J/MNRAS/VVV/pppp     Classification of Hipparcos variables     (Rimoldini+ 2012)
================================================================================
Automated classification of Hipparcos unsolved variables
     Rimoldini L., Dubath P., Suveges M., Lopez M., Sarro L.M., Blomme J., 
     De Ridder J., Cuypers J., Guy L., Mowlavi N., Lecoeur-Taibi I., Beck M., 
     Jan A., Nienartowicz K., Ordonez-Blanco D., Lebzelter T., Eyer L.
    <Mon. Not. R. Astron. Soc. VVV, pppp (2012)>
    =2012MNRAS.VVV.ppppR
================================================================================
ADC_Keywords: Photometry, classification ; Stars, variable
Mission_Name: Hipparcos

Keywords: methods: data analysis - catalogues - stars: variables: general 

Description:
    The Hipparcos catalogue (ESA 1997) and the AAVSO Variable Star 
    Index (Watson et al. 2011) are employed to complement the 
    training set of periodic variables of Dubath et al. (2011) with 
    irregular and non-periodic representatives, leading to 3881 
    sources in total which described 24 variability types. 
    The attributes employed to characterize light-curve features 
    are selected according to their relevance for classiﬁcation.
    Classifier models are produced with random forests and a 
    multi-stage methodology based on Bayesian networks, achieving 
    overall misclassification rates under 12 per cent. 
    Both classifiers are applied to predict variability types for 
    6051 Hipparcos variables associated with uncertain or missing
    types in the literature.

File Summary:
--------------------------------------------------------------------------------
  FileName   Lrecl  Records   Explanations
--------------------------------------------------------------------------------
ReadMe          80        .   This file
Table2.csv     109     3881   Training-set of Hipparcos variable stars
Table4.csv     100     6051   Test-set of stars to predict variability types for
Table5.csv      68     6051   Predictions of variability types
TableC1.csv    176     6051   Full random forest prediction probability arrays
TableC2.csv    176     6051   Full multi-stage Bayesian nets pred. prob. arrays
--------------------------------------------------------------------------------

See also:
    I/239 : The Hipparcos and Tycho Catalogues (ESA 1997)
    B/vsx : AAVSO International Variable Star Index VSX (Watson et al. 2011)
    J/MNRAS/414/2602 : Random forest automated supervised classification of
                       Hipparcos periodic variable stars (Dubath et al. 2011)

Byte-by-byte Description of file: Table2.csv
--------------------------------------------------------------------------------
    Bytes   Format Units    Label                  Explanations
--------------------------------------------------------------------------------
   1-   6   I6     ---      Hip                    [8,118307]+    Hipparcos ID
   8-  12   F5.2   mag      V-I                    [-0.27,9.03]   See Note (G1)
  14-  18   F5.2   ---      Skewness               [-6.13,6.45]   See Note (G2)
  20-  24   F5.2   [mag]    LogAmplitude           [-2.26,0.85]   See Note (G3)
  26-  32   F7.4   [d]      LogPeriod              [-1.4671,3]    See Note (G4)
  34-  39   F6.2   mag      AbsoluteMag            [-17.22,15.20] See Note (G5)
  41-  47   F7.2   ---      LogFAP                 [-163.91,0.00] See Note (G6)
  49-  53   F5.2   ---      LogP2PscatterFoldedRaw [-1.90,1.34]   See Note (G7)
  55-  59   F5.2   ---      LogQSOvar              [-0.37,3.05]   See Note (G8)
  61-  65   F5.2   ---      LogScatterRawRes       [-0.81,1.79]   See Note (G9)
  67-  71   F5.2   [mas]    LogParallax            [-2.50,2.89]   See Note (G10)
  73-  77   F5.2   [mag]    LogStdDevRes           [-2.58,-0.17]  See Note (G11)
  79-  83   F5.2   [mag]    LogShortVar            [-2.52,-0.38]  See Note (G12)
  85-  88   F4.2   ---      SumSqResRaw            [0.00,0.95]    See Note (G13)
  90-  94   F5.2   deg      AbsGLAT                [0.01,87.44]   See Note (G14)
  96-  99   F4.2   10000/d  FrequencyError         [0.02,5.30]    See Note (G15)
 101- 109   A9     ---      Type                   [!]            See Note (1)
--------------------------------------------------------------------------------
Note (1):
    Variability types mostly from the AAVSO Variable Star Index (Watson et al. 
    2011); other sources are detailed in Rimoldini et al. (2012).
--------------------------------------------------------------------------------

