orvara: An Efficient Code to Fit Orbits using Radial Velocity, Absolute, and/or Relative Astrometry
Timothy D. Brandt, Trent J. Dupuy, Yiting Li, G. Mirek Brandt, Yunlin, Zeng, Daniel Michalik, Daniella C. Bardalez Gagliuffi, and Virginia, Raposo-Pulido

TL;DR
orvara is an open-source Python package that efficiently combines radial velocity, absolute, and relative astrometry data to precisely fit Keplerian orbits and measure stellar and planetary masses, even with limited observational coverage.
Contribution
The paper introduces orvara, a new computational tool that accelerates orbit fitting by integrating multiple data types and analytical marginalization, improving mass measurements significantly.
Findings
Enhanced mass precision for HD 159062B by over an order of magnitude.
Demonstrated the ability to determine orbital parameters with limited data coverage.
Showcased the integration of Hipparcos and Gaia data for improved astrometric analysis.
Abstract
We present an open-source Python package, Orbits from Radial Velocity, Absolute, and/or Relative Astrometry (orvara), to fit Keplerian orbits to any combination of radial velocity, relative astrometry, and absolute astrometry data from the Hipparcos-Gaia Catalog of Accelerations. By combining these three data types, one can measure precise masses and sometimes orbital parameters even when the observations cover a small fraction of an orbit. orvara achieves its computational performance with an eccentric anomaly solver five to ten times faster than commonly used approaches, low-level memory management to avoid python overheads, and by analytically marginalizing out parallax, barycenter proper motion, and the instrument-specific radial velocity zero points. Through its integration with the Hipparcos and Gaia intermediate astrometry package htof, orvara can properly account for the epoch…
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.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
