Next-generation techniques for analyzing precise radial velocity data

Abstract

Measurements of the motions of a star as the star and its planets orbit a common center of mass are the cornerstone of the exciting and rapidly growing field of extrasolar planets. As Earth orbits the Sun, the Sun moves along a small orbital path at a speed of approximately 10 cm/s, slower than human walking speed. Detecting planets like Earth orbiting nearby stars has been a long-term goal in our field. Thanks to remarkable technological advances in astronomical hardware over the past two decades, today, a new generation of observatories around the world are approaching the level of measurement precision necessary to detect Earth-like planets orbiting other stars. However, Earth’s atmosphere and astrophysical processes in the stars limit the realized measurement precision to significantly less than what is theoretically possible. The next big leap forward in our ability to detect and characterize potentially Earth-like extrasolar planets will come from developing new methods for analyzing the data we have recently begun gathering. We propose a New Initiative to leverage the diverse expertise of our team to develop new algorithms that will employ machine learning techniques to measure better stellar radial velocities by removing the effects of Earth’s atmosphere. This work aims to enable the community to extract the ultimate performance from current instruments and make significant strides in worldwide efforts to detect and study Earth-like extrasolar planets.

Award amount: $289,149

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