Improving Orbit Estimates for Incomplete Orbits with a New Approach to Priors -- with Applications from Black Holes to Planets
K. Kosmo O'Neil (1), G. D. Martinez (1), A. Hees (1, 2), A.M. Ghez, (1), T. Do (1), G. Witzel (1), Q. Konopacky (3), E.E. Becklin (1), D.S. Chu, (1), J. Lu (4), K. Matthews (5), S. Sakai (1) ((1) University of California, Los Angeles, (2) Observatoire de Paris

TL;DR
This paper introduces an observable-based prior for Bayesian orbit fitting that reduces bias and underestimation of confidence intervals in low-phase-coverage orbits, improving parameter estimates for objects like exoplanets and stars.
Contribution
The paper presents a novel observable-based prior paradigm that enhances orbital parameter estimation for low-phase-coverage data, outperforming traditional uniform priors.
Findings
Observable-based priors reduce biases by a factor of two.
Improved confidence interval accuracy for less than 40% phase coverage.
Supports lower eccentricity and coplanarity hypotheses for HR 8799 planets.
Abstract
We propose a new approach to Bayesian prior probability distributions (priors) that can improve orbital solutions for low-phase-coverage orbits, where data cover less than approximately 40% of an orbit. In instances of low phase coverage such as with stellar orbits in the Galactic center or with directly-imaged exoplanets, data have low constraining power and thus priors can bias parameter estimates and produce under-estimated confidence intervals. Uniform priors, which are commonly assumed in orbit fitting, are notorious for this. We propose a new observable-based prior paradigm that is based on uniformity in observables. We compare performance of this observable-based prior and of commonly assumed uniform priors using Galactic center and directly-imaged exoplanet (HR 8799) data. The observable-based prior can reduce biases in model parameters by a factor of two and helps avoid…
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