TelescopeML -- I. An End-to-End Python Package for Interpreting Telescope Datasets through Training Machine Learning Models, Generating Statistical Reports, and Visualizing Results

We are in a new era of space exploration, thanks to advancements in ground-and space-based telescopes, such as the James Webb Space Telescope [JWST2023PASP] and CRIRES. These remarkable instruments collect high-resolution, high-signal-to-noise spectra from extrasolar planets [Alderson2023Nature], and brown dwarfs [Miles2023ApJ] atmospheres. Without accurate interpretation of this data, the main objectives of space missions will not be fully accomplished. Different analytical and statistical methods, such as the chi-squared-test, Bayesian statistics, and radiative-transfer atmospheric modeling packages have been developed [batalha2019picaso, MacDonald2023] to interpret the spectra. They utilize either forwardand/or retrieval-radiative transfer modeling to analyze the spectra and extract physical information, such as atmospheric temperature, metallicity, carbon-to-oxygen ratio, and surface gravity [line2014systematic, Iyer2023Sphinx, Marley2015]. These atmospheric models rely on generating the physics and chemistry of these atmospheres for a wide range of thermal structures and compositions. In addition to Bayesian-based techniques, machine learning and deep learning methods have been developed in recent years for various astronomical problems, including confirming the classification of light curves for exoplanet validation [Valizadegan2022], recognizing molecular features [Zingales2018ExoGAN] as well as interpreting brown dwarfs spectra using Random Forest technique [Lueber2023RandomForesr_BDs].