Datasets and experimental results for "Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks"

<p>This dataset contains the training data and experimental results (i.e. trained models and results on the test set) for the research paper “Parameterizing pressure-temperature profiles of exoplanet atmospheres with neural networks” by T. D. Gebhard et al. which has been accepted for publication in <em>Astronomy & Astrophysics</em>.</p>

<p>The corresponding Python code is available at <a href='https://github.com/timothygebhard/ml4ptp' target='_blank' class='url'>https://github.com/timothygebhard/ml4ptp</a>.</p>

<p>Please note that all rights to the training datasets remain with the respective original authors:</p>

<ul>
<li><strong>PyATMOS dataset:</strong> A. Chopra et al. (2023). “PyATMOS: A Scalable Grid of Hypothetical Planetary Atmospheres.” <a href='https://arxiv.org/abs/2308.10624'>arXiv:2308.10624</a> [Data available from the <a href='https://exoplanetarchive.ipac.caltech.edu/docs/fdl_landing.html'>Exoplanet Archive</a>]</li>
<li><strong>Goyal-2020 dataset:</strong> J. Goyal et al. (2020). “A library of self-consistent simulated exoplanet atmospheres.” MNRAS, 498 (4). <a href='https://academic.oup.com/mnras/article/498/4/4680/5892109'>DOI:10.1093/mnras/staa2300</a> [Data available via <a href='https://drive.google.com/drive/folders/1zCCe6HICuK2nLgnYJFal7W4lyunjU4JE'>Google Drive</a>]</li>
</ul>

<p>The authors have kindly given us permission to republish these datasets here in a format that can be easily loaded into a machine learning pipeline.</p>