Rigorous statistical methodology has been widely adopted across astrophysics, driven in large part by the availability of well-documented and user-friendly open source software. But, as datasets grow and research questions continue to get more ambitious, we need to continuously re-evaluate our tooling choices and learn from methodological developments across astrophysics and other disciplines. In this talk, I will give some examples of how and why open source tools developed for other purposes (like machine learning) can be used to accelerate and improve our data analysis workflows. I will, in particular, highlight some of my interdisciplinary work to develop computationally efficient and physically motivated methods for time domain astronomy, with specific applications to exoplanets and stellar variability, within modern high-performance model building frameworks. There are, however, some limitations to the broad application of these tools and ideas with astrophysics, so I will discuss some of the challenges and propose some possible approaches for tackling these issues.
Topic: Astronomy Colloquium SP22
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Meeting ID: 898 8024 2486