Directional stacking signals are reconstructed without information loss as cross-power spectra between the target field and the E/B modes of the spin field weighted by galaxy density.
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Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.
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Dipoles for everyone: the pseudo-$C_\ell$ approach to directional stacking
Directional stacking signals are reconstructed without information loss as cross-power spectra between the target field and the E/B modes of the spin field weighted by galaxy density.
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Machine Learning Techniques for Astrophysics and Cosmology: Lyman-$\alpha$ forest
Review of machine learning applications for analyzing Lyman-alpha forest observations to probe cosmology, reionization, and dark matter.