GUIBRUSHR – A comprehensive tool to characterize exoplanet atmospheres at different spectroscopic resolutions and with a multi-instrument approach (HoRS3S)

The observation of exoplanetary atmospheres provides crucial information about planet formation, evolution, and potential habitability. Spectroscopic observations at different orbital phases reveal chemical composition, thermal structure, and physical processes in these distant worlds. Ground-based high-resolution (HR) spectroscopy (GIANO-B, HARPS-N, IGRINS, CRIRES+) and space-based low-resolution (LR) spectrophotometry (JWST, HST) provide complementary atmospheric information through their sensitivity to different atmospheric depths. However, atmospheric characterization typically requires multiple separate sophisticated tools: spectral processing pipelines for telluric and stellar contamination removal, radiative transfer codes, and Bayesian inference methods. We present GUIBRUSHR (Graphic User Interface for Bayesian Retrieval Using Spectroscopy at High Resolution), a comprehensive Python 3.10+ package that unifies these capabilities into a single framework with an intuitive tkinter-based GUI. The tool integrates a local SQLite3 database for organized operation tracking and covers the complete atmospheric characterization workflow through dedicated modular tabs: unified parameter configuration, telluric removal via PCA with customizable settings, cross-correlation analysis for HR datasets, forward model generation, Bayesian retrieval analysis using parallelized differential evolution Markov chain Monte Carlo (DE-MCMC), and synthetic HR dataset generation. GUIBRUSHR enables simultaneous analysis of multi-resolution and multi-instrument datasets, combining HR ground-based observations with LR space-based data to leverage their complementary strengths. The retrieval framework and the forward model module use petitRADTRANS v3.0 as the radiative transfer engine. The user-friendly interface eliminates the need to manage multiple configuration files, providing one-click solutions for complex multi-step analyses alongside clear diagnostic visualizations. All analysis results, retrieval settings, and system parameters are automatically stored in a structured directory tree and database, enabling straightforward data interaction and reproducibility. GUIBRUSHR is designed to handle current large datasets and adapt to future instruments (ANDES@ELT, ARIEL) while remaining flexible for new techniques and methodologies.ReadTheDocs: https://guibrushr.readthedocs.io/en/latest/GitLab project: https://www.ict.inaf.it/gitlab/guibrushr/guibrushr