Exposure-averaged Gaussian Processes for Combining Overlapping Datasets

TL;DR

This paper introduces a Gaussian process framework that models exposure-averaged signals to improve the analysis of stellar variability data from multiple instruments with different exposure times.

Contribution

It develops a novel GP approach that accounts for exposure times and instrumental drift, enabling better combination and interpretation of overlapping stellar datasets.

Findings

The framework accurately predicts true stellar oscillation signals.

It effectively separates instrumental drift from stellar variability.

Application to Sun-as-a-star datasets demonstrates improved signal modeling.

Abstract

Physically motivated Gaussian process (GP) kernels for stellar variability, like the commonly used damped, driven simple harmonic oscillators that model stellar granulation and p-mode oscillations, quantify the instantaneous covariance between any two points. For kernels whose timescales are significantly longer than the typical exposure times, such GP kernels are sufficient. For time series where the exposure time is comparable to the kernel timescale, the observed signal represents an exposure-averaged version of the true underlying signal. This distinction is important in the context of recent data streams from Extreme Precision Radial Velocity (EPRV) spectrographs like fast readout stellar data of asteroseismology targets and solar data to monitor the Sun's variability during daytime observations. Current solar EPRV facilities have significantly different exposure times per-site, owing to the different design choices made. Consequently, each instrument traces different binned versions of the same "latent" signal. Here we present a GP framework that accounts for exposure times by computing integrated forms of the instantaneous kernels typically used. These functions allow one to predict the true latent oscillation signals and the exposure-binned version expected by each instrument. We extend the framework to work for instruments with significant time overlap (i.e., similar longitude) by including relative instrumental drift components that can be predicted and separated from the stellar variability components. We use Sun-as-a-star EPRV datasets as our primary example, but present these approaches in a generalized way for application to any dataset where exposure times are a relevant factor or combining instruments with significant overlap.