Abstract
Advanced LIGO and Advanced Virgo could observe the first lensed gravitational wave sources in the coming years, while the future Einstein Telescope could observe hundreds of lensed events. It is, therefore, crucial to develop methodologies to distinguish between lensed from unlensed gravitational-wave observations. A lensed signal not identified as such will lead to biases during the interpretation of the source. In particular, sources will appear to have intrinsically higher masses. No robust method currently exists to distinguish between the magnification bias caused by lensing and intrinsically high-mass sources. In this work, we show how to recognize lensed and unlensed binary neutron star systems through the measurement of their tidal effects for highly magnified sources as a proof-of-principle. The proposed method could be used to identify lensed binary neutron stars, which are the chief candidate for lensing cosmography studies. We apply our method on GW190425, finding no evidence in favor of lensing, mainly due to the poor measurement of the event's tidal effects. However, we expect that future detections with better tidal measurements can yield better constraints.
Original language | English |
---|---|
Pages (from-to) | 3740-3750 |
Number of pages | 11 |
Journal | Monthly Notices of the Royal Astronomical Society |
Volume | 495 |
Issue number | 4 |
Early online date | 23 May 2020 |
DOIs | |
Publication status | Published - 1 Jul 2020 |
Keywords
- astro-ph.HE
- gr-qc
- gravitational lensing: strong
- gravitational waves
- neutron star mergers
Fingerprint
Dive into the research topics of 'Lensed or not lensed: determining lensing magnifications for binary neutron star mergers from a single detection'. Together they form a unique fingerprint.Datasets
-
Data availability statement for 'Lensed or not lensed: determining lensing magnifications for binary neutron star mergers from a single detection'.
Pang, P. T. H. (Creator), Hannuksela, O. A. (Creator), Dietrich, T. (Creator), Pagano, G. (Creator) & Harry, I. (Creator), Oxford University Press, 1 Jul 2021
Dataset