ApJdataFrames

Devor et al. 2008

Title: IDENTIFICATION, CLASSIFICATIONS, AND ABSOLUTE PROPERTIES OF 773 ECLIPSING BINARIES FOUND IN THE TRANS-ATLANTIC EXOPLANET SURVEY
Authors: Jonathan Devor, David Charbonneau, Francis T O'Donovan, Georgi Mandushev, and Guillermo Torres

Data is from this paper:
http://iopscience.iop.org/article/10.1088/0004-6256/135/3/850/


In [1]:
import pandas as pd

In [2]:
from astropy.io import ascii, votable, misc

Download Data


In [3]:
#! mkdir ../data/Devor2008

In [4]:
#! curl http://iopscience.iop.org/1538-3881/135/3/850/suppdata/aj259648_mrt7.txt >> ../data/Devor2008/aj259648_mrt7.txt

In [5]:
! du -hs ../data/Devor2008/aj259648_mrt7.txt


296K	../data/Devor2008/aj259648_mrt7.txt

Not too big at all.

Data wrangle-- read in the data


In [6]:
dat = ascii.read('../data/Devor2008/aj259648_mrt7.txt')

In [7]:
! head ../data/Devor2008/aj259648_mrt7.txt


Title: Identification, Classifications, and Absolute Properties of 773 Eclipsing 
       Binaries Found in the TrES Survey 
Authors: Devor J., Charbonneau D., O'Donovan F.T., Mandushev G., Torres G. 
Table: Eclipsing Binary catalog
================================================================================
Byte-by-byte Description of file: datafile7.txt
--------------------------------------------------------------------------------
   Bytes Format Units     Label  Explanations
--------------------------------------------------------------------------------
   1- 13 A13    ---       Cat    Category (see Section 2)

In [8]:
dat.info


//anaconda/lib/python3.4/site-packages/astropy/table/column.py:268: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  return self.data.__eq__(other)
Out[8]:
<Table length=876>
 name   dtype     unit                          description                        
------ ------- --------- ----------------------------------------------------------
   Cat   str13                                             Category (see Section 2)
  Name   str12                                               Binary designation (1)
   RAh   int64         h                            Hour of Right Ascension (J2000)
   RAm   int64       min                          Minute of Right Ascension (J2000)
   RAs float64         s                          Second of Right Ascension (J2000)
   DEd   int64       deg                              Degree of Declination (J2000)
   DEm   int64    arcmin                           Arcminute of Declination (J2000)
   DEs float64    arcsec                           Arcsecond of Declination (J2000)
   Per float64         d                                             Orbital period
 e_Per float64         d                                         Uncertainty in Per
    M1 float64 [solMass]                  Primary (more massive) component mass (2)
 errM1 float64 [solMass]                                      Uncertainty in M1 (3)
    M2 float64 [solMass]                Secondary (less massive) component mass (2)
 errM2 float64 [solMass]                                      Uncertainty in M2 (3)
   Age float64       Gyr                      System age (assumed to be coeval) (2)
errAge float64       Gyr                                     Uncertainty in Age (3)
 Score float64                        Weighted reduced {chi}^2^ of MECI model (2,4)
   Iso    str7                                             Isochrone table used (5)
Weight float64                                      Relative weight of LC fit (2,4)
    PM    str7                               Proper motion measurement database (6)
  pmRA float64  mas / yr                 Right Ascension component of proper motion
  pmDE float64  mas / yr                     Declination component of proper motion
Lerror float64    arcsec       Distance between this work and propermotion database
  Bmag float64       mag                                USNO-B B-band magnitude (7)
  Rmag float64       mag                                USNO-B R-band magnitude (7)
   3rd float64                             Fraction of third-light flux blended (8)
  Jmag float64       mag             2MASS J-band magnitude converted to ESO J-band
  Hmag float64       mag             2MASS H-band magnitude converted to ESO H-band
  Kmag float64       mag             2MASS K-band magnitude converted to ESO K-band
  JMag float64       mag      Absolute ESO J-band magnitude fromisochrone table (2)
  HMag float64       mag      Absolute ESO H-band magnitude fromisochrone table (2)
  KMag float64       mag      Absolute ESO K-band magnitude fromisochrone table (2)
   Dis float64        pc       Distance from extinction-correctdistance modulus (2)
    AV float64       mag                         Galactic ISM V-band absorption (2)
  sini float64                              The sine of the orbital inclination (2)
 ecosw float64                      Robust lower eccentricity limit; see equ. 1 (2)
    Ec float64                                             Orbital eccentricity (2)
 errEc float64                                           Uncertainty in Eccen (3,9)
 Delm1 float64       mag                  R-band primary (deeper) eclipse depth (2)
Epoch1 float64         d   Heliocentric Julian Date at center ofprimary eclipse (2)
 Delm2 float64       mag             R-band secondary (shallower) eclipse depth (2)
Epoch2 float64         d Heliocentric Julian Date at center ofsecondary eclipse (2)

