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SUMMARY:Preparing for the future gravitational wave burst searches with ma
 chine learning techniques
DTSTART;VALUE=DATE-TIME:20210902T154500Z
DTEND;VALUE=DATE-TIME:20210902T160000Z
DTSTAMP;VALUE=DATE-TIME:20260716T020230Z
UID:indico-contribution-15511@indico.ific.uv.es
DESCRIPTION:Speakers: Sophie Bini (Univ. Trento)\nGeneric searches for gra
 vitational wave bursts are a powerful discovery tool and in the near futur
 e they are expected to unveil new phenomena. The coherentWaveBurst (cWB) p
 ipeline is a state-of-the-art burst search pipeline\, and it has been used
  to analyze the data from the latest observing runs of the LIGO/Virgo dete
 ctors.\nIn preparation for the next observing run\, which will include KAG
 RA detector\, we are investigating several improvements involving also the
  application of cutting edge machine learning techniques. A decision tree 
 algorithm will address the post production analysis of the candidate event
 s\, upgrading the selection criteria and ranking procedures applied so far
 . Moreover\, an autoencoder neural network will pinpoint morphologies asso
 ciated to well known noise transients\, mitigating their impact.\nWe show 
 that both these procedures are robust and do not limit the general charact
 er of the search. We present preliminary results on public LIGO-Virgo data
  for widely different burst morphologies\, ranging from extreme ad-hoc sig
 nals to more astrophysically inspired gravitational-wave transients.\n\nht
 tps://indico.ific.uv.es/event/6178/contributions/15511/
LOCATION:
URL:https://indico.ific.uv.es/event/6178/contributions/15511/
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