Future of High-Dimensional Data-Driven Exoplanet Science

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

The detection and characterization of exoplanets has come a long way since the 1990's. For example, instruments specifically designed for Doppler planet surveys feature environmental controls to minimize instrumental effects and advanced calibration systems. Combining these instruments with powerful telescopes, astronomers have detected thousands of exoplanets. The application of Bayesian algorithms has improved the quality and reliability with which astronomers characterize the mass and orbits of exoplanets. Thanks to continued improvements in instrumentation, now the detection of extrasolar low-mass planets is limited primarily by stellar activity, rather than observational uncertainties. This presents a new set of challenges which will require cross-disciplinary research to combine improved statistical algorithms with an astrophysical understanding of stellar activity and the details of astronomical instrumentation. I describe these challenges and outline the roles of parameter estimation over high-dimensional parameter spaces, marginalizing over uncertainties in stellar astrophysics and machine learning for the next generation of Doppler planet searches.

Original languageEnglish (US)
Article number012007
JournalJournal of Physics: Conference Series
Volume699
Issue number1
DOIs
StatePublished - Apr 6 2016
EventInternational Meeting on High-Dimensional Data-Driven Science, HD3 2015 - Kyoto, Japan
Duration: Dec 14 2015Dec 17 2015

Fingerprint

extrasolar planets
stellar activity
planets
astrophysics
environmental control
machine learning
learning
telescopes
orbits

All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

Cite this

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abstract = "The detection and characterization of exoplanets has come a long way since the 1990's. For example, instruments specifically designed for Doppler planet surveys feature environmental controls to minimize instrumental effects and advanced calibration systems. Combining these instruments with powerful telescopes, astronomers have detected thousands of exoplanets. The application of Bayesian algorithms has improved the quality and reliability with which astronomers characterize the mass and orbits of exoplanets. Thanks to continued improvements in instrumentation, now the detection of extrasolar low-mass planets is limited primarily by stellar activity, rather than observational uncertainties. This presents a new set of challenges which will require cross-disciplinary research to combine improved statistical algorithms with an astrophysical understanding of stellar activity and the details of astronomical instrumentation. I describe these challenges and outline the roles of parameter estimation over high-dimensional parameter spaces, marginalizing over uncertainties in stellar astrophysics and machine learning for the next generation of Doppler planet searches.",
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Future of High-Dimensional Data-Driven Exoplanet Science. / Ford, Eric B.

In: Journal of Physics: Conference Series, Vol. 699, No. 1, 012007, 06.04.2016.

Research output: Contribution to journalConference article

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