Lauren Anderson

Carnegie Postdoctoral Fellow



List of Publications [link]

Computational Data Analysis

I work on applying computational data analysis techniques to interesting astronomy problems. Currently I'm working on building a 3D dust map of the Milky Way galaxy using scalable Gaussian processes. I am also interested in falsifying dark matter models using stellar streams around the Milky Way galaxy observed by Gaia.


Data-driven Models

I enjoy thinking about data-driven approaches in situations with complex physical models, where I want to build the model from the data rather than the physics. I apply these concepts to stars using the Gaia catalogue where our physical understanding of stars and stellar models aren't sufficient for the precision we need and introduce too much bias. I made the Gaia DR1 parallaxes more precise using a data-driven model of stars.


Physical Models

I enjoy combining data-driven approaches with physical models where we want to interpret the results, like measuring the shape of the dark matter halo using stellar streams. Stellar streams are a powerful tool to falsify dark matter models, but these models, especially using multiple stellar streams in a time dependent potential, require many parameters. I am currently working on implementing this as a tractable model.



Computational Efficiency

I also enjoy thinking about making complex models computationally tractable which requires advances in computational efficiency. Each prediction of a complex model is expensive to generate, and for inferences we need a continuous prediction over a wide range parameter space. I took a step towards this goal by implementing a method that reduces sample variance in predicted power spectra. This quicker convergence of the mean predictions frees up computing time for exploring parameter space.

Milky Way science, computational data analysis methods