Automated Classification of Variable Stars in the Asteroseismology Program of the Kepler Space Mission

J Blomme; J Debosscher; J De Ridder; C Aerts; RL Gilliland; J Christensen-Dalsgaard; H Kjeldsen; TM Brown; WJ Borucki; D Koch; JM Jenkins; DW Kurtz; D Stello; Ian Stevens; MD Suran; A Derekas

doi:10.1088/2041-8205/713/2/L204

Automated Classification of Variable Stars in the Asteroseismology Program of the Kepler Space Mission

J Blomme, J Debosscher, J De Ridder, C Aerts, RL Gilliland, J Christensen-Dalsgaard, H Kjeldsen, TM Brown, WJ Borucki, D Koch, JM Jenkins, DW Kurtz, D Stello, Ian Stevens, MD Suran, A Derekas

Physics and Astronomy

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22 Citations (Scopus)

Abstract

We present the first results of the application of supervised classification methods to the Kepler Q1 long-cadence light curves of a subsample of 2288 stars measured in the asteroseismology program of the mission. The methods, originally developed in the framework of the CoRoT and Gaia space missions, are capable of identifying the most common types of stellar variability in a reliable way. Many new variables have been discovered, among which a large fraction are eclipsing/ellipsoidal binaries unknown prior to launch. A comparison is made between our classification from the Kepler data and the pre-launch class based on data from the ground, showing that the latter needs significant improvement. The noise properties of the Kepler data are compared to those of the exoplanet program of the CoRoT satellite. We find that Kepler improves on CoRoT by a factor of 2-2.3 in point-to-point scatter.

Original language	English
Pages (from-to)	L204-L207
Journal	Astrophysical Journal Letters
Volume	713
Issue number	2
DOIs	https://doi.org/10.1088/2041-8205/713/2/L204
Publication status	Published - 1 Apr 2010

Keywords

techniques: photometric
stars: variables: general
binaries: eclipsing
methods: data analysis
methods: statistical

Access to Document

10.1088/2041-8205/713/2/L204

Birmingham Astrophysics - Rolling Grant 2007-2012
Ponman, T., Cruise, M., Freise, A., Raychaudhury, S., Smith, G., Speake, C., Stevens, I. & Vecchio, A.
SCIENCE & TECHNOLOGY FACILITIES COUNCIL
1/04/07 → 31/03/12
Project: Research Councils

Cite this

Blomme, J., Debosscher, J., De Ridder, J., Aerts, C., Gilliland, RL., Christensen-Dalsgaard, J., Kjeldsen, H., Brown, TM., Borucki, WJ., Koch, D., Jenkins, JM., Kurtz, DW., Stello, D., Stevens, I., Suran, MD., & Derekas, A. (2010). Automated Classification of Variable Stars in the Asteroseismology Program of the Kepler Space Mission. Astrophysical Journal Letters, 713(2), L204-L207. https://doi.org/10.1088/2041-8205/713/2/L204

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title = "Automated Classification of Variable Stars in the Asteroseismology Program of the Kepler Space Mission",

abstract = "We present the first results of the application of supervised classification methods to the Kepler Q1 long-cadence light curves of a subsample of 2288 stars measured in the asteroseismology program of the mission. The methods, originally developed in the framework of the CoRoT and Gaia space missions, are capable of identifying the most common types of stellar variability in a reliable way. Many new variables have been discovered, among which a large fraction are eclipsing/ellipsoidal binaries unknown prior to launch. A comparison is made between our classification from the Kepler data and the pre-launch class based on data from the ground, showing that the latter needs significant improvement. The noise properties of the Kepler data are compared to those of the exoplanet program of the CoRoT satellite. We find that Kepler improves on CoRoT by a factor of 2-2.3 in point-to-point scatter.",

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author = "J Blomme and J Debosscher and {De Ridder}, J and C Aerts and RL Gilliland and J Christensen-Dalsgaard and H Kjeldsen and TM Brown and WJ Borucki and D Koch and JM Jenkins and DW Kurtz and D Stello and Ian Stevens and MD Suran and A Derekas",

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Blomme, J, Debosscher, J, De Ridder, J, Aerts, C, Gilliland, RL, Christensen-Dalsgaard, J, Kjeldsen, H, Brown, TM, Borucki, WJ, Koch, D, Jenkins, JM, Kurtz, DW, Stello, D, Stevens, I, Suran, MD & Derekas, A 2010, 'Automated Classification of Variable Stars in the Asteroseismology Program of the Kepler Space Mission', Astrophysical Journal Letters, vol. 713, no. 2, pp. L204-L207. https://doi.org/10.1088/2041-8205/713/2/L204

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T1 - Automated Classification of Variable Stars in the Asteroseismology Program of the Kepler Space Mission

AU - Blomme, J

AU - Debosscher, J

AU - De Ridder, J

AU - Aerts, C

AU - Gilliland, RL

AU - Christensen-Dalsgaard, J

AU - Kjeldsen, H

AU - Brown, TM

AU - Borucki, WJ

AU - Koch, D

AU - Jenkins, JM

AU - Kurtz, DW

AU - Stello, D

AU - Stevens, Ian

AU - Suran, MD

AU - Derekas, A

PY - 2010/4/1

Y1 - 2010/4/1

N2 - We present the first results of the application of supervised classification methods to the Kepler Q1 long-cadence light curves of a subsample of 2288 stars measured in the asteroseismology program of the mission. The methods, originally developed in the framework of the CoRoT and Gaia space missions, are capable of identifying the most common types of stellar variability in a reliable way. Many new variables have been discovered, among which a large fraction are eclipsing/ellipsoidal binaries unknown prior to launch. A comparison is made between our classification from the Kepler data and the pre-launch class based on data from the ground, showing that the latter needs significant improvement. The noise properties of the Kepler data are compared to those of the exoplanet program of the CoRoT satellite. We find that Kepler improves on CoRoT by a factor of 2-2.3 in point-to-point scatter.

AB - We present the first results of the application of supervised classification methods to the Kepler Q1 long-cadence light curves of a subsample of 2288 stars measured in the asteroseismology program of the mission. The methods, originally developed in the framework of the CoRoT and Gaia space missions, are capable of identifying the most common types of stellar variability in a reliable way. Many new variables have been discovered, among which a large fraction are eclipsing/ellipsoidal binaries unknown prior to launch. A comparison is made between our classification from the Kepler data and the pre-launch class based on data from the ground, showing that the latter needs significant improvement. The noise properties of the Kepler data are compared to those of the exoplanet program of the CoRoT satellite. We find that Kepler improves on CoRoT by a factor of 2-2.3 in point-to-point scatter.

KW - techniques: photometric

KW - stars: variables: general

KW - binaries: eclipsing

KW - methods: data analysis

KW - methods: statistical

U2 - 10.1088/2041-8205/713/2/L204

DO - 10.1088/2041-8205/713/2/L204

M3 - Article

SN - 2041-8213

VL - 713

SP - L204-L207

JO - Astrophysical Journal Letters

JF - Astrophysical Journal Letters

IS - 2

ER -

Automated Classification of Variable Stars in the Asteroseismology Program of the Kepler Space Mission

Abstract

Keywords

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Projects

Birmingham Astrophysics - Rolling Grant 2007-2012

Cite this