Joint, parameter-free reconstruction of the mass distribution in galaxy clusters from all available data sets
根据所有可用数据集联合、无参数重建星系团中的质量分布
基本信息
- 批准号:241898342
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:德国
- 项目类别:Research Grants
- 财政年份:2013
- 资助国家:德国
- 起止时间:2012-12-31 至 2014-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Being the largest gravitationally bound structures in the Universe, galaxyclusters play an important role for our attempts at understanding cosmic structure formation. Furthermore, their internal structure allows to constrain some of the properties of the Dark Matter. The project proposed here intends to obtain joint reconstructions of the matter distribution ingalaxy clusters from data of the strong and weak gravitational lensing effects, the X-ray emission, the thermal Sunyaev-Zeldovich effect and the kinematics of cluster galaxies, applying as little prejudice as possible.The project will be based on an existing algorithm allowing to jointly analyse data from the strong and weak gravitational lensing effects on an adaptive grid. The additional observables will be related to the gravitational potential of the galaxy clusters by physical models and regularised deprojection. The gravitational potential can then be projected and combined with the gravitational lensing effects. The respective substantial extension of our algorithm, the development of suitable physical models and the relaxation of restricting symmetry and stability assumptions to the largest feasible extent are substantial goals of the proposed research. When completed, an essentially parameter-free, adaptive algorithm will be provided which combines all types of observational data from galaxy clusters in joint reconstructions of their mass distribution.
星系团作为宇宙中最大的引力束缚结构,在我们试图理解宇宙结构形成方面起着重要作用。此外,它们的内部结构允许限制暗物质的一些属性。本文提出的项目旨在利用星系团的强、弱引力透镜效应、X射线辐射、热Sunyaev-Zeldovich效应和运动学数据联合重建星系团的物质分布,该项目将基于一种现有的算法,允许联合分析来自强引力透镜效应和弱引力透镜效应的数据。自适应网格额外的观测量将通过物理模型和正则化的反投影与星系团的引力势相关。然后,引力势可以被投射并与引力透镜效应相结合。我们的算法,适当的物理模型的发展和放松限制对称性和稳定性的假设,以最大的可行程度上各自的实质性扩展是所提出的研究的实质性目标。完成后,将提供一种基本上无参数的自适应算法,将星系团的所有类型的观测数据结合起来,共同重建其质量分布。
项目成果
期刊论文数量(1)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Reconstructing the projected gravitational potential of galaxy clusters from galaxy kinematics
从星系运动学重建星系团的投影引力势
- DOI:10.1051/0004-6361/201321748
- 发表时间:2014
- 期刊:
- 影响因子:6.5
- 作者:E. Sarli;S. Meyer;M. Meneghetti;S. Konrad;C. L. Majer;M. Bartelmann
- 通讯作者:M. Bartelmann
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Professor Dr. Matthias Bartelmann其他文献
Professor Dr. Matthias Bartelmann的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Professor Dr. Matthias Bartelmann', 18)}}的其他基金
Kinetic Field Theory: Second-order perturbation theory
动场论:二阶微扰理论
- 批准号:
418152809 - 财政年份:2019
- 资助金额:
-- - 项目类别:
Research Grants
Developing galaxy-cluster potentials into a cosmological diagnostic
将星系团势发展为宇宙学诊断
- 批准号:
346672789 - 财政年份:2017
- 资助金额:
-- - 项目类别:
Research Grants
Perturbations and observables in inhomogeneous cosmologies
非均匀宇宙学中的扰动和可观测量
- 批准号:
225630247 - 财政年份:2012
- 资助金额:
-- - 项目类别:
Research Grants
