English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Density-clustering of continuous gravitational wave candidates from large surveys

Steltner, B., Menne, T., Papa, M. A., & Eggenstein, H.-B. (in preparation). Density-clustering of continuous gravitational wave candidates from large surveys.

Item is

Files

show Files
hide Files
:
2207.14286.pdf (Preprint), 2MB
Name:
2207.14286.pdf
Description:
File downloaded from arXiv at 2022-08-02 06:48
OA-Status:
Green
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Steltner, Benjamin1, Author           
Menne, Thorben1, Author           
Papa, Maria Alessandra1, Author           
Eggenstein, Heinz-Bernd2, Author           
Affiliations:
1Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_2630691              
2Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, ou_24011              

Content

show
hide
Free keywords: General Relativity and Quantum Cosmology, gr-qc, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM
 Abstract: Searches for continuous gravitational waves target nearly monochromatic
gravitational wave emission from e.g. non-axysmmetric fast-spinning neutron
stars. Broad surveys often require to explicitly search for a very large number
of different waveforms, easily exceeding $\sim10^{17}$ templates. In such
cases, for practical reasons, only the top, say $\sim10^{10}$, results are
saved and followed-up through a hierarchy of stages. Most of these candidates
are not completely independent of neighbouring ones, but arise due to some
common cause: a fluctuation, a signal or a disturbance. By judiciously
clustering together candidates stemming from the same root cause, the
subsequent follow-ups become more effective. A number of clustering algorithms
have been employed in past searches based on iteratively finding symmetric and
compact over-densities around candidates with high detection statistic values.
The new clustering method presented in this paper is a significant improvement
over previous methods: it is agnostic about the shape of the over-densities, is
very efficient and it is effective: at a very high detection efficiency, it has
a noise rejection of $99.99\%$ , is capable of clustering two orders of
magnitude more candidates than attainable before and, at fixed sensitivity it
enables more than a factor of 30 faster follow-ups. We also demonstrate how to
optimally choose the clustering parameters.

Details

show
hide
Language(s):
 Dates: 2022-07-28
 Publication Status: Not specified
 Pages: 7 pages, 3 figures
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2207.14286
 Degree: -

Event

show

Legal Case

show

Project information

show

Source

show