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VERSION:2.0
PRODID:-//linuxsoftware.nz//NONSGML Joyous v1.4//EN
BEGIN:VEVENT
SUMMARY:Sequential simulation-based inference for extreme mass ratio inspi
 rals
DTSTART:20251002T143000Z
DTEND:20251002T160000Z
DTSTAMP:20260516T103504Z
UID:f5ce48b3-0b5e-4f9c-9274-6057c282c4af
SEQUENCE:2
CREATED:20250929T090158Z
DESCRIPTION:Extreme mass ratio inspirals are a key target for next generat
 ion space-based gravitational wave detectors because they have a rich phen
 omenology that could offer new astrophysics and fundamental physics insigh
 ts. However\, their dynamics are complicated to model\, and they will be b
 uried amongst a large population of other sources in the milliHertz freque
 ncy band\, with a background of non-stationary and non-Gaussian noise. Sea
 rching for these systems and measuring their parameters therefore presents
  a difficult challenge.Simulation-based inference methods could offer solu
 tions to some of these challenges. I will show parameter estimation result
 s for extreme mass ratio inspiral systems achieved using sequential simula
 tion-based inference\, specifically truncated marginal neural ratio estima
 tion. I will highlight the benefits of this approach with respect to tradi
 tional likelihood-based methods\, and discuss the broader context in which
  such a pipeline will need to be embedded as well as how and when environm
 ental effects should be considered.
LAST-MODIFIED:20250929T090213Z
LOCATION:DF Seminar Room (2-8.3)\, 2nd floor of Physics Building
URL:http://df.vps.tecnico.ulisboa.pt/pt/eventos/sequential-simulation-base
 d-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 w
 ave detectors because they have a rich phenomenology that could offer new 
 astrophysics and fundamental physics insights. However\, their dynamics ar
 e complicated to model\, and they will be buried amongst a large populatio
 n of other sources in the milliHertz frequency band\, with a background of
  non-stationary and non-Gaussian noise. Searching for these systems and me
 asuring their parameters therefore presents a difficult challenge.<br/></p
 ><p data-block-key="stss">Simulation-based inference methods could offer s
 olutions to some of these challenges. I will show parameter estimation res
 ults for extreme mass ratio inspiral systems achieved using sequential sim
 ulation-based inference\, specifically truncated marginal neural ratio est
 imation. I will highlight the benefits of this approach with respect to tr
 aditional likelihood-based methods\, and discuss the broader context in wh
 ich such a pipeline will need to be embedded as well as how and when envir
 onmental effects should be considered.</p>
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