[ascl:2502.017]
blasé: Interpretable machine learning for high-resolution astronomical spectroscopy
blasé performs whole-spectrum fitting by cloning 10,000+ spectral lines from a pre-computed synthetic spectral model template and then learning the perturbations to those lines through comparison to real data. Each spectral line has four parameters, yielding possibly 40,000+ parameters. The technique uses autodiff to tune the parameters precisely and quickly. Built in PyTorch with native GPU support, blasé can be extended to, for example, Doppler imaging, Precision RVs, and abundances.
[ascl:1812.013]
Lightkurve: Kepler and TESS time series analysis in Python
Lightkurve Collaboration;
Cardoso, José Vinícius de Miranda;
Hedges, Christina;
Gully-Santiago, Michael;
Saunders, Nicholas;
Cody, Ann Marie;
Barclay, Thomas;
Hall, Oliver;
Sagear, Sheila;
Turtelboom, Emma;
Zhang, Johnny;
Tzanidakis, Andy;
Mighell, Ken;
Coughlin, Jeff;
Bell, Keaton;
Berta-Thompson, Zach;
Williams, Peter;
Dotson, Jessie;
Barentsen, Geert
Lightkurve analyzes astronomical flux time series data, in particular the pixels and light curves obtained by NASA’s Kepler, K2, and TESS exoplanet missions. This community-developed Python package is designed to be user friendly to lower the barrier for students, astronomers, and citizen scientists interested in analyzing data from these missions. Lightkurve provides easy tools to download, inspect, and analyze time series data and its documentation is supported by a large syllabus of tutorials.