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Nonparametric estimation of galaxy cluster emissivity and detection of point sources in astrophysics with two lasso penalties
Journal of the American Statistical Association ( IF 3.0 ) Pub Date : 2020-07-28
Jairo Diaz-Rodriguez, Dominique Eckert, Hatef Monajemi, Stéphane Paltani, Sylvain Sardy

Astrophysicists are interested in recovering the 3D gas emissivity of a galaxy cluster from a 2D telescope image. Blurring and point sources make this inverse problem harder to solve. The conventional approach requires in a first step to identify and mask the point sources. Instead we model all astrophysical components in a single Poisson generalized linear model. To enforce sparsity on the parameters, maximum likelihood estimation is regularized with two 1 penalties with weights λ1 for the radial emissivity and λ2 for the point sources. The method has the advantage of not employing cross validation to select λ1 and λ2. To judge the significance of interesting features, we quantify uncertainty with the bootstrap. We apply our method to two X-ray telescopes (XMM-Newton and Chandra) data to estimate gas emissivity. The results are more stable and seems less biased than the conventional method, in particular in the outskirt of galaxy clusters.



中文翻译:

天体物理学中星系团发射率的非参数估计和点套索检测的两个套索

天体物理学家对从2D望远镜图像恢复星系团的3D气体发射率感兴趣。模糊和点源使这个反问题更难解决。传统方法在第一步中需要识别并掩盖点源。取而代之的是,我们在单个Poisson广义线性模型中对所有天体物理分量进行建模。为了对参数实施稀疏性,将最大似然估计调整为两个1个与重量处罚λ 1用于径向发射率和λ 2为点源。该方法具有不采用交叉验证来选择的优点λ 1λ 2。为了判断有趣功能的重要性,我们使用自举量化了不确定性。我们将我们的方法应用于两个X射线望远镜(XMM-Newton和Chandra)数据以估计气体发射率。结果比常规方法更稳定,而且似乎偏差更少,特别是在星系团的郊区。

更新日期:2020-07-28
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