J/A+A/550/A120 Variability classification of CoRoT targets (Sarro+, 2013)
Improved variability classification of CoRoT targets with Giraffe spectra.
Sarro L.M., Debosscher J., Neiner C., Bello-Garcia A., Gonzalez-Marcos A.,
Prendes-Gero B., Ordieres J., Leon G., Aerts C., de Batz B.
<Astron. Astrophys. 550, A120 (2013)>
=2013A&A...550A.120S 2013A&A...550A.120S
ADC_Keywords: Stars, variable ; Photometry, classification
Keywords: stars: variables: general - stars: oscillations -
techniques: spectroscopic - stars: fundamental parameters -
methods: statistical - methods: data analysis
Abstract:
We present an improved method for automated stellar variability
classification, using fundamental parameters derived from high
resolution spectra, with the goal to improve the variability
classification obtained using information derived from CoRoT light
curves only. Although we focus on Giraffe spectra and CoRoT light
curves in this work, the methods are much more widely applicable. In
order to improve the variability classification obtained from the
photometric time series, only rough estimates of the stellar physical
parameters (Teff and logg) are needed because most variability types
that overlap in the space of time series parameters, are well
separated in the space of physical parameters (e.g. γ Dor/SPB or
δ Sct/β Cep). In this work, several state-of-the-art
machine learning techniques are combined to estimate these fundamental
parameters from high resolution Giraffe spectra. Next, these
parameters are used in a multi-stage Gaussian-Mixture classifier to
perform an improved supervised variability classification of CoRoT
light curves. The variability classifier can be used independently of
the regression module that estimates the physical parameters, so that
non-spectroscopic estimates derived e.g. from photometric colour
indices can be used instead. Teff and logg are derived from Giraffe
spectra, for 6832 CoRoT targets. The use of those parameters in
addition to information extracted from the CoRoT light curves,
significantly improves the results of our previous automated stellar
variability classification. Several new pulsating stars are identified
with high confidence levels, including hot pulsators such as SPB and
β Cep, and several γ Dor-δ Sct hybrids. From our
samples of new γ Dor and δ Sct stars, we find strong
indications that the instability domains for both types of pulsators
are larger than previously thought.
Description:
This table contains the parameters used to classify CoRoT targets
observed with the Giraffe spectrograph at the Very Large Telescope
VLT. It contains both parameters derived from the photometric time
series and physical parameters (Teff and logg) derived from the
spectra. We also include the final classification obtained with these
parameters.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table2.dat 119 10134 Catalog of time series and physical parameters
for 6834 CoRoT observations
(corrected version, 17-Feb-2025)
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See also:
B/corot : CoRoT observation log Release 10 (CoRoT, 2012)
J/MNRAS/358/30 : Automated classification of ASAS variables (Eyer+, 2005)
J/A+A/475/1159 : Variable stars supervised classification (Debosscher+, 2008)
J/A+A/494/739 : Automatic classification of OGLE variables (Sarro+, 2009)
J/A+A/506/519 : CoRoT variables Supervised classification (Debosscher+ 2009)
J/AJ/138/466 : NSVS variables automated classification (Hoffman+, 2009)
J/MNRAS/414/2602 : Automated classification of HIP variables (Dubath+, 2011)
J/A+A/538/A76 : Automatic stellar spectral classification (Navarro+, 2012)
J/MNRAS/427/2917 : HIP variables automated classification (Rimoldini+, 2012)
Byte-by-byte Description of file: table2.dat
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Bytes Format Units Label Explanations
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1- 9 I9 --- CoRoT CoRoT identifier (B/corot)
11- 15 A5 --- Run Run
17- 24 F8.4 deg RAdeg Right ascension (J2000)
26- 33 F8.5 deg DEdeg Declination (J2000)
35- 41 F7.3 mag Vmag ?=- V magnitude
43- 48 A6 --- Class Variability type (1)
50- 53 F4.2 --- Prob [0/1] Probability of class
55- 59 F5.2 --- MD Mahalanobis distance to class center
61- 68 F8.4 d-1 nu1 First detected frequency
70- 77 F8.4 d-1 nu2 Second detected frequency
79- 84 F6.4 mag a11 Amplitude of the first term in the Fourier
series of nu1
86- 91 F6.4 mag a21 Amplitude of the first term in the Fourier
series of nu2
93- 96 F4.2 --- p1 [0/1] p-value1 for nu1 in hypothesis test
98-101 F4.2 --- p2 [0/1] p-value2 for nu2 in hypothesis test
103-108 I6 K Teff1 KT model effective temperature
110-112 F3.1 [cm/s2] logg log(g) from KT model
114-119 I6 K Teff2 ELODIE model effective temperature
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Note (1): Variability type as follows:
ROT = Rotational modulation
ACT = Activity
SPB = Slowly Pulsating B star
DSCUT = δ Scuti stars
GDOR = γ Doradus stars
BCEP = β Cephei stars
ECL = Eclipsing binary
MISC = Miscellaneous variable
RVTAU = RV Tauri
ELL = Ellipsoidal variable
RRAB = RR Lyrae subtype ab (fundamental)
RRD = RR Lyrae subtype d (first overtone)
RRC = RR Lyrae subtype c (several modes)
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Acknowledgements:
Luis Sarro, lsb(at)dia.uned.es
History:
05-Feb-2013: on-line version
17-Feb-2025: corrected table2
(End) Luis Sarro [UNED], Patricia Vannier [CDS] 21-Dec-2012