Bayesian Characterization of Main Sequence Binaries in the Old Open Cluster NGC 188
Roger E. Cohen (1), Aaron M. Geller (2, 3), Ted von Hippel (4) ((1), STScI, (2) CIERA, Northwestern U., (3) Adler Planetarium, (4) ERAU)

TL;DR
This study uses Bayesian analysis on photometric data to detect and characterize main sequence binaries in the old open cluster NGC 188, providing insights into binary fractions and distributions with improved accuracy.
Contribution
It introduces a Bayesian photometric method that accounts for errors and biases, accurately estimating binary fractions and mass ratio distributions in NGC 188.
Findings
Recovered 65% of spectroscopic binaries with high accuracy.
Derived a binary fraction of 42% for main sequence stars with q > 0.5.
Found binaries are more centrally concentrated than single stars.
Abstract
The binary fractions of open and globular clusters yield powerful constraints on their dynamical state and evolutionary history. We apply publicly available Bayesian analysis code to a photometric catalog of the old open cluster NGC 188 to detect and characterize photometric binaries along the cluster main sequence. This technique has the advantage that it self-consistently handles photometric errors, missing data in various bandpasses, and star-by-star prior constraints on cluster membership. Simulations are used to verify uncertainties and quantify selection biases in our analysis, illustrating that among binaries with mass ratios >0.5, we recover the binary fraction to better than 7% in the mean, with no significant dependence on binary fraction and a mild dependence on assumed mass ratio distribution. Using our photometric catalog, we recover the majority…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
