Insights on the Spectral Signatures of Stellar Activity and Planets from PCA

TL;DR

This paper demonstrates that principal component analysis (PCA) can distinguish stellar activity signals from planetary signals in high-resolution spectra, aiding the detection of lower-amplitude exoplanets.

Contribution

The study introduces a PCA-based method to differentiate stellar activity-induced spectral variations from Doppler shifts in simulated spectra.

Findings

PCA can effectively separate stellar activity signals from planetary signals.

Higher spectral resolution and S/N improve the discrimination of signals.

Spectral line variability due to stellar activity is distinct from Doppler shifts.

Abstract

Photospheric velocities and stellar activity features such as spots and faculae produce measurable radial velocity signals that currently obscure the detection of sub-meter-per-second planetary signals. However, photospheric velocities are imprinted differently in a high-resolution spectrum than Keplerian Doppler shifts. Photospheric activity produces subtle differences in the shapes of absorption lines due to differences in how temperature or pressure affects the atomic transitions. In contrast, Keplerian Doppler shifts affect every spectral line in the same way. With high enough S/N and high enough resolution, statistical techniques can exploit differences in spectra to disentangle the photospheric velocities and detect lower-amplitude exoplanet signals. We use simulated disk-integrated time-series spectra and principal component analysis (PCA) to show that photospheric signals introduce spectral line variability that is distinct from Doppler shifts. We quantify the impact of instrumental resolution and S/N for this work.