Analytic Post-Newtonian Astrometric and Spectroscopic Models of Orbits around Black Holes
S\'oley \'O. Hyman, Dimitrios Psaltis, Feryal \"Ozel

TL;DR
This paper introduces a fast, first-order post-Newtonian model for analyzing S-star orbits around the Galactic Center's black hole, enabling efficient tests of general relativity with upcoming telescopes.
Contribution
It presents a novel, computationally efficient post-Newtonian model for S-star orbit analysis, improving upon slow numerical geodesic solutions.
Findings
Future telescopes may detect the Shapiro effect in S-star orbits.
The model enables faster analysis of orbital data.
Potential to test general relativity near supermassive black holes.
Abstract
Observations of the S-stars, the cluster of young stars in the inner 0.1 pc of the Galactic Center, have been crucial in providing conclusive evidence for a supermassive black hole at the center of our galaxy. Since some of the stars have orbits less than that of a typical human lifetime, it is possible to observe multiple orbits and test the weak-field regime of general relativity. Current calculations of S-star orbits require slow and expensive computations in order to numerically solve geodesic equations for many small time steps. In this paper, we present a computationally efficient, first-order post-Newtonian model for the astrometric and spectroscopic data gathered for the S-stars. We find that future, 30-m class telescopes -- and potentially even current large telescopes with very high spectroscopic resolution -- may be able to detect the Shapiro effect for an S-star in the next…
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.
Taxonomy
TopicsAstrophysical Phenomena and Observations · Pulsars and Gravitational Waves Research · Adaptive optics and wavefront sensing
