Note: These data products are now superseded by the DR16 catalogs. See the DR16 catalog page
All code related to this project can be found in this GitHub repository.
If you're just looking for catalogs of binaries, these are probably the files you want.
This file contains maximum a posteriori values of the orbital parameters for 320 systems that were flagged as having unimodal posterior samplings. The uncertainties on the orbital parameters (_err columns) are estimated using percentiles from the 1D (marginal) distributions: Note that the parameters often have correlated posterior distributions, so if this matters to your analysis, you should directly use the posterior samples provided below (especially the MCMC samplings file). To select out a clean sample of systems with orbital solutions that have been visually inspected, and with converged MCMC samplings in Python, use:
from astropy.table import QTable tbl = QTable.read('highK-unimodal.fits', astropy_native=True, character_as_bytes=False) clean = tbl[(tbl['clean_flag'] == 0) & tbl['converged']]
This file also contains all columns from the APOGEE DR14 allStar file, and columns from Melissa Ness' derived masses and ages from this paper (where available).
This table contains two rows per system for all systems with bimodal posterior samplings over orbital period. Within each mode, the maximum a posteriori sample is reported.
This file contains the 1st percentile of the velocity semi-amplitude, K, samplings for all APOGEE stars in our parent sample. You can use this value to select probable binaries: systems with a large value of this parameter have large radial velocity variations that can be explained by two-body orbital moton.
- All posterior samplings returned by *The Joker* (96,231 stars)
- Database containing metadata for APOGEE DR14 stars (96,231 stars)
- MCMC samplings for stars with unimodal samplings (320 stars)
- All posterior samplings for the (simulated) control sample (16,384 stars)