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

Exoplanetary atmospheres reveal key information about planet formation, evolution, and habitability. Spectroscopy at different orbital phases probes chemical composition, thermal structure, and physical processes. Ground-based high-resolution (HR; GIANO-B, HARPS-N, IGRINS, CRIRES+) and space-based low-resolution (LR; JWST, HST) observations probe complementary depths. However, characterization requires separate tools: telluric and stellar contamination removal, radiative transfer, and Bayesian inference. 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 removes the burden of managing multiple configuration files, offering one-click workflows for complex multi-step analyses with clear diagnostic visualizations. Results, retrieval settings, and system parameters are automatically organized in a structured directory tree and database, ensuring easy data access and reproducibility. GUIBRUSHR is built to handle today's large datasets and scale to future instruments (ANDES@ELT, ARIEL), while remaining flexible for new techniques and methodologies.