Byte-by-byte Description of file: Table4.csv
--------------------------------------------------------------------------------
    Bytes   Format Units    Label                  Explanations
--------------------------------------------------------------------------------
   1-   6   I6     ---      Hip                    [40,118318]+   Hipparcos ID
   8-  12   F5.2   mag      V-I                    [-0.34,5.44]   See Note (G1)
  14-  19   F6.2   ---      Skewness               [-11.71,10.02] See Note (G2)
  21-  25   F5.2   [mag]    LogAmplitude           [-2.31,0.56]   See Note (G3)
  27-  33   F7.4   [d]      LogPeriod              [-1.4760,3]    See Note (G4)
  35-  40   F6.2   mag      AbsoluteMag            [-15.19,12.33] See Note (G5)
  42-  47   F6.2   ---      LogFAP                 [-65.81,0.00]  See Note (G6)
  49-  53   F5.2   ---      LogP2PscatterFoldedRaw [-1.01,1.01]   See Note (G7)
  55-  59   F5.2   ---      LogQSOvar              [-0.60,2.05]   See Note (G8)
  61-  65   F5.2   ---      LogScatterRawRes       [-0.56,1.06]   See Note (G9)
  67-  71   F5.2   [mas]    LogParallax            [-2.20,2.46]   See Note (G10)
  73-  77   F5.2   [mag]    LogStdDevRes           [-2.54,-0.60]  See Note (G11)
  79-  83   F5.2   [mag]    LogShortVar            [-2.51,-0.68]  See Note (G12)
  85-  88   F4.2   ---      SumSqResRaw            [0.02,1.01]    See Note (G13)
  90-  94   F5.2   deg      AbsGLAT                [0.00,88.81]   See Note (G14)
  96- 100   F5.2   10000/d  FrequencyError         [0.08,50.79]   See Note (G15)
--------------------------------------------------------------------------------

Byte-by-byte Description of file: Table5.csv
--------------------------------------------------------------------------------
    Bytes   Format Units    Label                  Explanations
--------------------------------------------------------------------------------
    1-  6   I6     ---      Hip                    [40,118318]+   Hipparcos ID
    8-  9   A2     ---      HipparcosSet           [!]            See Note (1)
   11- 15   A5     ---      HipparcosType          [?]            See Note (2)
   17- 38   A22    ---      AAVSOtype              [?]            See Note (3)
   40- 48   A9     ---      PredictedTypeRF        [!]            See Note (4)
   50- 58   A9     ---      PredictedTypeMB        [!]            See Note (5)
   60- 63   F4.2   ---      ProbabilityRF          [0.10,1.00]    See Note (6)
   65- 68   F4.2   ---      ProbabilityMB          [0.16,1.00]    See Note (7)
--------------------------------------------------------------------------------
Note (1):
    The Hipparcos sets from which the sources have been selected
    (U1, U2, and M, as defined in Sec. 2 of Rimoldini et al. 2012).
Note (2):
    Variability types from literature as listed in the Hipparcos 
    catalogue (ESA 1997), when available.
Note (3):
    Variability types from literature included in the AAVSO Variable
    Star Index, Version 2011-01-16 (Watson et al. 2011), when 
    available.
Note (4):
    Variability types predicted by random forests (limited to single 
    types only).
Note (5):
    Variability types predicted by a multi-stage methodology based on
    Bayesian networks (limited to single types only).
Note (6):
    Probability of the variability type predicted by random forests.
Note (7):
    Probability of the variability type predicted by a multi-stage
    methodology based on Bayesian networks.
--------------------------------------------------------------------------------

