析的一部分,我们在 DES 模拟上测试了基于循环神经网络的测光 SN 分类器 SuperNNova 的性能。根据模拟数据样本与训练样本中非 Ia SN 模型的选择,污染率在 0.8-3.5% 之间,分类效率为 97.7-99.5%。利用应用于多物种的贝叶斯估计(BEAMS)框架及其扩展‘带偏差修正的 BEAMS’(BBC),我们生成了对污染边缘化并修正选择效应的红移分箱哈勃图,并用其约束暗能量状态方程 。假设平坦宇宙且 的高斯先验为 ,我们表明当使用 SuperNNova 并在模拟中考虑广泛的非 Ia SN 模型时, 上的偏差 。与污染相关的系统不确定度估计至多 。这相较于 DES-SN 样本预期的统计不确定度 更小,从而表明污染并非我们分析中的限制不确定度。我们还测量了污染在 与 (假设平坦宇宙)上的偏差,发现 中 、 中 ,因此比 DES-SN 样本预期统计不确定度小 5 至 10 倍。
引用
@article{arxiv.2111.10382,
title = {The Dark Energy Survey Supernova Program: Cosmological biases from supernova photometric classification},
author = {M. Vincenzi and M. Sullivan and A. Möller and P. Armstrong and B. A. Bassett and D. Brout and D. Carollo and A. Carr and T. M. Davis and C. Frohmaier and L. Galbany and K. Glazebrook and O. Graur and L. Kelsey and R. Kessler and E. Kovacs and G. F. Lewis and C. Lidman and U. Malik and R. C. Nichol and B. Popovic and M. Sako and D. Scolnic and M. Smith and G. Taylor and B. E. Tucker and P. Wiseman and M. Aguena and S. Allam and J. Annis and J. Asorey and D. Bacon and E. Bertin and D. Brooks and D. L. Burke and A. Carnero Rosell and J. Carretero and F. J. Castander and M. Costanzi and L. N. da Costa and M. E. S. Pereira and J. De Vicente and S. Desai and H. T. Diehl and P. Doel and S. Everett and I. Ferrero and B. Flaugher and P. Fosalba and J. Frieman and J. García-Bellido and D. W. Gerdes and D. Gruen and G. Gutierrez and S. R. Hinton and D. L. Hollowood and K. Honscheid and D. J. James and K. Kuehn and N. Kuropatkin and O. Lahav and T. S. Li and M. Lima and M. A. G. Maia and J. L. Marshall and R. Miquel and R. Morgan and R. L. C. Ogando and A. Palmese and F. Paz-Chinchón and A. Pieres and A. A. Plazas Malagón and K. Reil and A. Roodman and E. Sanchez and M. Schubnell and S. Serrano and I. Sevilla-Noarbe and E. Suchyta and G. Tarle and C. To and T. N. Varga and J. Weller and R. D. Wilkinson},
journal= {arXiv preprint arXiv:2111.10382},
year = {2022}
}