An Improved Test of the Binary Black Hole Hypothesis for Quasars with Double-peaked Broad Balmer Lines
Anh Doan (1), Michael Eracleous (1,2), Jessie C. Runnoe (3,4), Jia Liu, (5), Gavin Mathes (6), and Helene M. L. G. Flohic (7) (1) Penn State, (2), IGC, Penn State, (3) U. Michigan, (4) Vanderbilt U., (5) Princeton U., (6), New Mexico State U., (7) U. of the Pacific

TL;DR
This study rigorously tests the supermassive black hole binary hypothesis in quasars with double-peaked emission lines using long-term data, advanced statistical modeling, and new observations, ultimately finding it unlikely for most cases.
Contribution
It introduces an improved analysis method incorporating elliptical orbits, a statistical jitter model, and MCMC exploration, providing more robust constraints on the SBHB hypothesis.
Findings
Lower mass limits for SBHBs range from 10^8 to 10^11 solar masses.
Seven objects are unlikely to host SBHBs based on fit quality.
Physical and observational evidence generally disfavor the SBHB scenario.
Abstract
Velocity offsets in the broad Balmer lines of quasars and their temporal variations serve as indirect evidence for bound supermassive black hole binaries (SBHBs) at sub-parsec separations. In this work, we test the SBHB hypothesis for 14 quasars with double-peaked broad emission lines using their long-term (14--41 years) radial velocity curves. We improve on previous work by (a) using elliptical instead of circular orbits for the SBHBs, (b) adopting a statistical model for radial velocity jitter, (c) employing a Markov Chain Monte Carlo method to explore the orbital parameter space efficiently and build posterior distributions of physical parameters and (d) incorporating new observations. We determine empirically that jitter comprises approximately Gaussian distributed fluctuations about the smooth radial velocity curves that are larger than the measurement errors by factors of order a…
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.
