Model-Independent Estimates of Dark Matter Distributions

• 成果类型: Article
• 署名作者: Wang, Xiao; Walker, Matthew; Pal, Jayanta; Woodroofe, Michael; Mateo, Mario
• 署名单位: University System of Maryland; University of Maryland Baltimore; University of Cambridge; University of Michigan System; University of Michigan; University of Michigan System; University of Michigan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214508000000652
• 发表日期: 2008
• 页码: 1070-1084
• 关键词: interior-point methods; internal kinematics; maximum-likelihood; CONVERGENCE; galaxies

摘要

A new nonparametric method is described to estimate the distribution of mass within spherical galaxies. The problem of estimating the mass, M(r), within radius r is converted into a problem of estimating a regression function nonparametrically, subject to shape restriction. We represent the restrictions by the interception of quadratic cones and use the second-order cone programming to estimate the unknown parameters. We establish asymptotic results that are used to construct confidence intervals M(r). We apply the technique to new kinematic data for four dwarf galaxies. Results indicate that dark matter dominates the stellar kinematics of these systems at all radii.