Trend filtering – II. Denoising astronomical signals with varying degrees of smoothness

@article{Politsch2020TrendF,
  title={Trend filtering – II. Denoising astronomical signals with varying degrees of smoothness},
  author={Collin A Politsch and Jessi Cisewski-Kehe and Rupert Croft and Larry Wasserman},
  journal={Monthly Notices of the Royal Astronomical Society},
  year={2020}
}
Trend filtering – first introduced into the astronomical literature in Paper I of this series – is a state-of-the-art statistical tool for denoising 1D signals that possess varying degrees of smoothness. In this work, we demonstrate the broad utility of trend filtering to observational astronomy by discussing how it can contribute to a variety of spectroscopic and time-domain studies. The observations we discuss are (1) the Lyman-α (Lyα) forest of quasar spectra; (2) more general spectroscopy… 

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