This repository supplements the paper available here [TODO: link to paper once I have it]. To run this code, you will need my personal research library, kglib. You can use the github version, or just
pip install kglibThe analysis was done in a series of jupyter notebooks using legacy python (python 2.7). The relevant notebooks are:
- CompileObsStats.ipynb: Compile observation data to make Tables 1, 2, and 4. Also makes a sky map of all the targets. This notebook will not run all the way through, because it contains some hard-coded paths to directories and files on my computer.
- CompileCompanionData.ipynb: Compiles the measured temperature data for the companions into a csv file.
- EstimateMasses.ipynb: Estimates the primary star mass and system age, as well as the companion mass. Saves everything into
data/MassSamples.h5 - ImagingAnalysis.ipynb: Finds all stars in my reduced NIRI data, and estimates the separation, position angle, and flux ratio between stars.
- MakeCCF_Plots.ipynb: Makes cross-correlation function plots similar to Figure 1, but for every star.
- MakeImagingPlots.ipynb: Makes Figure 2
- Malmquist_Bias.ipynb: Does the simulation described in Section n 6.2 to estimate the malmquist bias P(obs|q). This notebook is where the polynomial coefficients present in other notebooks are derived.
- RealData.ipynb: does all of the following
- Compile the mass ratio samples for each star I detect
- Estimate the completeness as a function of mass ratio
- Fit the data to:
- histogram
- gaussian
- power law
- beta distribution
- Compare my results to the VAST survey close companions. Find that they are unlikely to be drawn from the same distribution.