de.mpg.escidoc.pubman.appbase.FacesBean
English
 
Help Guide Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  An adaptive clustering procedure for continuous gravitational wave searches

Singh, A., Papa, M. A., Eggenstein, H.-B., & Walsh, S. (2017). An adaptive clustering procedure for continuous gravitational wave searches. Physical Review D, 96: 082003. doi:10.1103/PhysRevD.96.082003.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002D-DB9C-5 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-E39F-C
Genre: Journal Article

Files

show Files
hide Files
:
1707.02676.pdf (Preprint), 6MB
Description:
File downloaded from arXiv at 2017-09-05 12:06
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
:
PRD.96.082003.pdf (Publisher version), 6MB
 
File Permalink:
-
Description:
-
Visibility:
Restricted
MIME-Type / Checksum:
application/pdf
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Singh, Avneet1, 2, Author              
Papa, Maria Alessandra1, 2, Author              
Eggenstein, Heinz-Bernd2, 3, Author              
Walsh, Sinéad1, Author              
Affiliations:
1Astrophysical and Cosmological Relativity, AEI-Golm, MPI for Gravitational Physics, Max Planck Society, escidoc:1933290              
2Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, Hannover, DE, escidoc:2630691              
3Observational Relativity and Cosmology, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society, escidoc:24011              

Content

show
hide
Free keywords: General Relativity and Quantum Cosmology, gr-qc, Astrophysics, Instrumentation and Methods for Astrophysics, astro-ph.IM,Mathematics, General Topology, math.GN
 Abstract: In hierarchical searches for continuous gravitational waves, clustering of candidates is an important postprocessing step because it reduces the number of noise candidates that are followed-up at successive stages [1][7][12]. Previous clustering procedures bundled together nearby candidates ascribing them to the same root cause (be it a signal or a disturbance), based on a predefined cluster volume. In this paper, we present a procedure that adapts the cluster volume to the data itself and checks for consistency of such volume with what is expected from a signal. This significantly improves the noise rejection capabilities at fixed detection threshold, and at fixed computing resources for the follow-up stages, this results in an overall more sensitive search. This new procedure was employed in the first Einstein@Home search on data from the first science run of the advanced LIGO detectors (O1) [11].

Details

show
hide
Language(s):
 Dates: 2017-07-09201720172017
 Publication Status: Published in print
 Pages: 11 pages, 9 figures
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: arXiv: 1707.02676
URI: http://arxiv.org/abs/1707.02676
DOI: 10.1103/PhysRevD.96.082003
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Physical Review D
  Other : Phys. Rev. D.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Lancaster, Pa. : American Physical Society
Pages: - Volume / Issue: 96 Sequence Number: 082003 Start / End Page: - Identifier: ISSN: 0556-2821
CoNE: http://pubman.mpdl.mpg.de/cone/journals/resource/111088197762258