SSSpaNG: Stellar Spectra as Sparse Non-Gaussian Processes

Stephen M. Feeney; Benjamin D. Wandelt; Melissa K. Ness

SSSpaNG: Stellar Spectra as Sparse Non-Gaussian Processes

Stephen M. Feeney, Benjamin D. Wandelt, Melissa K. Ness

Research School of Astronomy & Astrophysics

Research output: Other contribution

Abstract

SSSpaNG is a data-driven Gaussian Process model of the spectra of APOGEE red clump stars, whose parameters are inferred using Gibbs sampling. By pooling information between stars to infer their covariance it permits clear identification of the correlations between spectral pixels. Harnessing this correlation structure, a complete spectrum for each red clump star can be inferred, inpainting missing regions and de-noising by a factor of at least 2-3 for low-signal-to-noise stars.

Original language	English
Number of pages	14
Publication status	Published - 1 Apr 2021

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https://ui.adsabs.harvard.edu/abs/2021ascl.soft04014F

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title = "SSSpaNG: Stellar Spectra as Sparse Non-Gaussian Processes",

abstract = "SSSpaNG is a data-driven Gaussian Process model of the spectra of APOGEE red clump stars, whose parameters are inferred using Gibbs sampling. By pooling information between stars to infer their covariance it permits clear identification of the correlations between spectral pixels. Harnessing this correlation structure, a complete spectrum for each red clump star can be inferred, inpainting missing regions and de-noising by a factor of at least 2-3 for low-signal-to-noise stars.",

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AU - Feeney, Stephen M.

AU - Wandelt, Benjamin D.

AU - Ness, Melissa K.

N1 - C 2020 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society

PY - 2021/4/1

Y1 - 2021/4/1

N2 - SSSpaNG is a data-driven Gaussian Process model of the spectra of APOGEE red clump stars, whose parameters are inferred using Gibbs sampling. By pooling information between stars to infer their covariance it permits clear identification of the correlations between spectral pixels. Harnessing this correlation structure, a complete spectrum for each red clump star can be inferred, inpainting missing regions and de-noising by a factor of at least 2-3 for low-signal-to-noise stars.

AB - SSSpaNG is a data-driven Gaussian Process model of the spectra of APOGEE red clump stars, whose parameters are inferred using Gibbs sampling. By pooling information between stars to infer their covariance it permits clear identification of the correlations between spectral pixels. Harnessing this correlation structure, a complete spectrum for each red clump star can be inferred, inpainting missing regions and de-noising by a factor of at least 2-3 for low-signal-to-noise stars.

KW - Software

M3 - Other contribution

ER -

SSSpaNG: Stellar Spectra as Sparse Non-Gaussian Processes

Abstract

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