A public dataset of Ariel simulated observations for developing exoplanetary atmosphere data reduction pipelines

TL;DR

This paper introduces a comprehensive public dataset based on Ariel mission simulations to benchmark exoplanet atmosphere data reduction methods, including a neural network baseline and analysis of ML limitations.

Contribution

It provides a new, extensive dataset for benchmarking and validating detrending algorithms in exoplanet spectroscopy, supporting community development.

Findings

Neural network baseline highlights ML detrending limitations.

Dataset demonstrates robustness and fidelity for Ariel mission simulations.

Identifies risks of dataset shift in ML-based detrending methods.

Abstract

Detecting and characterising exoplanet atmospheres remains challenging because atmospheric signals can be comparable to residual noise and instrumental/astrophysical systematics. Spectral features span from a few ppm for small planets up to $\sim 10^3$ ppm for warm/hot giants, while high-quality JWST time-series spectroscopy typically reaches $\sim 10$--$50$ ppm (occasionally $\sim 100$--$200$ ppm in the presence of stellar variability or stronger systematics), making correlated noise across temporal and spectral dimensions a key limitation. With JWST delivering an increasing volume of high-precision transmission spectra, and Ariel set to extend this to a homogeneous survey of $\sim 10^3$ exoplanet atmospheres, robust benchmarking resources with known ground truth are essential to develop and validate data-driven (including ML-based) detrending approaches. As a major step towards this goal, we use ExoSim2 and TauREx to generate one of the most comprehensive public datasets based on the current payload design of the ESA Ariel mission, specifically intended to benchmark detrending algorithms. We also provide a deep neural network baseline for time-series reduction, and use it to highlight the limitations of ML based detrendng methods, i.e. the risks posed by dataset shift when observed distributions diverge from those of the training set, a scenario likely to arise in real observations. This dataset is featured in the Ariel Data Challenge 2024 on Kaggle and has been field-tested for robustness and simulation fidelity. By making these resources publicly available, we aim to support the community in developing, comparing, and stress-testing scalable and reliable methods for exoplanet transmission spectroscopy.