Byte-by-byte Description of file: TableC1.csv
--------------------------------------------------------------------------------
    Bytes   Format Units    Label                  Explanations
--------------------------------------------------------------------------------
    1-  6   I6     ---      Hip                    [40,118318]+   Hipparcos ID
    8- 11   F4.2   ---      ProbabilityI_X         [0.00,0.62]    See Note (1)
   13- 16   F4.2   ---      ProbabilityLPV_P       [0.00,0.50]    See Note (2)
   18- 21   F4.2   ---      ProbabilityLPV_X       [0.00,1.00]    See Note (3)
   23- 26   F4.2   ---      ProbabilityRS+BY_P     [0.00,0.38]    See Note (4)
   28- 31   F4.2   ---      ProbabilityRS+BY_X     [0.00,0.97]    See Note (5)
   33- 36   F4.2   ---      ProbabilityBE+GCAS_P   [0.00,0.15]    See Note (6)
   38- 41   F4.2   ---      ProbabilityBE+GCAS_X   [0.00,0.97]    See Note (7)
   43- 46   F4.2   ---      ProbabilitySPB_P       [0.00,0.78]    See Note (8)
   48- 51   F4.2   ---      ProbabilityACV_P       [0.00,0.64]    See Note (9)
   53- 56   F4.2   ---      ProbabilityACV_X       [0.00,0.42]    See Note (10)
   58- 61   F4.2   ---      ProbabilityEA_P        [0.00,0.71]    See Note (11)
   63- 66   F4.2   ---      ProbabilityEA_X        [0.00,0.89]    See Note (12)
   68- 71   F4.2   ---      ProbabilityEB_P        [0.00,0.67]    See Note (13)
   73- 76   F4.2   ---      ProbabilityEW_P        [0.00,0.09]    See Note (14)
   78- 81   F4.2   ---      ProbabilityELL_P       [0.00,0.21]    See Note (15)
   83- 86   F4.2   ---      ProbabilityACYG_P      [0.00,0.52]    See Note (16)
   88- 91   F4.2   ---      ProbabilityACYG_X      [0.00,0.75]    See Note (17)
   93- 96   F4.2   ---      ProbabilityBCEP_P      [0.00,0.35]    See Note (18)
   98-101   F4.2   ---      ProbabilityBCEP_X      [0.00,0.48]    See Note (19)
  103-106   F4.2   ---      ProbabilityDCEPS_P     [0.00,0.09]    See Note (20)
  108-111   F4.2   ---      ProbabilityDCEP_P      [0.00,0.10]    See Note (21)
  113-116   F4.2   ---      ProbabilityCEP(B)_P    [0.00,0.05]    See Note (22)
  118-121   F4.2   ---      ProbabilityRRAB_P      [0.00,0.05]    See Note (23)
  123-126   F4.2   ---      ProbabilityRRC_P       [0.00,0.02]    See Note (24)
  128-131   F4.2   ---      ProbabilityGDOR_P      [0.00,0.44]    See Note (25)
  133-136   F4.2   ---      ProbabilityGDOR_X      [0.00,0.59]    See Note (26)
  138-141   F4.2   ---      ProbabilityDSCT_P      [0.00,0.22]    See Note (27)
  143-146   F4.2   ---      ProbabilityDSCT_X      [0.00,0.39]    See Note (28)
  148-151   F4.2   ---      ProbabilityDSCTC_P     [0.00,0.78]    See Note (29)
  153-156   F4.2   ---      ProbabilityDSCTC_X     [0.00,0.86]    See Note (30)
  158-161   F4.2   ---      ProbabilityCWA_P       [0.00,0.09]    See Note (31)
  163-166   F4.2   ---      ProbabilityCWB_P       [0.00,0.04]    See Note (32)
  168-171   F4.2   ---      ProbabilitySXARI_P     [0.00,0.15]    See Note (33)
  173-176   F4.2   ---      ProbabilityRV_P        [0.00,0.03]    See Note (34)
--------------------------------------------------------------------------------
Note (1):
    Probability of the source to be of type I_X, 
    as predicted by random forests.
Note (2):
    Probability of the source to be of type LPV_P,
