TelescopeML -- I An End-to-End Python Package for Interpreting Telescope Datasets through Training Machine Learning Models, Generating Statistical Reports, and Visualizing Results
<|reference_start|>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 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.<|reference_end|>
@article{ehsan2024telescopeml,
    title={TelescopeML -- I. An End-to-End Python Package for Interpreting
  Telescope Datasets through Training Machine Learning Models, Generating
  Statistical Reports, and Visualizing Results},
    author={Ehsan (Sam) Gharib-Nezhad, Natasha E. Batalha, Hamed Valizadegan,
  Miguel J. S. Martinho, Mahdi Habibi, and Gopal Nookula},
    journal={Journal of Open Source Software, 9(99), 6346 (2024)},
    year={2024},
    doi={10.21105/joss.06346},
    archivePrefix={arXiv},
    eprint={2407.16917},
    primaryClass={astro-ph.IM astro-ph.EP cs.LG}
}