07-27, 14:50–15:00 (UTC), Green
Astronomers have detected nearly a thousand exoplanets by precisely charting the radial velocity (RV) wobble of their host stars. The RVSpectML family of packages is a new, open-source, modular and performant pipeline for measuring radial velocities and stellar variability indicators from spectroscopic time-series. This talk aims to give potential users and/or developers an overview of the component packages and their status.
Purpose: The RVSpectML family of packages provides performant implementations of both traditional methods for measuring precise radial velocities (e.g., computing RVs from CCFs or template matching) and a variety of physics-informed machine learning-based approaches to mitigating stellar variability (e.g., Doppler-constrained PCA, Scalpels, custom line lists, Gaussian process latent variable models). It aims to make it practical for researchers to experiment with new approaches. Additionally, it aims to help astronomers improve the robustness of exoplanet discoveries by exploring the sensitivity of their results to choice of data analysis algorithm.
Context: Recently, NASA and NSF chartered the Extreme Precision Radial Velocity Working Group to recommend a plan for detecting potentially Earth-like planets around other stars. Their recommendations included developing a modular, customizable, and open-source pipeline for analyzing spectroscopic timeseries data from multiple instruments. The RVSpectML family of packages directly addresses this need.
Eric Ford is a Professor of Astronomy & Astrophysics at the Pennsylvania State University, where he is active in its Institute for Computational & Data Sciences, Center for Astrostatistics, and Center for Exoplanets & Habitable Worlds. Ford’s research focuses on detecting and characterizing planetary systems around other stars. This often involves using Julia to apply modern Bayesian methods to improve the interpretation of exoplanet observations. Ford has taught a graduate-level class on High-Performance Computing for Astrophysics using Julia since 2014.