    as predicted by random forests.
Note (3):
    Probability of the source to be of type LPV_X,
    as predicted by random forests.
Note (4):
    Probability of the source to be of type RS+BY_P,
    as predicted by random forests.
Note (5):
    Probability of the source to be of type RS+BY_X,
    as predicted by random forests.
Note (6):
    Probability of the source to be of type BE+GCAS_P,
    as predicted by random forests.
Note (7):
    Probability of the source to be of type BE+GCAS_X,
    as predicted by random forests.
Note (8):
    Probability of the source to be of type SPB_P,
    as predicted by random forests.
Note (9):
    Probability of the source to be of type ACV_P,
    as predicted by random forests.
Note (10):
    Probability of the source to be of type ACV_X,
    as predicted by random forests.
Note (11):
    Probability of the source to be of type EA_P,
    as predicted by random forests.
Note (12):
    Probability of the source to be of type EA_X,
    as predicted by random forests.
Note (13):
    Probability of the source to be of type EB_P,
    as predicted by random forests.
Note (14):
    Probability of the source to be of type EW_P,
    as predicted by random forests.
Note (15):
    Probability of the source to be of type ELL_P,
    as predicted by random forests.
Note (16):
    Probability of the source to be of type ACYG_P,
    as predicted by random forests.
Note (17):
    Probability of the source to be of type ACYG_X,
    as predicted by random forests.
Note (18):
    Probability of the source to be of type BCEP_P,
    as predicted by random forests.
Note (19):
    Probability of the source to be of type BCEP_X,
    as predicted by random forests.
Note (20):
    Probability of the source to be of type DCEPS_P,
    as predicted by random forests.
Note (21):
    Probability of the source to be of type DCEP_P,
    as predicted by random forests.
Note (22):
    Probability of the source to be of type CEP(B)_P,
    as predicted by random forests.
Note (23):
    Probability of the source to be of type RRAB_P,
    as predicted by random forests.
Note (24):
    Probability of the source to be of type RRC_P,
    as predicted by random forests.
Note (25):
    Probability of the source to be of type GDOR_P,
    as predicted by random forests.
Note (26):
    Probability of the source to be of type GDOR_X,
    as predicted by random forests.
Note (27):
    Probability of the source to be of type DSCT_P,
    as predicted by random forests.
Note (28):
    Probability of the source to be of type DSCT_X,
    as predicted by random forests.
Note (29):
    Probability of the source to be of type DSCTC_P,
    as predicted by random forests.
Note (30):
    Probability of the source to be of type DSCTC_X,
    as predicted by random forests.
Note (31):
    Probability of the source to be of type CWA_P,
    as predicted by random forests.
Note (32):
    Probability of the source to be of type CWB_P,
    as predicted by random forests.
Note (33):
    Probability of the source to be of type SXARI_P,
    as predicted by random forests.
Note (34):
    Probability of the source to be of type RV_P,
    as predicted by random forests.
--------------------------------------------------------------------------------