In [9]:
df = dat.to_pandas()

In [10]:
df.head()


Out[10]:
Cat Name RAh RAm RAs DEd DEm DEs Per e_Per ... Dis AV sini ecosw Ec errEc Delm1 Epoch1 Delm2 Epoch2
0 Circular T-And0-00194 1 20 12.816 48 36 41.36 2.144842 0.000082 ... 429.310 0.075 0.99673 0.0 0.0 -1.0 0.200 52908.101 0.038 52907.029
1 Circular T-And0-00459 1 11 24.845 46 57 49.44 3.655130 0.000240 ... 373.150 0.431 0.99786 0.0 0.0 -1.0 0.475 52905.439 0.462 52907.266
2 Ambig-equal T-And0-00657 1 6 6.159 47 31 59.37 13.455900 0.003200 ... 784.911 0.028 0.99832 0.0 0.0 -1.0 0.134 52903.924 0.134 52897.196
3 Ambig-unequal T-And0-00657 1 6 6.159 47 31 59.37 6.724600 0.003200 ... 777.616 0.207 0.99954 0.0 0.0 -1.0 0.117 52903.929 0.007 52907.291
4 Circular T-And0-00745 1 3 45.076 44 50 41.14 2.851320 0.000150 ... 746.666 0.000 0.97895 0.0 0.0 -1.0 0.053 52906.421 0.021 52907.847

5 rows × 42 columns


In [11]:
df.columns


Out[11]:
Index(['Cat', 'Name', 'RAh', 'RAm', 'RAs', 'DEd', 'DEm', 'DEs', 'Per', 'e_Per',
       'M1', 'errM1', 'M2', 'errM2', 'Age', 'errAge', 'Score', 'Iso', 'Weight',
       'PM', 'pmRA', 'pmDE', 'Lerror', 'Bmag', 'Rmag', '3rd', 'Jmag', 'Hmag',
       'Kmag', 'JMag', 'HMag', 'KMag', 'Dis', 'AV', 'sini', 'ecosw', 'Ec',
       'errEc', 'Delm1', 'Epoch1', 'Delm2', 'Epoch2'],
      dtype='object')

In [12]:
sns.distplot(df.Per, norm_hist=False, kde=False)


Out[12]:
<matplotlib.axes._subplots.AxesSubplot at 0x11d38a240>

Look for LkCa 4


In [13]:
gi = (df.RAh == 4) & (df.RAm == 16) & (df.DEd == 28) & (df.DEm == 7)

In [14]:
gi.sum()


Out[14]:
1

In [15]:
df[gi].T


Out[15]:
782
Cat Roche-filled
Name T-Tau0-01262
RAh 4
RAm 16
RAs 28.109
DEd 28
DEm 7
DEs 35.81
Per 6.74215
e_Per 0.00067
M1 -99
errM1 -99
M2 -99
errM2 -99
Age -99
errAge -99
Score -99
Iso N/A
Weight -99
PM UCAC
pmRA 7.8
pmDE -30.1
Lerror 0.24
Bmag 14.44
Rmag 11.825
3rd 0.0267
Jmag 9.323
Hmag 8.538
Kmag 8.362
JMag -99
HMag -99
KMag -99
Dis -99
AV -99
sini -99
ecosw -99
Ec -99
errEc -99
Delm1 0.543
Epoch1 53739.8
Delm2 0.469
Epoch2 53736.4

