Characterization of exoplanetary atmospheres with SLOPpy

TL;DR

SLOPpy is a new, open-source Python tool that automates the extraction and analysis of high-resolution exoplanet transmission spectra, improving reproducibility and consistency across studies.

Contribution

The paper introduces SLOPpy, a comprehensive, publicly available pipeline that standardizes the analysis of exoplanet transmission spectra, addressing reproducibility issues in the field.

Findings

SLOPpy successfully reproduces results consistent with previous analyses.

The tool achieves comparable or higher statistical significance in detecting planetary signals.

Validation on multiple datasets demonstrates its robustness and accuracy.

Abstract

Transmission spectroscopy is among the most fruitful techniques to infer the main opacity sources present in the upper atmosphere of a transiting planet and to constrain the composition of the thermosphere and of the unbound exosphere. Not having a public tool able to automatically extract a high-resolution transmission spectrum creates a problem of reproducibility for scientific results. As a consequence, it is very difficult to compare the results obtained by different research groups and to carry out a homogeneous characterization of the exoplanetary atmospheres. In this work, we present a standard, publicly available, user-friendly tool, named SLOPpy (Spectral Lines Of Planets with python), to automatically extract and analyze the optical transmission spectrum of exoplanets as accurately as possible. Several data reduction steps are first performed by SLOPpy to correct the input spectra for sky emission, atmospheric dispersion, the presence of telluric features and interstellar lines, center-to-limb variation, and Rossiter-McLaughlin effect, thus making it a state-of-the-art tool. The pipeline has successfully been applied to HARPS and HARPS-N data of ideal targets for atmospheric characterization. To first assess the code's performance and to validate its suitability, here we present a comparison with the results obtained from the previous analyses of other works on HD 189733 b, WASP-76 b, WASP-127 b, and KELT-20 b. Comparing our results with other works that have analyzed the same datasets, we conclude that this tool gives results in agreement with the published results within 1$\sigma$ most of the time, while extracting, with SLOPpy, the planetary signal with a similar or higher statistical significance.