基本信息
浏览量:217
职业迁徙
个人简介
My group's main research is studying how galaxies formed their stars. Past projects involved reconstructing star formation histories for all observable galaxies as a function of their halo mass and redshift, as well as making empirical connections between galaxies and their host dark matter halos. At present, we are refining these models to better model galaxies' colors, dust content, and metallicities.
Our group has also developed methodology for reconstructing growth histories also applies to black holes. Quasar luminosity functions combined with AGN occupation fractions and z=0 black hole mass functions provide constraints not only on black hole accretion histories but also on their typical Eddington ratios and duty cycles as a function of redshift and host galaxy mass.
Deep neural networks allow self-consistent inferences from multiple data sources that was not possible before. My group uses machine learning to measure halo properties beyond mass (e.g., accretion rates, concentrations, and spins) from combining many observable features simultaneously (e.g., satellite angular momentum distributions and galaxy environments). We are also developing new methods for Bayesian Deep Learning.
I am the main developer of the Rockstar (Robust Overdensity Calculation using K-Space Topologically Adaptive Refinement) phase-space halo finder. Rockstar excels at identifying halos and substructure where other halo finders often fail---in major mergers and at the centers of large clusters.
Our group has also developed methodology for reconstructing growth histories also applies to black holes. Quasar luminosity functions combined with AGN occupation fractions and z=0 black hole mass functions provide constraints not only on black hole accretion histories but also on their typical Eddington ratios and duty cycles as a function of redshift and host galaxy mass.
Deep neural networks allow self-consistent inferences from multiple data sources that was not possible before. My group uses machine learning to measure halo properties beyond mass (e.g., accretion rates, concentrations, and spins) from combining many observable features simultaneously (e.g., satellite angular momentum distributions and galaxy environments). We are also developing new methods for Bayesian Deep Learning.
I am the main developer of the Rockstar (Robust Overdensity Calculation using K-Space Topologically Adaptive Refinement) phase-space halo finder. Rockstar excels at identifying halos and substructure where other halo finders often fail---in major mergers and at the centers of large clusters.
研究兴趣
论文共 191 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
arxiv(2024)
引用0浏览0引用
0
0
Monthly Notices of the Royal Astronomical Societyno. 2 (2023): 2696-2696
引用0浏览0引用
0
0
arXiv (Cornell University) (2023)
引用0浏览0引用
0
0
arxiv(2023)
arxiv(2023)
The Open Journal of Astrophysics (2023)
引用0浏览0引用
0
0
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn