A Typical User's Ground-Level Perspective on Machine Learning in Astronomy

A Typical User's Ground-Level Perspective on Machine Learning in Astronomy

Slides from the AAS Splinter talk I gave on machine learning for normal astronomers.

The full talk video was also posted here:
https://www.youtube.com/watch?v=jaB5jghcu20

Full Abstract:
We have entered an era in observational astronomy in which sky surveys routinely release massive datasets. While this wealth of data is critical for determining rates of rare phenomena (e.g. transiting exoplanets or tidal disruption events), it also enables a new kind of data-driven astrophysics (e.g. "hidden" correlations in our data that point towards new or challenging undetandings of physics). Machine learning is simply one tool available to us to discover these new trends or make predictions from our growing volume of data. However, machine learning alone cannot make astrophysical discoveries, and astronomers are still required to interpret astrophysical meaning from our data. Here I will discuss some uses of machine learning in analyzing data from the Kepler and Gaia missions, and attempt to highlight some of the opportunities and limitations in its use.

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James Davenport

January 07, 2019
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