A Gaussian process model for stellar activity in 2-D line profile time-series

TL;DR

This paper introduces a physics-driven Gaussian process model that effectively separates stellar activity signals from planetary signals in 2-D line profile time-series, improving detection of Earth-like planets.

Contribution

The novel GP framework models activity signals directly in line profile time series, exploiting velocity correlations, and demonstrates successful separation of planetary signals in synthetic and real data.

Findings

Successfully separates planetary signals from stellar activity in synthetic data.

Accurately recovers a 1.5-Earth mass planet signal in solar data.

Effective at low signal-to-noise ratios (~100).

Abstract

Stellar active regions like spots and faculae can distort the shapes of spectral lines, inducing variations in the radial velocities that are often orders of magnitude larger than the signals from Earth-like planets. Efforts to mitigate these activity signals have hitherto focused on either the time or the velocity (wavelength) domains. We present a physics-driven Gaussian process (GP) framework to model activity signals directly in time series of line profiles or Cross-Correlation Functions (CCFs). Unlike existing methods which correct activity signals in line profile time series, our approach exploits the time correlation between velocity (wavelength) bins in the line profile variations, and is based on a simplified but physically motivated model for the origin of these variations. When tested on both synthetic and real data sets with signal-to-noise ratios down to $\sim$ 100, our method was able to separate the planetary signal from the activity signal, even when their periods were identical. We also conducted injection/recovery tests using two years of realistically sampled HARPS-N solar data, demonstrating the ability of the method to accurately recover a signal induced by a 1.5-Earth mass planet with a semi-amplitude of 0.3 m/s and a period of 33 days during high solar activity.