|
Activity Number:
|
265
- Innovations in Statistics for Astronomy and Space Physics
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Tuesday, August 4, 2020 : 1:00 PM to 2:50 PM
|
|
Sponsor:
|
SSC (Statistical Society of Canada)
|
|
Abstract #313014
|
|
|
Title:
|
Likelihood-Free Inference of Chemical Homogeneity in Open Clusters
|
|
Author(s):
|
Aarya Patil* and Jo Bovy
|
|
Companies:
|
and University of Toronto
|
|
Keywords:
|
methods: data analysis;
techniques: spectroscopic;
Galaxy: abundances;
Galaxy: disk;
Galaxy: formation
|
|
Abstract:
|
Star clusters are excellent astrophysical laboratories to study the history of star formation and chemical enrichment in our Galaxy. These are groupings of stars born out of the same gas cloud, and are theoretically expected to have similar chemical compositions. Empirically validating this chemical homogeneity is important yet difficult because the measurement of accurate and precise chemistry of stars using stellar spectroscopic data is statistically challenging. We perform high-fidelity Likelihood-free Inference of chemistry of stars using state-of-the-art Neural Density Estimation to observationally determine the level of chemical homogeneity in open clusters. We make our model computationally efficient by incorporating active learning and dimensionality reduction of stellar spectroscopic data through Functional Principal Component Analysis. Our constraints on chemical homogeneity will not only help understand the detailed evolution of star-forming clouds but also allow us to trace the chemical and dynamical history of our Galaxy through chemical tagging.
|
Authors who are presenting talks have a * after their name.
