MuSCAT2 photometry and transit analysis pipelines

Python-based photometry and transit analysis pipelines for MuSCAT2 developed in collaboration with the Instituto de Astrofísica de Canarias (IAC), University of Tokyo (UT), National Astronomical Observatory of Japan (NAOJ), The Graduate University for Advanced Studies (SOKENDAI), and Astrobiology Center (ABC).

The pipeline consists of a set of executable scripts and two Python packages: muscat2ph for photometry, and muscat2ta for transit analysis. The MuSCAT2 photometry can be carried out using the scripts only. The transit analysis can also in most cases be done using the main transit analysis script m2fit, but the muscat2ta package also offers high-level classes that can be used to carry out more customised transit analysis as a Python script (or Jupyter notebook).

Overview

Photometry

MuSCAT2 photometry pipeline consists of three steps, each carried out by an executable script

1. m2organize organises the observed raw frames into a logical directory hierarchy,
2. m2astrometry calculates the astrometry for the science frames using astrometry.net,
3. m2photometry calculates the aperture photometry.

The first two steps are meant to be run on per-night basis. First, m2organize is used to organise the frames from one night of observations in a directory yymmdd, into a directory hierarchy where the calibration and science frames are separated, different objects are separated, and different CCDs are separated as

yymmdd
|- calib
 |- flat
  |- [g, r, y, z]
 |- dark
  |- [g, r, y, z]
|- obj
 |- obj1name
  |- [g, r, y, z]
 |- obj2name
  |- [g, r, y, z]
 |- objnname
  |- [g, r, y, z]

Note that m2organize does not delete or overwrite the original raw data, but creates a new copy for each night.

Next, m2astrometry is ran on the organised directory. The script is a parallel wrapper over astrometry.net that calculates the astrometric solution for each science frame filename.fits, and stores it in a separate filename.wcs sidecar file.

Finally, m2photometry is ran on the organised directory to calculate either the photometry for all the targets observed during a single night, or for a single target if more customised photometry is required.

Transit Analysis

The transit analysis pipeline offers an executable script m2fit that can be used to carry out multicolour transit analysis, and also a set of high-level classes that make writing customised transit analysis scripts straightforward.

Transit analysis consists of a set of basic steps

1. Define the problem
2. Select the optimal comparison stars and the optimal target and comparison apertures
3. Fit a transit using a linear systematics model (LM)
4. Learn the GP hyperparameters from the data after removing the LM transit
5. Fit a transit using a GP systematics model (GPM)
6. Sample the GP systematics model posterior using MCMC
7. Plots the light curves, systematics, and parameter posteriors
8. Save the posterior samples and the final light curves

The m2fit scripts carries out the steps and offers some customisability through command line options. However, the pipeline also comes with a Jupyter notebook template that carries out the steps with significantly higher level of interaction and customisability than offered by the script.

Requirements

• Python 3.6
• NumPy, SciPy, Pandas, xarray, scikit-learn, astropy, IPython, matplotlib, george, emcee, tqdm
• PyTransit 2.0, PyDE

Installation

git clone [email protected]:hpparvi/MuSCAT2_transit_pipeline.git
cd MuSCAT2_transit_pipeline
python setup.py install

Usage

Photometry pipeline

Organisation

m2organize raw_dir org_root

The organisation script m2organize takes the raw observation directory raw_dir and copies and organises the data in it in a directory created under an organised data root directory as org_root/raw_dir. In general, raw_dir is of format yymmdd, and the organised directory will then be org_root/yymmdd.

Astrometry

m2astrometry -n n_processes

The astrometry script needs to be run from the organised data directory and takes the number of parallel processes as its main (optional) argument. This should be no larger than the number of cores available.

Photometry

m2photometry XX XX

Transit Analysis

m2fit input_file

Collaborators

• Instituto de Astrofísica de Canarias
• University of Tokyo
• National Astronomical Observatory of Japan
• The Graduate University for Advanced Studies
• Astrobiology Center

Contributors

• Hannu Parviainen

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© 2018 Hannu Parviainen