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VERSION:2.0
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BEGIN:VEVENT
SUMMARY:Sequential simulation-based inference for extreme mass ratio inspi
 rals
DTSTART:20251002T143000Z
DTEND:20251002T160000Z
DTSTAMP:20260614T165752Z
UID:f5ce48b3-0b5e-4f9c-9274-6057c282c4af
SEQUENCE:1
CREATED:20250929T090145Z
DESCRIPTION: Extreme mass ratio inspirals are a key target for next genera
 tion space-based gravitational wave detectors because they have a rich phe
 nomenology that could offer new astrophysics and fundamental physics insig
 hts. However\, their dynamics are complicated to model\, and they will be 
 buried amongst a large population of other sources in the milliHertz frequ
 ency band\, with a background of non-stationary and non-Gaussian noise. Se
 arching for these systems and measuring their parameters therefore present
 s a difficult challenge.Simulation-based inference methods could offer sol
 utions to some of these challenges. I will show parameter estimation resul
 ts for extreme mass ratio inspiral systems achieved using sequential simul
 ation-based inference\, specifically truncated marginal neural ratio estim
 ation. I will highlight the benefits of this approach with respect to trad
 itional likelihood-based methods\, and discuss the broader context in whic
 h such a pipeline will need to be embedded as well as how and when environ
 mental effects should be considered. 
LAST-MODIFIED:20250929T090145Z
LOCATION:DF Seminar Room (2-8.3)\, 2nd floor of Physics Building
URL:http://df.vps.tecnico.ulisboa.pt/en/events/sequential-simulation-based
 -inference-for-extreme-mass-ratio-inspirals/
X-ALT-DESC;FMTTYPE=text/html:<p data-block-key="buxp8"> Extreme mass ratio
  inspirals are a key target for next generation space-based gravitational 
 wave detectors because they have a rich phenomenology that could offer new
  astrophysics and fundamental physics insights. However\, their dynamics a
 re complicated to model\, and they will be buried amongst a large populati
 on of other sources in the milliHertz frequency band\, with a background o
 f non-stationary and non-Gaussian noise. Searching for these systems and m
 easuring their parameters therefore presents a difficult challenge.<br/></
 p><p data-block-key="stss">Simulation-based inference methods could offer 
 solutions to some of these challenges. I will show parameter estimation re
 sults for extreme mass ratio inspiral systems achieved using sequential si
 mulation-based inference\, specifically truncated marginal neural ratio es
 timation. I will highlight the benefits of this approach with respect to t
 raditional likelihood-based methods\, and discuss the broader context in w
 hich such a pipeline will need to be embedded as well as how and when envi
 ronmental effects should be considered. </p>
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