The source is named T-Tau0-01262

The light curve files have the following 3-column format:
Column 1 - the Heliocentric Julian date (HJD), minus 2400000
Column 2 - normalized r-band magnitude
Column 3 - magnitude uncertainty


In [16]:
! head ../data/Devor2008/T-Tau0-01262.lc


53702.7461238000  0.467890 0.003853
53702.7528206000  0.471913 0.003889
53702.7595056000  0.466458 0.003898
53702.7659976667  0.464268 0.004887
53702.7713506000  0.467648 0.003805
53702.7780384000  0.473767 0.003864
53702.7846095000  0.463499 0.004236
53702.7905007500  0.471051 0.004200
53702.7965172000  0.474429 0.003888
53702.8025465000  0.465546 0.004299

In [17]:
cols = ['HJD-2400000', 'r_band', 'r_unc']
lc_raw = pd.read_csv('../data/Devor2008/T-Tau0-01262.lc', names=cols, delim_whitespace=True)

In [18]:
lc_raw.head()


Out[18]:
HJD-2400000 r_band r_unc
0 53702.746124 0.467890 0.003853
1 53702.752821 0.471913 0.003889
2 53702.759506 0.466458 0.003898
3 53702.765998 0.464268 0.004887
4 53702.771351 0.467648 0.003805

In [19]:
lc_raw.count()


Out[19]:
HJD-2400000    1171
r_band         1171
r_unc          1171
dtype: int64

In [20]:
sns.set_context('talk')

In [21]:
plt.plot(lc_raw['HJD-2400000'], lc_raw.r_band, '.')
plt.ylim(0.6, -0.6)


Out[21]:
(0.6, -0.6)

In [22]:
plt.plot(np.mod(lc_raw['HJD-2400000'], 3.375)/3.375, lc_raw.r_band, '.', alpha=0.5)
plt.xlabel('phase')
plt.ylabel('$\Delta \;\; r$')
plt.ylim(0.6, -0.6)


Out[22]:
(0.6, -0.6)

In [23]:
plt.plot(np.mod(lc_raw['HJD-2400000'], 6.74215), lc_raw.r_band, '.')
plt.ylim(0.6, -0.6)


Out[23]:
(0.6, -0.6)

The Devor et al. period is just twice the photometric period of 3.375 days.
Are those large vertical drops flares?


In [30]:
! ls /Users/gully/Downloads/catalog/T-Tau0-* | head -n 10


/Users/gully/Downloads/catalog/T-Tau0-00397.lc
/Users/gully/Downloads/catalog/T-Tau0-00686.lc
/Users/gully/Downloads/catalog/T-Tau0-00722.lc
/Users/gully/Downloads/catalog/T-Tau0-00781.lc
/Users/gully/Downloads/catalog/T-Tau0-00883.lc
/Users/gully/Downloads/catalog/T-Tau0-01025.lc
/Users/gully/Downloads/catalog/T-Tau0-01104.lc
/Users/gully/Downloads/catalog/T-Tau0-01266.lc
/Users/gully/Downloads/catalog/T-Tau0-01292.lc
/Users/gully/Downloads/catalog/T-Tau0-01540.lc

In [34]:
lc2 = pd.read_csv('/Users/gully/Downloads/catalog/T-Tau0-00397.lc', names=cols, delim_whitespace=True)
plt.plot(lc2['HJD-2400000'], lc2.r_band, '.')
plt.ylim(0.6, -0.6)


Out[34]:
(0.6, -0.6)

In [46]:
this_p = df.Per[df.Name == 'T-Tau0-00397']
plt.plot(np.mod(lc2['HJD-2400000'], this_p), lc2.r_band, '.', alpha=0.5)
plt.xlabel('phase')
plt.ylabel('$\Delta \;\; r$')
plt.ylim(0.6, -0.6)


Out[46]:
(0.6, -0.6)

In [ ]: