J/PASJ/64/63     Dwarf novae characterization using SDSS colors   (Kato+, 2012)
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Characterization of dwarf novae using SDSS colors.
    Kato T., Maehara H., Uemura M.
   <Publ. Astron. Soc. Jap., 64, 63 (2012)>
   =2012PASJ...64...63K
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ADC_Keywords: Novae ; Photometry, SDSS
Keywords: methods: statistical - stars: dwarf novae - stars: evolution -
          stars: novae, cataclysmic variables - surveys

Abstract:
    We have developed a method for estimating the orbital periods of dwarf
    novae from the Sloan Digital Sky Survey (SDSS) colors in quiescence
    using an artificial neural network. For typical objects below the
    period gap with sufficient photometric accuracy, we were able to
    estimate the orbital periods with accuracy to a 1{sigma} error of
    22%. The error of the estimation is worse for systems with longer
    orbital periods. We have also developed a neural-network-based method
    for categorical classification. This method has proven to be efficient
    in classifying objects into three categories (WZ Sge type, SU UMa
    type, and SS Cyg/Z Cam type), and works for very faint objects to a
    limit of g=21mag. Using this method, we have investigated the
    distribution of the orbital periods of dwarf novae from a modern
    transient survey (Catalina Real-Time Survey).

File Summary:
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 FileName   Lrecl  Records   Explanations
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ReadMe         80        .   This file
table2.dat     45       41   Averaged P_orb_ estimates for objects with more
                              SDSS scans than four
table3.dat    114     1236   List of dwarf novae
table4.dat     98     1236   Estimated extinction and neural-network
                              classification
table5.dat     41      123   Catalina Real-Time Survey (CRTS) sources for the
                              estimation of the intrinsic P_orb_ distribution
table7.dat     92      243   Identification list of optical transients
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See also:
    http://nesssi.cacr.caltech.edu/catalina/ : Catalina Real-Time Survey (CRTS)

Byte-by-byte Description of file: table2.dat
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   Bytes Format Units   Label     Explanations
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   1- 25  A25   ---     Name      Object name
      26  A1    ---   n_Name      [+] Note on Name (G1)
  28- 29  I2    ---     N         Number of SDSS scans
  31- 35  F5.3  d       Pest      Average orbital period (P_orb_) estimate
      36  A1    ---     ---       [(]
  37- 38  I2    10-3d sdPest      Standard deviation of estimated P_orb_
      39  A1    ---     ---       [)]
  41- 45  F5.3  d     e_Pest      Mean error of neutral network analysis on Pest
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Byte-by-byte Description of file: table3.dat
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   Bytes Format Units   Label   Explanations
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   1- 26  A26   ---     Name    Object name
      27  A1    ---   n_Name    [+] Note on Name (G1)
  29- 34  F6.3  mag     umag    SDSS u magnitude
  36- 40  F5.3  mag   e_umag    rms uncertainty on umag
  43- 48  F6.3  mag     gmag    ?=- SDSS g magnitude
  50- 53  F4.3  mag   e_gmag    ? rms uncertainty on gmag
  56- 61  F6.3  mag     rmag    ?=- SDSS r magnitude
  63- 66  F4.3  mag   e_rmag    ? rms uncertainty on rmag
  69- 74  F6.3  mag     imag    ?=- SDSS i magnitude
  76- 80  F5.3  mag   e_imag    ? rms uncertainty on imag
  83- 88  F6.3  mag     zmag    SDSS z magnitude
  90- 94  F5.3  mag   e_zmag    rms uncertainty on zmag
  97-102  F6.4  d       Porb    ?=- Orbital period
     103  A1    ---   n_Porb    [h] h = Determined from superhump period
 105-109  F5.3  d       Pest    ?=- Orbital period estimated with neural network
 111-114  F4.3  d     e_Pest    ? rms uncertainty on Pest
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Byte-by-byte Description of file: table4.dat
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   Bytes Format Units   Label   Explanations
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   1- 26  A26   ---     Name    Object name
      27  A1    ---   n_Name    [+] Note on Name (G1)
  29- 34  F6.3  mag     gmag    ?=- SDSS g magnitude
  36- 39  F4.3  mag   e_gmag    ? rms uncertainty on gmag
  42- 46  I5    pc      Dist    Distance
      48  I1    ---   n_Dist    [1/6] Distance estimation method (2)
  50- 54  F5.3  mag     AV      Absorption in V band
  56- 61  F6.4  d       Porb    ?=- Orbital period
      62  A1    ---   n_Porb    [h] h: determined from superhump period
  64- 68  F5.3  ---     PU      [0/1]?=- Probability of being of WZ Sge type 
                                         (ultrashort period P_orb_<0.06d)
  70- 73  F4.3  ---   e_PU      [0/1]? rms uncertainty on PU
  76- 80  F5.3  ---     PS      [0/1]?=- Probability of being of SU UMa type
                                         (short period, 0.06<=P_orb_<0.10d)
  82- 86  F5.3  ---   e_PS      [0/1]? rms uncertainty on PS
  89- 93  F5.3  ---     PL      [0/1]?=- Probability of being of SS Cyg type
                                         (long period, P_orb_>=0.10d)
  95- 98  F4.3  ---   e_PL      [0/1]? rms uncertainty PL
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Note (2): Method of distance estimation as follows:
   1 = from Patterson (2011MNRAS.411.2695P) and other references (see text)
   2 = determined from Porb and maximum magnitude
   3 = from estimated Porb and maximum magnitude
   4 = from Porb and minimum magnitude
   5 = from estimated Porb and minimum magnitude
   6 = assuming maximum M_V_=~4.95
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Byte-by-byte Description of file: table5.dat
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   Bytes Format Units   Label     Explanations
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   1-  5  F5.1  min     Pest      Estimated period
       7  A1    ---   f_Pest      [~] Limit flag on e_Pest
   8- 11  F4.1  min   e_Pest      rms uncertainty on Pest
  13- 14  I2    ---     N         Number of outbursts recorded by CRTS
                                  (Catalina Real-Time Survey)
      15  A1    ---   u_N         Uncertainty flag on N
  18- 41  A24   ---     Name      Object name
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Byte-by-byte Description of file: table7.dat
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   Bytes Format Units   Label     Explanations
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   1- 19  A19   ---     Name      Object name
  21- 43  A23   ---     ID        Identification name
  45- 92  A48   ---     Disc      Discoverer
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Global notes:
Note (G1): + : Dwarf nova proposed by Wils et al., 2010MNRAS.402..436W.
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History:
    From electronic version of the journal

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(End)                                      Patricia Vannier [CDS]    19-Sep-2012