Byte-by-byte Description of file: TableC2.csv
--------------------------------------------------------------------------------
    Bytes   Format Units    Label                  Explanations
--------------------------------------------------------------------------------
    1-  6   I6     ---      Hip                    [40,118318]+   Hipparcos ID
    8- 11   F4.2   ---      ProbabilityI_X         [0.00,0.95]    See Note (1)
   13- 16   F4.2   ---      ProbabilityLPV_P       [0.00,0.60]    See Note (2)
   18- 21   F4.2   ---      ProbabilityLPV_X       [0.00,1.00]    See Note (3)
   23- 26   F4.2   ---      ProbabilityRS+BY_P     [0.00,0.54]    See Note (4)
   28- 31   F4.2   ---      ProbabilityRS+BY_X     [0.00,0.95]    See Note (5)
   33- 36   F4.2   ---      ProbabilityBE+GCAS_P   [0.00,0.38]    See Note (6)
   38- 41   F4.2   ---      ProbabilityBE+GCAS_X   [0.00,0.99]    See Note (7)
   43- 46   F4.2   ---      ProbabilitySPB_P       [0.00,0.95]    See Note (8)
   48- 51   F4.2   ---      ProbabilityACV_P       [0.00,0.85]    See Note (9)
   53- 56   F4.2   ---      ProbabilityACV_X       [0.00,0.86]    See Note (10)
   58- 61   F4.2   ---      ProbabilityEA_P        [0.00,0.87]    See Note (11)
   63- 66   F4.2   ---      ProbabilityEA_X        [0.00,0.99]    See Note (12)
   68- 71   F4.2   ---      ProbabilityEB_P        [0.00,0.80]    See Note (13)
   73- 76   F4.2   ---      ProbabilityEW_P        [0.00,0.18]    See Note (14)
   78- 81   F4.2   ---      ProbabilityELL_P       [0.00,0.40]    See Note (15)
   83- 86   F4.2   ---      ProbabilityACYG_P      [0.00,0.53]    See Note (16)
   88- 91   F4.2   ---      ProbabilityACYG_X      [0.00,0.84]    See Note (17)
   93- 96   F4.2   ---      ProbabilityBCEP_P      [0.00,0.41]    See Note (18)
   98-101   F4.2   ---      ProbabilityBCEP_X      [0.00,0.89]    See Note (19)
  103-106   F4.2   ---      ProbabilityDCEPS_P     [0.00,0.09]    See Note (20)
  108-111   F4.2   ---      ProbabilityDCEP_P      [0.00,0.16]    See Note (21)
  113-116   F4.2   ---      ProbabilityCEP(B)_P    [0.00,0.05]    See Note (22)
  118-121   F4.2   ---      ProbabilityRRAB_P      [0.00,0.07]    See Note (23)
  123-126   F4.2   ---      ProbabilityRRC_P       [0.00,0.10]    See Note (24)
  128-131   F4.2   ---      ProbabilityGDOR_P      [0.00,0.66]    See Note (25)
  133-136   F4.2   ---      ProbabilityGDOR_X      [0.00,0.85]    See Note (26)
  138-141   F4.2   ---      ProbabilityDSCT_P      [0.00,0.52]    See Note (27)
  143-146   F4.2   ---      ProbabilityDSCT_X      [0.00,0.92]    See Note (28)
  148-151   F4.2   ---      ProbabilityDSCTC_P     [0.00,0.94]    See Note (29)
  153-156   F4.2   ---      ProbabilityDSCTC_X     [0.00,0.94]    See Note (30)
  158-161   F4.2   ---      ProbabilityCWA_P       [0.00,0.21]    See Note (31)
  163-166   F4.2   ---      ProbabilityCWB_P       [0.00,0.17]    See Note (32)
  168-171   F4.2   ---      ProbabilitySXARI_P     [0.00,0.31]    See Note (33)
  173-176   F4.2   ---      ProbabilityRV_P        [0.00,0.01]    See Note (34)
--------------------------------------------------------------------------------
Note (1):
    Probability of the source to be of type I_X,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (2):
    Probability of the source to be of type LPV_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (3):
    Probability of the source to be of type LPV_X,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (4):
    Probability of the source to be of type RS+BY_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (5):
    Probability of the source to be of type RS+BY_X,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (6):
    Probability of the source to be of type BE+GCAS_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (7):
    Probability of the source to be of type BE+GCAS_X,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (8):
    Probability of the source to be of type SPB_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (9):
    Probability of the source to be of type ACV_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (10):
    Probability of the source to be of type ACV_X,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (11):
    Probability of the source to be of type EA_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (12):
    Probability of the source to be of type EA_X,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (13):
    Probability of the source to be of type EB_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (14):
    Probability of the source to be of type EW_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (15):
    Probability of the source to be of type ELL_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (16):
    Probability of the source to be of type ACYG_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (17):
    Probability of the source to be of type ACYG_X,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (18):
    Probability of the source to be of type BCEP_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (19):
    Probability of the source to be of type BCEP_X,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (20):
    Probability of the source to be of type DCEPS_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (21):
    Probability of the source to be of type DCEP_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (22):
    Probability of the source to be of type CEP(B)_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (23):
    Probability of the source to be of type RRAB_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (24):
    Probability of the source to be of type RRC_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (25):
    Probability of the source to be of type GDOR_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (26):
    Probability of the source to be of type GDOR_X,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (27):
    Probability of the source to be of type DSCT_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (28):
    Probability of the source to be of type DSCT_X,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (29):
    Probability of the source to be of type DSCTC_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (30):
    Probability of the source to be of type DSCTC_X,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (31):
    Probability of the source to be of type CWA_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (32):
    Probability of the source to be of type CWB_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (33):
    Probability of the source to be of type SXARI_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
Note (34):
    Probability of the source to be of type RV_P,
    as predicted by a multi-stage methodology based on Bayesian networks.
--------------------------------------------------------------------------------

