J/A+A/529/A89 Kepler satellite variability study (Debosscher+, 2011)
Global stellar variability study in the field-of-view of the Kepler satellite.
Debosscher J., Blomme J., Aerts C., De Ridder J.
<Astron. Astrophys. 529, A89 (2011)>
=2011A&A...529A..89D 2011A&A...529A..89D
ADC_Keywords: Stars, variable - Binaries, eclipsing
Keywords: stars: variables: general - stars: statistics - binaries: eclipsing -
techniques: photometric - methods: statistical -
methods: data analysis
Abstract:
We present the results of an automated variability analysis of the
Kepler public data measured in the first quarter (Q1) of the mission.
In total, about 150000 light curves have been analysed to detect
stellar variability, and to identify new members of known variability
classes. We also focus on the detection of variables present in
eclipsing binary systems, given the important constraints on stellar
fundamental parameters they can provide. The methodology we use here
is based on the automated variability classification pipeline which
was previously developed for and applied successfully to the CoRoT
exofield database and to the limited subset of a few thousand Kepler
asteroseismology light curves. We use a Fourier decomposition of the
light curves to describe their variability behaviour and use the
resulting parameters to perform a supervised classification. Several
improvements have been made, including a separate extractor method to
detect the presence of eclipses when other variability is present in
the light curves. We also included two new variability classes
compared to previous work: variables showing signs of rotational
modulation and of activity. Statistics are given on the number of
variables and the number of good candidates per class. A comparison is
made with results obtained for the CoRoT exoplanet data. We present
some special discoveries, including variable stars in eclipsing binary
systems. Many new candidate non-radial pulsators are found, mainly
Delta Sct and Gamma Dor stars. We have studied those samples in more
detail by using 2MASS colours. The full classification results are
made available as an online catalogue.
Description:
Light curve parameters and classification results are presented for
the Kepler public data measured in the first quarter (Q1) of the
mission. The information presented here should allow scientists to
make candidate lists of their objects of study and to obtain some
basic light curve information. We recall that we did not perform
detailed light curve modelling, only a basic one, sufficient for
producing variability class memberships for each target. We refer to
Debosscher et al. (2011A&A...529A..89D 2011A&A...529A..89D, this paper) and Debosscher et
al. (2009, Cat. J/A+A/506/519) for a detailed description of the light
curve parameters, and the interpretation of the classification
results. Note that the classification results presented here, were
obtained using Kepler light curve information only. 2MASS colours were
only used afterwards to refine and evaluate the classification results
(as presented in the paper).
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
q1-class.dat 487 150256 Kepler Q1 classification results
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See also:
V/133 : Kepler Input Catalog (Kepler Mission Team, 2009)
II/246 : 2MASS All-Sky Catalog of Point Sources (Cutri+ 2003)
J/AcA/59/33 : ASAS. Variable stars catalog in Kepler field (Pigulski+, 2009)
J/A+A/517/A3 : Kepler early-type targets stellar parameters (Catanzaro+, 2010)
J/A+A/506/519 : Supervised classification of CoRoT variables (Debosscher+ 2009)
Byte-by-byte Description of file: q1-class.dat
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Bytes Format Units Label Explanations
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1- 4 A4 --- --- [kplr]
5- 13 I9 --- KIC KIC (Kepler Input Catalogue) Identifier
15- 21 F7.2 --- Mdist1 Mahalanobis distance to class 1 (1)
23- 29 F7.2 --- Mdist2 Mahalanobis distance to class 2 (1)
31- 37 F7.2 --- Mdist3 Mahalanobis distance to class 3 (1)
39- 46 F8.6 --- pV1 Class probability 1 (2)
48- 55 F8.6 --- pV2 Class probability 2 (2)
57- 64 F8.6 --- pV3 Class probability 3 (2)
66- 71 A6 --- V1 Class code 1 (3)
73- 78 A6 --- V2 Class code 2 (3)
80- 85 A6 --- V3 Class code 3 (3)
87- 94 F8.6 --- Pf1 [0/1] Significance parameter f1 (4)
96-103 F8.6 --- Pf2 [0/1] Significance parameter f2 (4)
105-112 F8.6 --- Pf3 [0/1] Significance parameter f3 (4)
114-126 F13.8 1/d f1 First (dominant) detected frequency
128-140 F13.8 1/d f2 Second detected frequency
142-154 F13.8 1/d f3 Third detected frequency
156-168 F13.8 mag amp11 Amplitude of f1
