Masses, Luminosities, and Orbital Coplanarities of the mu Orionis Quadruple Star System from PHASES Differential Astrometry
Matthew W. Muterspaugh (Berkeley), Benjamin F. Lane (Draper), Francis, C. Fekel (Tennessee State), Maciej Konacki (NCAC Polish Academy of Sciences),, Bernard F. Burke (MIT), S. R. Kulkarni (Caltech), M. M. Colavita (JPL), M., Shao (JPL), Sloane J. Wiktorowicz (Caltech)

TL;DR
This study uses high-precision differential astrometry to analyze the mu Orionis quadruple star system, determining orbital orientations, masses, and coplanarity, and providing insights into hierarchical system dynamics.
Contribution
First detailed astrometric analysis of mu Orionis revealing orbital orientations, component masses, and mutual inclinations using PHASES data.
Findings
Orbital plane angle between A-B and Aa-Ab is 136.7 +/- 8.3 degrees.
Component masses are determined with high precision, e.g., 5% for Aa.
The system's mutual inclination aligns with theoretical predictions.
Abstract
mu Orionis was identified by spectroscopic studies as a quadruple star system. Seventeen high precision differential astrometry measurements of mu Ori have been collected by the Palomar High-precision Astrometric Search for Exoplanet Systems (PHASES). These show both the motion of the long period binary orbit and short period perturbations superimposed on that caused by each of the components in the long period system being themselves binaries. The new measurements enable the orientations of the long period binary and short period subsystems to be determined. Recent theoretical work predicts the distribution of relative inclinations between inner and outer orbits of hierarchical systems to peak near 40 and 140 degrees. The degree of coplanarity of this complex system is determined, and the angle between the planes of the A-B and Aa-Ab orbits is found to be 136.7 +/- 8.3 degrees, near…
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.
