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SUMMARY:Breakout room #4: Detection and parameter estimation for GW-burst 
 signal with machine learning
DTSTART;VALUE=DATE-TIME:20201117T052000Z
DTEND;VALUE=DATE-TIME:20201117T065000Z
DTSTAMP;VALUE=DATE-TIME:20260425T132617Z
UID:indico-contribution-22@cern.ch
DESCRIPTION:Speakers: KIMURA\, Yuto (Hiroshima University)\nIn recent 
 years\, machine learning(ML) has begun to be used to find out small signal
  of gravitational wave(GW) from noisy data and to estimate physical 
 parameters of it. The approach is an alternative to using templates\, by 
 which the parameters are estimated by matching with theoretical models. 
 George and Huerta (2018) demonstrated the ML method for GW from binary 
 black holes. It is important to explore the possibility of detection and 
 estimation for different types of GW signals. We consider the possibility 
 of detection and parameter estimation for GW burst by ML. Since the wave 
 forms\, e. g.\, driven by magnetar giant flares\, are uncertain at 
 present\, we model them and explore the ability of the ML approach. We use
  the same algorithms based by convolution neural network used in binary 
 black hole merger by George and Huerta (2018). In this poster\, we discuss
  accuracy of detection and how much error we can estimate parameters.\n\nh
 ttps://agenda.hepl.phys.nagoya-u.ac.jp/indico/contributionDisplay.py?contr
 ibId=22&sessionId=5&confId=1282
LOCATION:Nagoya University KMI Online
URL:https://agenda.hepl.phys.nagoya-u.ac.jp/indico/contributionDisplay.py?c
 ontribId=22&sessionId=5&confId=1282
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