Global Notes:
Note (G1):
    The reddened V-I colour index in Cousins' system, as provided by 
    ESA (1997).
Note (G2):
    The unbiased skewness of the distribution of Hipparcos magnitudes,
    weighted by the inverse of squared measurement uncertainties. 
Note (G3):
    The decadic logarithm of the difference between the faintest and 
    the brightest values of the light-curve model.
Note (G4):
    The decadic logarithm of the period computed with the generalized
    Lomb-Scargle method (Zechmeister & Kurster 2009) for sources with
    weighted skewness of the magnitude distribution smaller than 1.6.
    Periods of sources with skewness greater than 1.6 are computed
    with the classical (unweighted) Lomb-Scargle method (Lomb 1976;
    Scargle 1982). Limitations regarding the recovered periods are
    described in Sec. 4.2 of Rimoldini et al. (2012).
Note (G5):
    The absolute magnitude in the Hipparcos band employing the 
    parallax described in Note (G10) and neglecting interstellar 
    absorption. 
Note (G6):
    The decadic logarithm of the probability that the maximum peak in
    the Lomb-Scargle periodogram (Scargle 1982) is due to noise 
    rather than the true signal, employing the beta distribution as 
    indicated by Schwarzenberg-Czerny (1998). The computation assumed 
    a number of independent frequencies equal to the number of 
    frequencies tested divided by an oversampling factor (estimated 
    by the largest value between one and the inverse of the product 
    of the frequency spacing employed and the time-series duration).
Note (G7):
    The decadic logarithm of the reduced chi-square of the source 
    variability with respect to a parametrized quasar variance model,
    denoted by {chi}^2^_QSO_/{nu} in Butler & Bloom (2011). 
    Following Richards et al. (2011), the parameter values employed 
    for the Hipparcos data correspond to the SDSS g-band at fixed 
    magnitude of 19. 
Note (G8):
    The decadic logarithm of the point-to-point scatter of the time 
    series folded with twice the recovered period (measured by the sum
    of squared magnitude differences between successive measurements 
    in phase) divided by the same quantity computed on the raw time 
    series (i.e., with respect to successive measurements in time). 
Note (G9):
    The decadic logarithm of the ratio between the median of absolute
    deviations from the median of the raw time series and the median
    of absolute values of the residual time series (obtained by
    subtracting model values from the raw time series).
Note (G10):
    The decadic logarithm of the parallax value from the new
    reduction of the Hipparcos raw data (van Leeuwen 2007).
    Non-positive values of parallax are replaced by positive values 
    randomly extracted from a Gaussian distribution with zero mean 
    and standard deviation equal to the measurement uncertainty. 
Note (G11):
    The decadic logarithm of the unbiased standard deviation of the 
    residual time series, weighted by the inverse of squared 
    measurement uncertainties.
Note (G12):
    The decadic logarithm of the average of absolute values of 
    magnitude differences between all pairs of measurements separated 
    by time-scales from 0.01 to 0.1 day.
Note (G13):
    The ratio between the sum of squared residuals of the model from 
    the raw data and the sum of squared deviations of the raw time 
    series from its mean value.
Note (G14):
    The absolute value of the Galactic latitude of the source 
    position.
Note (G15):
    The error estimate of the derived frequency (multiplied by 10000),
    under the assumption of equidistant observations of a sinusoidal 
    signal (Kovacs 1981; Baliunas et al. 1985; Gilliland & Fisher 1985).       
--------------------------------------------------------------------------------

References:
  Baliunas et al., 1985ApJ...294..310B
  Butler & Bloom, 2011AJ....141...93B
  Dubath et al., 2011MNRAS.414.2602D
  ESA, 1997yCat.1239....0E
  Gilliland & Fisher, 1985PASP...97..285G
  Kovacs, 1981Ap&SS..78..175K
  Lomb, 1976Ap&SS..39..447L
  Richards et al., 2011ApJ...733...10R
  Scargle, 1982ApJ...263..835S
  Schwarzenberg-Czerny, 1998MNRAS.301..831S
  van Leeuwen, 2007ASSL..350.....V, 2007A&A...474..653V
  Watson, Henden, Price, 2011yCat....102027W
  Zechmeister & Kurster, 2009A&A...496..577Z
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(End)    Lorenzo Rimoldini [Geneva Observatory/ISDC, Switzerland]    15-Jul-2012
