paper

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

arXiv:2407.16917 · doi:10.21105/joss.06346

Abstract

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.

Please find the accepted paper with complete reference list at https://joss.theoj.org/papers/10.21105/joss.06346

References in corpus (7)

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