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

Journal Article · 14 January 2020 · Monthly Notices of the Royal Astronomical Society

DOI: https://doi.org/10.1093/mnras/staa110 · OSTI ID:1595847

Politsch, Collin A.  ^[1]; Cisewski-Kehe, Jessi ^[2]; Croft, Rupert A. C. ^[3]; Wasserman, Larry ^[1]

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1. Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA, Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA, McWilliams Center for Cosmology, Carnegie Mellon University, Pittsburgh, PA 15213, USA
2. Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
3. McWilliams Center for Cosmology, Carnegie Mellon University, Pittsburgh, PA 15213, USA, Department of Physics, Carnegie Mellon University, Pittsburgh, PA 15213, USA, School of Physics, University of Melbourne, VIC 3010, Australia

ABSTRACT 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 of quasars, galaxies, and stars; (3) stellar light curves with planetary transits; (4) eclipsing binary light curves; and (5) supernova light curves. We study the Lyα forest in the greatest detail – using trend filtering to map the large-scale structure of the intergalactic medium along quasar-observer lines of sight. The remaining studies share broad themes of: (1) estimating observable parameters of light curves and spectra; and (2) constructing observational spectral/light-curve templates. We also briefly discuss the utility of trend filtering as a tool for 1D data reduction and compression.

• Sponsoring Organization: USDOE
• OSTI ID: 1595847
• Journal Information: Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Vol. 492 Journal Issue: 3; ISSN 0035-8711
• Publisher: Oxford University PressCopyright Statement
• Country of Publication: United Kingdom
• Language: English