TelescopeML -- I. An End-to-End Python Package for Interpreting Telescope Datasets through Training Machine Learning Models, Generating Statistical Reports, and Visualizing Results
This provides astronomers with a tool for interpreting telescope datasets, but it is incremental as it applies existing methods to new astronomical data.
The authors tackled the challenge of analyzing high-resolution spectra from exoplanet and brown dwarf atmospheres by developing TelescopeML, a Python package that processes synthetic datasets, trains CNN models, and deploys them on observational data to derive spectroscopic parameters, achieving accurate and swift results.
We are on the verge of a revolutionary era in space exploration, thanks to advancements in telescopes such as the James Webb Space Telescope (\textit{JWST}). High-resolution, high signal-to-noise spectra from exoplanet and brown dwarf atmospheres have been collected over the past few decades, requiring the development of accurate and reliable pipelines and tools for their analysis. Accurately and swiftly determining the spectroscopic parameters from the observational spectra of these objects is crucial for understanding their atmospheric composition and guiding future follow-up observations. \texttt{TelescopeML} is a Python package developed to perform three main tasks: 1. Process the synthetic astronomical datasets for training a CNN model and prepare the observational dataset for later use for prediction; 2. Train a CNN model by implementing the optimal hyperparameters; and 3. Deploy the trained CNN models on the actual observational data to derive the output spectroscopic parameters.