Parameter estimation for binary neutron-star coalescences with realistic noise during the Advanced LIGO era
Christopher P. L. Berry, Ilya Mandel, Hannah Middleton, Leo P. Singer,, Alex L. Urban, Alberto Vecchio, Salvatore Vitale, Kipp Cannon, Ben Farr, Will, M. Farr, Philip B. Graff, Chad Hanna, Carl-Johan Haster, Satya Mohapatra,, Chris Pankow, Larry R. Price, Trevor Sidery

TL;DR
This study evaluates the accuracy of parameter estimation for binary neutron-star mergers detected by early Advanced LIGO, focusing on sky localization, mass, and distance measurements amid realistic noise conditions.
Contribution
It provides an assessment of the parameter-estimation pipeline's performance during early aLIGO runs, including effects of non-stationary noise and limitations in sky localization.
Findings
Sky localization median ~600 deg², with 3% within 100 deg².
Chirp mass is accurately measured with less than 0.001 solar mass error.
Distance estimates are poorly constrained, with median credible interval ~85% of true distance.
Abstract
Advanced ground-based gravitational-wave (GW) detectors begin operation imminently. Their intended goal is not only to make the first direct detection of GWs, but also to make inferences about the source systems. Binary neutron-star mergers are among the most promising sources. We investigate the performance of the parameter-estimation \edit{(PE)} pipeline that will be used during the first observing run of the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) in 2015: we concentrate on the ability to reconstruct the source location on the sky, but also consider the ability to measure masses and the distance. Accurate, rapid sky-localization is necessary to alert electromagnetic (EM) observatories so that they can perform follow-up searches for counterpart transient events. We consider PE accuracy in the presence of \edit{non-stationary}, non-Gaussian noise. We find…
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.
