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Title: Disentangling Time-series Spectra with Gaussian Processes: Applications to Radial Velocity Analysis

Abstract

Measurements of radial velocity variations from the spectroscopic monitoring of stars and their companions are essential for a broad swath of astrophysics; these measurements provide access to the fundamental physical properties that dictate all phases of stellar evolution and facilitate the quantitative study of planetary systems. The conversion of those measurements into both constraints on the orbital architecture and individual component spectra can be a serious challenge, however, especially for extreme flux ratio systems and observations with relatively low sensitivity. Gaussian processes define sampling distributions of flexible, continuous functions that are well-motivated for modeling stellar spectra, enabling proficient searches for companion lines in time-series spectra. We introduce a new technique for spectral disentangling, where the posterior distributions of the orbital parameters and intrinsic, rest-frame stellar spectra are explored simultaneously without needing to invoke cross-correlation templates. To demonstrate its potential, this technique is deployed on red-optical time-series spectra of the mid-M-dwarf binary LP661-13. We report orbital parameters with improved precision compared to traditional radial velocity analysis and successfully reconstruct the primary and secondary spectra. We discuss potential applications for other stellar and exoplanet radial velocity techniques and extensions to time-variable spectra. The code used in this analysis is freely available asmore » an open-source Python package.« less

Authors:
 [1]; ; ;  [2];  [3];  [4];  [5]
  1. Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, Stanford, CA 94305 (United States)
  2. Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States)
  3. Department of Statistics, NC State University, 2311 Stinson Drive, Raleigh, NC 27695 (United States)
  4. Department of Astronomy and Astrophysics, University of Chicago, 5640 S. Ellis Avenue, Chicago, IL 60637 (United States)
  5. Massachusetts Institute of Technology, Cambridge, MA 02138 (United States)
Publication Date:
OSTI Identifier:
22663640
Resource Type:
Journal Article
Resource Relation:
Journal Name: Astrophysical Journal; Journal Volume: 840; Journal Issue: 1; Other Information: Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
79 ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; ACCURACY; ASTROPHYSICS; CONVERSION; CORRELATIONS; DISTRIBUTION; GAUSSIAN PROCESSES; RADIAL VELOCITY; SENSITIVITY; SIMULATION; SPECTRA; STAR EVOLUTION; STARS

Citation Formats

Czekala, Ian, Mandel, Kaisey S., Andrews, Sean M., Dittmann, Jason A., Ghosh, Sujit K., Montet, Benjamin T., and Newton, Elisabeth R., E-mail: iczekala@stanford.edu. Disentangling Time-series Spectra with Gaussian Processes: Applications to Radial Velocity Analysis. United States: N. p., 2017. Web. doi:10.3847/1538-4357/AA6AAB.
Czekala, Ian, Mandel, Kaisey S., Andrews, Sean M., Dittmann, Jason A., Ghosh, Sujit K., Montet, Benjamin T., & Newton, Elisabeth R., E-mail: iczekala@stanford.edu. Disentangling Time-series Spectra with Gaussian Processes: Applications to Radial Velocity Analysis. United States. doi:10.3847/1538-4357/AA6AAB.
Czekala, Ian, Mandel, Kaisey S., Andrews, Sean M., Dittmann, Jason A., Ghosh, Sujit K., Montet, Benjamin T., and Newton, Elisabeth R., E-mail: iczekala@stanford.edu. Mon . "Disentangling Time-series Spectra with Gaussian Processes: Applications to Radial Velocity Analysis". United States. doi:10.3847/1538-4357/AA6AAB.
@article{osti_22663640,
title = {Disentangling Time-series Spectra with Gaussian Processes: Applications to Radial Velocity Analysis},
author = {Czekala, Ian and Mandel, Kaisey S. and Andrews, Sean M. and Dittmann, Jason A. and Ghosh, Sujit K. and Montet, Benjamin T. and Newton, Elisabeth R., E-mail: iczekala@stanford.edu},
abstractNote = {Measurements of radial velocity variations from the spectroscopic monitoring of stars and their companions are essential for a broad swath of astrophysics; these measurements provide access to the fundamental physical properties that dictate all phases of stellar evolution and facilitate the quantitative study of planetary systems. The conversion of those measurements into both constraints on the orbital architecture and individual component spectra can be a serious challenge, however, especially for extreme flux ratio systems and observations with relatively low sensitivity. Gaussian processes define sampling distributions of flexible, continuous functions that are well-motivated for modeling stellar spectra, enabling proficient searches for companion lines in time-series spectra. We introduce a new technique for spectral disentangling, where the posterior distributions of the orbital parameters and intrinsic, rest-frame stellar spectra are explored simultaneously without needing to invoke cross-correlation templates. To demonstrate its potential, this technique is deployed on red-optical time-series spectra of the mid-M-dwarf binary LP661-13. We report orbital parameters with improved precision compared to traditional radial velocity analysis and successfully reconstruct the primary and secondary spectra. We discuss potential applications for other stellar and exoplanet radial velocity techniques and extensions to time-variable spectra. The code used in this analysis is freely available as an open-source Python package.},
doi = {10.3847/1538-4357/AA6AAB},
journal = {Astrophysical Journal},
number = 1,
volume = 840,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2017},
month = {Mon May 01 00:00:00 EDT 2017}
}
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