Photometric Optimal Extraction of Time Series Spectra
Author: Alexis Brandeker (alexis@astro.su.se)
This is a collection of routines that optimally extracts photometry from spectral time series from space telescopes where the PSF and pointing are stable.
These routines assume a basic reduction of data has been made and that the data are in the following format:
A) Flux data in a numpy array cube, with (frame#, row# (spatial direction), column# (wavelentgth direction))
B) The corresponding noise cube, containing uncertainties (1 std) for all data
C) A bad pixelmap, i.e. a 2D array of pixels that are bad in all frames. The format is a boolean 2D numpy array (row#, column#) fram where pixel is True if bad, False otherwise.
The routines are then used to
- Identify outliers (cosmic rays)
- Determine the trace of the spectrum in all frames, finding the relative offset in the spatial direction (due to jitter)
- Shift the region around the trace into a matrix where the spectral trace is approximately parallel to the rows
- Define linear correlation between pixel values and dx
- Extract photometry and error per column in frame
Also included is a class to mock data. Example code on how to run POETSS is given by the end of poetss.py.
Installation for POETSS can be done using setup.py file in the repository, by following commands below:
git clone https://github.com/alphapsa/POETSS.git
cd POETSS
python setup.py install
There you are! You are now ready to use this package!