English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse
  1. View item / 
  2. Item Summary

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

Deep learning for clustering of continuous gravitational wave candidates

MPS-Authors
/persons/resource/persons243907

Beheshtipour,  Banafsheh
Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

/persons/resource/persons20662

Papa,  Maria Alessandra
Searching for Continuous Gravitational Waves, AEI-Hannover, MPI for Gravitational Physics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

2001.03116.pdf
(Preprint), 7MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Beheshtipour, B., & Papa, M. A. (2020). Deep learning for clustering of continuous gravitational wave candidates. Physical Review D, 101: 064009. doi:10.1103/PhysRevD.101.064009.


Cite as: https://hdl.handle.net/21.11116/0000-0005-78E7-0
Abstract
In searching for continuous gravitational waves over very many ($\approx
10^{17}$) templates , clustering is a powerful tool which increases the search
sensitivity by identifying and bundling together candidates that are due to the
same root cause. We implement a deep learning network that identifies clusters
of signal candidates in the output of continuous gravitational wave searches
and assess its performance.