Statistical Challenges in Stellar Parameter Estimation from Theory and Data

Abstract: Understanding how the Milky Way fits into the broader galaxy population requires studying the properties of other galaxies as well as our own. While it is possible to observe the structure of other galaxies directly, understanding the structure of our own Galaxy from within requires inferring the 3-D positions, velocities, and other properties of billions of stars. In this talk, I will discuss some of the statistical challenges in inferring stellar parameters from modern photometric surveys such as Gaia and SDSS, focusing in particular on issues with existing theoretical stellar models, the complex nature of parameter uncertainties, and scalability to large datasets. I will then describe some ongoing work trying to solve these problems using a combination of physics-inspired but data-driven calibrations along with a host of inference approaches including gradient-based optimization, grid-based searches, importance sampling, and nested sampling.

other computer sciencespace physics and aeronomydata analysis, statistics and probability

Audience: researchers in the topic

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IAU-IAA Astrostats & Astroinfo seminar(archived version by January 2023)

Series comments: This is an archived version of the seminar with information about talks by January 2023.

Use the following link for the new version: sites.google.com/view/iau-iaaseminar-new

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Joint IAU-IAA Astrostats & Astroinfo seminar series focuses on statistical and computational methodological challenges arising in the various fields of astronomy. It discusses existing and new advanced approaches in statistical analysis and data mining of astronomical data.

In the 21st century, increasing resources are devoted to wide-field astronomical surveys, multi-dimensional data, and high-throughput instruments that produce peta-scale datasets and giga-scale samples. In addition to the growing tasks of data storage and management, new statistical tools have been developed or specified for astronomical problems. Astronomical insights require characterizing structure in images, spectra or time series by using non-linear, often high-dimensional models.

This international online seminar series is an initiative of the International Astrostatistics Association and the IAU Astroinformatics and Astrostatistics Commission.

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