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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
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, 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:
FileName Lrecl Records Explanations
ReadMe 80 . This file q1-class.dat 487 150256 Kepler Q1 classification results
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
Bytes Format Units Label Explanations
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)
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.
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
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