Paper detail

Forecasts for detecting the gravitational-wave memory effect with Advanced LIGO and Virgo

The detection of gravitational waves (GWs) from binary black holes (BBHs) has allowed the theory of general relativity to be tested in a previously unstudied regime: that of strong curvature and high GW luminosities. One distinctive and measurable effect associated with this aspect of the theory is the nonlinear GW memory effect. The GW memory effect is characterized by its effect on freely falling observers: the proper distance between their locations differs before and after a burst of GWs passes by their locations. Gravitational-wave interferometers, like the LIGO and Virgo detectors, can measure features of this effect from a single BBH merger, but previous work has shown that it will require an event that is significantly more massive and closer than any previously detected GW event. Finding evidence for the GW memory effect within the entire population of BBH mergers detected by LIGO and Virgo is more likely to occur sooner. A prior study has shown that the GW memory effect could be detected in a population of BBHs consisting of binaries like the first GW150914 event after roughly one-hundred events. In this paper, we compute forecasts of the time it will take the advanced LIGO and Virgo detectors (when the detectors are operating at their design sensitivities) to find evidence for the GW memory effect in a population of BBHs that is consistent with the measured population of events in the first two observing runs of the LIGO detectors. We find that after five years of data collected by the advanced LIGO and Virgo detectors the signal-to-noise ratio for the nonlinear GW memory effect in the population will be about three (near a previously used threshold for detection). We point out that the different approximation methods used to compute the GW memory effect can lead to notably different signal-to-noise ratios.

preprint2020arXivOpen access
0citations
0reviews
0saves
Nocode
Nodataset
0institutions

Next steps

Decide what to do with this paper

Use like or dislike for the fast social read. The more specific scholarly feedback stays available below when needed.

Log in to curate

Reading frame

Keep the important context close to the paper

Keep the important signals around this paper in one place: votes, save state, collection context, reviews and the metadata you need before deciding what to do next.

Institutions

Add specific reaction

Move through the context

Research map

Open full explorer

Move through nearby people, institutions, topics and adjacent work without leaving the paper page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Structured reviews

0 review(s)

ContributeLeave structured feedbackUse the review template when you have a concrete strength, concern or method question.Open review form

No structured reviews yet. High-signal critique starts here.

Work discussion

0 comment(s)

DiscussAdd a high-signal commentKeep quick notes, caveats and replication pointers separate from formal reviews.Open comment form

No discussion yet. The first strong comment sets the tone.