Detailed analysis of a large, dedicated sample of HST clusters
HST 簇的大型专用样本的详细分析
- 批准号:
182811170 - 财政年份:2011
- 资助金额:
-- - 项目类别:
Research Grants
Optimal filtering of three-dimensional, weak-lensing data
三维弱透镜数据的优化过滤
- 批准号:
195252990 - 财政年份:2011
- 资助金额:
-- - 项目类别:
Research Grants
Gravitational flexion, its measurement and its application to galaxy clusters
引力弯曲、测量及其在星系团中的应用
- 批准号:
179826617 - 财政年份:2011
- 资助金额:
-- - 项目类别:
Research Grants
Automatic detection of gravitational arcs in wide-area survey data, comparison of the observed and the theoretically expected arc abundance
自动检测广域测量数据中的引力弧,比较观测到的引力弧丰度与理论预期的引力弧丰度
- 批准号:
125319829 - 财政年份:2009
- 资助金额:
-- - 项目类别:
Priority Programmes
Statistics of structures in the gravitational potential - a possible way to constrain halo populations without reference to mass
引力势结构的统计——一种在不参考质量的情况下约束晕圈群体的可能方法
- 批准号:
106639007 - 财政年份:2009
- 资助金额:
-- - 项目类别:
Research Grants
Detection and characterisation of dark-matter halos by gravitational shear and flexion; constraints on the non-linear cosmic structure growth
通过引力剪切和弯曲检测和表征暗物质晕;
- 批准号:
42389529 - 财政年份:2007
- 资助金额:
-- - 项目类别:
Priority Programmes
Effects of gas dynamics on gravitational lensing by galaxy clusters; determination of physical cluster properties from analyses of lensing effects and the cluster gas
气体动力学对星系团引力透镜效应的影响;
- 批准号:
5449526 - 财政年份:2005
- 资助金额:
-- - 项目类别:
Priority Programmes
相似国自然基金
固定参数可解算法在平面图问题的应用以及和整数线性规划的关系
- 批准号:60973026
- 批准年份:2009
- 资助金额:32.0 万元
- 项目类别:面上项目
相似海外基金
Adaptive optimization: parameter-free self-tuning algorithms beyond smoothness and convexity
自适应优化:超越平滑性和凸性的无参数自调整算法
- 批准号:
24K20737 - 财政年份:2024
- 资助金额:
-- - 项目类别:
Grant-in-Aid for Early-Career Scientists
Chemotherapy-free cure of hemoglobin disorders through base editing
通过碱基编辑无需化疗即可治愈血红蛋白疾病
- 批准号:
10754114 - 财政年份:2023
- 资助金额:
-- - 项目类别:
NSF-BSF: AF: Small: Parameter-Free Stochastic Optimization via Trajectory Cues
NSF-BSF:AF:小:通过轨迹线索进行无参数随机优化
- 批准号:
2239527 - 财政年份:2023
- 资助金额:
-- - 项目类别:
Standard Grant
Prediction of nearest neighbor parameters for folding RNAs with modified nucleotides
预测具有修饰核苷酸的折叠 RNA 的最近邻参数
- 批准号:
10576175 - 财政年份:2023
- 资助金额:
-- - 项目类别:
Parameter-free Algorithms for Reinforcement Learning
强化学习的无参数算法
- 批准号:
558512-2021 - 财政年份:2022
- 资助金额:
-- - 项目类别:
Alexander Graham Bell Canada Graduate Scholarships - Doctoral
Parameter-Free Stochastic Gradient Descent: Fast, Self-Tuning Algorithms for Training Deep Neural Networks
无参数随机梯度下降:用于训练深度神经网络的快速自调整算法
- 批准号:
547242-2020 - 财政年份:2022
- 资助金额:
-- - 项目类别:
Postgraduate Scholarships - Doctoral
Parameter-free Algorithms for Reinforcement Learning
强化学习的无参数算法
- 批准号:
558512-2021 - 财政年份:2021
- 资助金额:
-- - 项目类别:
Alexander Graham Bell Canada Graduate Scholarships - Doctoral
Parameter-Free Stochastic Gradient Descent: Fast, Self-Tuning Algorithms for Training Deep Neural Networks
无参数随机梯度下降:用于训练深度神经网络的快速自调整算法
- 批准号:
547242-2020 - 财政年份:2021
- 资助金额:
-- - 项目类别:
Postgraduate Scholarships - Doctoral
CAREER: Parameter-free Optimization Algorithms for Machine Learning
职业:机器学习的无参数优化算法
- 批准号:
2046096 - 财政年份:2021
- 资助金额:
-- - 项目类别:
Continuing Grant














{{item.name}}会员




