kepstddev -- Calculate Combined Differential Photometric Precision for a time series light curve
kepstddev infile outfile datacol timescale clobber verbose logfile status
infile = string
The name of a
MAST standard format FITS file containing Kepler light curve data within the first data extension. While the kepstddev task will perform a calculation upon any data column in the first FITS extension of the input file, the output is only meaningful if the data column has first been normalized to a time-dependent model or function. For example, the kepflatten task removes astrophysical and systematic features in a light curve by fitting and normalizing to a running polynomial. The data output of kepflatten is a column named DETSAP_FLUX and is a suitable input column for kepstddev.
outfile = string
Name of the output FITS file containing the results of kepstddev. The output file is a direct copy of infile except for the addition of a new column called CDPPnn, where nn refers to the CDPP timescale. If the column CDPPnn exists already in the input file, then it will be overwritten in the output file.
datacol = String
The name of the FITS data column upon which to calculate CDPP. datacol must be a column within the FITS table extension of the light curve - the first extension of the input file. The time-series within datacol must be normalized, by e.g. the task kepflatten.
timescale = float
The characteristic timescale over which to calculate CDPP. The units are hours.
clobber = boolean (optional)
Overwrite the output file? if clobber = no and an existing file has the same name as outfile then the task will stop with an error.
verbose = boolean (optional)
Print informative messages and warnings to the shell and logfile?
logfile = string (optional)
Name of the logfile containing error and warning messages.
status = integer
Exit status of the script. It will be non-zero if the task halted with an
error. This parameter is set by the task and should not be modified by the
kepstddev calculates a simple form of the Combined Differential Photometric Precision (CDPP) often used to infer the detection limit for individual transit events within a photometric time-series. The form of CDPP calculated is described by Gilliland et al. (2011). For each data point in datacol with time t the normalized standard deviation is calculated across the time range (t - timescale / 2, t + timescale / 2) and divided by &sqrt;m, where m is the number of data points within the time range. The time-averaged median and RMS CDPP are derived from the output time-series data and written to output file as keywords - MCDPPnn and RCDPPnn. A plot of the time-series CDPP, median CDPP and RMS CDPP is provided at the end of the task - an example is provided in figure 1. Note well that the CDPP calculated in the Kepler pipeline and archived in the MAST data tables is not replicated by this task. The Kepler pipeline smooths time series data using wavelet functions, fills data gaps with synthetic data according to noise models and convolves the data with a broad function related to timescale before calculating CDPP.
- kepstddev infile=kplr011086270-2012088054726_llc.fits outfile=kplr011086270-2012088054726_std.fits datacol=DETSAP_FLUX timescale=2.0 clobber=y verbose=y logfile=kepstddev.log
Completion upon one light curve file image using a 2.66
GHz Intel Core 2 i7 Mac running OS 10.6.7, takes a few seconds.
BUGS AND LIMITATIONS
The Kepler PyRAF package is made available to the community through the Kepler Science Center at
http://keplerscience.arc.nasa.gov/PyKE.shtml. Please send bug reports and suggestions email@example.com.
Initial software release (MS)