A Gaussian process framework for modelling instrumental systematics: application to transmission spectroscopy

TL;DR

This paper introduces a Gaussian process-based method for modeling and mitigating instrumental systematics in transmission spectroscopy, improving the accuracy of exoplanet atmospheric measurements.

Contribution

It presents a non-parametric Gaussian process framework that incorporates auxiliary instrument data without assuming specific systematic dependencies.

Findings

Applied to NICMOS data of HD 189733, reducing systematic noise effects.

Reconciled previous conflicting results in the literature.

Provided a general introduction to Gaussian processes for regression.

Abstract

Transmission spectroscopy, which consists of measuring the wavelength-dependent absorption of starlight by a planet's atmosphere during a transit, is a powerful probe of atmospheric composition. However, the expected signal is typically orders of magnitude smaller than instrumental systematics, and the results are crucially dependent on the treatment of the latter. In this paper, we propose a new method to infer transit parameters in the presence of systematic noise using Gaussian processes, a technique widely used in the machine learning community for Bayesian regression and classification problems. Our method makes use of auxiliary information about the state of the instrument, but does so in a non-parametric manner, without imposing a specific dependence of the systematics on the instrumental parameters, and naturally allows for the correlated nature of the noise. We give an example application of the method to archival NICMOS transmission spectroscopy of the hot Jupiter HD 189733, which goes some way towards reconciling the controversy surrounding this dataset in the literature. Finally, we provide an appendix giving a general introduction to Gaussian processes for regression, in order to encourage their application to a wider range of problems.