170-182 F13.8 mag amp12 Amplitude of 2*f1
184-196 F13.8 mag amp13 Amplitude of 3*f1
198-210 F13.8 mag amp14 Amplitude of 4*f1
212-224 F13.8 mag amp21 Amplitude of f2
226-238 F13.8 mag amp22 Amplitude of 2*f2
240-252 F13.8 mag amp23 Amplitude of 3*f2
254-266 F13.8 mag amp24 Amplitude of 4*f2
268-280 F13.8 mag amp31 Amplitude of f3
282-294 F13.8 mag amp32 Amplitude of 2*f3
296-308 F13.8 mag amp33 Amplitude of 3*f3
310-322 F13.8 mag amp34 Amplitude of 4*f3
324-336 F13.8 rad phd12 Phase of 2*f1, if phase of f1=0 (5)
338-350 F13.8 rad phd13 Phase of 3*f1, if phase of f1=0 (5)
352-364 F13.8 rad phd14 Phase of 4*f1, if phase of f1=0 (5)
366-378 F13.8 rad phd21 Phase of f2, if phase of f1=0 (5)
380-392 F13.8 rad phd22 Phase of 2*f2, if phase of f1=0 (5)
394-406 F13.8 rad phd23 Phase of 3*f2, if phase of f1=0 (5)
408-420 F13.8 rad phd24 Phase of 4*f2, if phase of f1=0 (5)
422-434 F13.8 rad phd31 Phase of f3, if phase of f1=0 (5)
436-448 F13.8 rad phd32 Phase of 2*f3, if phase of f1=0 (5)
450-462 F13.8 rad phd33 phase of 3*f3, if phase of f1=0 (5)
464-476 F13.8 rad phd34 phase of 4*f3, if phase of f1=0 (5)
478-485 F8.6 --- varred [0/1] Total variance reduction (6)
487 I1 --- ecl [0,1] Eclipse detection flag (7)
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Note (1): In short, the Mahalanobis distance is a multi-dimensional
generalisation of the one-dimensional statistical or standard distance
(e.g. distance to the mean value of a Gaussian in terms of sigma). This
distance can effectively be used to retain only the objects that are not too
far from the class centre in a statistical sense. It should be used together
with the probabilities, in order to select the best candidates. Using only the
probability values is usually insufficient to select the best candidates.
Consider the case e.g., where the probability for one class is 99% (0.99).
This high probability value seems to indicate a very certain class assignment.
However, these are only relative probabilities (see Note 2), and, even though
the relative probability for the class is very high, the object might still be
very far away from the class centre. If this is the case, the Mahalanobis
distance will have a large value, and one has to conclude that the object is
not a good candidate to belong to the class after all. A typical cutoff value
for this distance is 2 or 3 (think of outlier removal using 2 or 3-sigma
cutoff values). The smaller the cutoff value used, the more similar the
selected objects will be to the objects used to define the variability class.
Note (2): Relative probabilities for the three most likely class memberships.
Note (3): Corresponding to the three most likely variability class memberships,
in decreasing order of probability. The classes are (from Appendix A1):
BCEP = β-Cephei stars
CLCEP = Classical Cepheids (δ Cep)
DMCEP = Double-mode Cepheids
DSCUT = δ-Scuti stars
ECL = Eclipsing binaries (all types)
ELL = Ellipsoidal variables
GDOR = γ-Doradus stars
MIRA = Mira variables
RRAB = RR-Lyrae stars, subtype ab
RRC = RR-Lyrae stars, subtype c
RRD = Double-mode RR-Lyrae stars
RVTAU = RV-Tauri stars
SPB = Slowly pulsating B-stars
SR = Semi-regular variables
ROT = Rotational modulation
ACT = Active stars
MISC = Miscellaneous
Note (4): Significance parameters (P-values) resulting from a statistical
F-test. For each of the 3 detected frequencies, the reduction in variance
obtained by subtracting a least-squares fit with 4 harmonics is checked.
If this reduction is not significant (can be explained by noise), the
significance parameters will be close to 1, if the reduction is significant,
the values will be close to 0. We refer to Debosscher et al., (2009, Cat.
J/A+A/506/519) for a detailed description of these parameters.
Note (5): Phase in the range (-π, π).
Note (6): Total variance reduction of the trend-subtracted light curve, after
subtraction of the least-squares fits with the 3 frequencies, each with their
4 harmonics (this parameter has values close to 1 if the fit is good, close
to 0 if the fit is poor).
Note (7): Eclipse detection is 1 if possible eclipses have been detected
with the extractor method described in the paper (a form of outlier
detection); or 0 if possible eclipses have not been detected.
This flag complements the classification results for the detection of
eclipsing binaries.
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Acknowledgements:
Jonas Debosscher, jonas(at)ster.kuleuven.be
History:
* 08-Apr-2011: First version
* 10-Oct-2011: parameters of kplr011446443 corrected, from author
(End) Jonas Debosscher [IVS, K.U.Leuven], Patricia Vannier [CDS] 15-Feb-2011