Paper detail

Towards mitigating the effect of sine-Gaussian noise transients on searches for gravitational waves from compact binary coalescences

Gravitational wave (GW) signals were recently detected directly by LIGO from the coalescences of two black hole pairs. These detections have strengthened our belief that compact binary coalescences (CBCs) are the most promising GW detection prospects accessible to ground-based interferometric detectors. For detecting CBC signals it is of vital importance to characterize and identify non-Gaussian and non-stationary noise in these detectors. In this work we model two important classes of transient artifacts that contribute to this noise and adversely affect the detector sensitivity to CBC signals. One of them is the sine-Gaussian glitch, characterized by a central frequency $f_0$ and a quality factor $Q$ and the other is the chirping sine-Gaussian glitch, which is characterized by $f_0$, $Q$ as well as a chirp parameter. We study the response a bank of compact binary inspiral templates has to these two families of glitches when they are used to match-filter data containing any of these glitches. Two important characteristics of this response are the distributions of the signal-to-noise ratio and the timelag of individual templates. We show how these distributions differ from those when the detector data has a real CBC signal instead of a glitch. We argue that these distinctions can be utilized to develop useful signal-artifact vetos that add negligibly to the computational cost of a CBC search. Specifically, we show how $f_0$ of a glitch can be used to set adaptive time-windows around it so that any template trigger occurring in that window can be quarantined for further vetting of its supposed astrophysical nature. Second, we recommend focusing efforts on reducing the incidence of glitches with low $f_0$ values because they create CBC triggers with the longest timelags. This work allows us to associate such triggers with the glitches which otherwise would have escaped attention.

preprint2016arXivOpen access

Signal facts

What is known right now

Open access4 authors1 topic

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 map preview

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.