Dust Properties of Galaxies from a Data-Driven Hierarchical Model - John Forbes
Offered By: Kavli Institute for Theoretical Physics via YouTube
Course Description
Overview
Explore the application of data-driven hierarchical modeling to understand dust properties in galaxies through this 30-minute conference talk by John Forbes from Flatiron Institute. Delve into the intersection of astrostatistics, machine learning, and galaxy formation physics as part of the Kavli Institute for Theoretical Physics' conference on data-driven approaches in galaxy evolution studies. Gain insights into how advanced statistical techniques can extract meaningful information from vast datasets, including Integral Field Unit surveys and multi-wavelength imaging. Discover how these methods contribute to detecting anomalous galaxies, linking observations with theoretical models, and advancing our understanding of galaxy formation and evolution in the era of large-scale astronomical surveys.
Syllabus
Dust Properties of Galaxies from a Data-Driven Hierarchical Model ▸ John Forbes (Flatiron)
Taught by
Kavli Institute for Theoretical Physics
Related Courses
Data AnalysisJohns Hopkins University via Coursera Computing for Data Analysis
Johns Hopkins University via Coursera Scientific Computing
University of Washington via Coursera Introduction to Data Science
University of Washington via Coursera Web Intelligence and Big Data
Indian Institute of Technology Delhi via Coursera