# Estimating dayside effective temperatures of hot Jupiters and associated uncertainties through Gaussian process regression

## TL;DR

This paper introduces a Gaussian process regression method to estimate the dayside effective temperatures of hot Jupiters from limited broadband observations, providing more robust uncertainty estimates than previous approaches.

## Contribution

The authors develop and validate a new Gaussian process regression technique for estimating exoplanet temperatures, demonstrating its effectiveness with simulated and real observational data.

## Key findings

- GP method yields more robust uncertainty estimates.
- Unbiased temperature estimates possible with three broadband measurements.
- Application to 12 hot Jupiters provides temperature estimates with uncertainties of 66-136 K.

## Abstract

In this work, we outline a new method for estimating dayside effective temperatures of exoplanets and associated uncertainties using Gaussian process (GP) regression. By applying our method to simulated observations, we show that the GP method estimates uncertainty more robustly than other model-independent approaches. We find that unbiased estimates of effective temperatures can be made using as few as three broad-band measurements (white-light HST WFC3 and the two warm Spitzer IRAC channels), although we caution that estimates made using only IRAC can be significantly biased. We then apply our GP method to the twelve hot Jupiters in the literature whose secondary eclipse depths have been measured by WFC3 and IRAC channels 1 and 2: CoRoT-2 b; HAT-P-7 b; HD 189733 b; HD 209458 b; Kepler-13A b; TrES-3 b; WASP-4 b; WASP-12 b; WASP-18 b; WASP-33 b; WASP-43 b; and WASP-103 b. We present model-independent dayside effective temperatures for these planets, with uncertainty estimates that range from $\pm$66 K to $\pm$136 K.