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SUMMARY:GWDALI: Derivative approximation for gravitational wave likelihood
 s
DTSTART:20241113T110000Z
DTEND:20241113T130000Z
DTSTAMP:20260728T195400Z
UID:433e9e2e-34c6-4cd6-9888-3c0d9e30ce86
SEQUENCE:1
CREATED:20241112T154658Z
DESCRIPTION: In the next decade\, third-generation gravitational wave (GW)
   observatories\, such as the Einstein Telescope and Cosmic Explorer\, wil
 l  begin operation\, enabling the detection of compact object coalescences
   at unprecedented distances (up to z &lt\; 100). Analyzing these sources 
  cosmologically necessitates GW parameter inference\, which typically  inv
 olves around 15 parameters for each detection—a process that is  computa
 tionally intensive. To address this challenge\, we discuss the  applicatio
 n of Derivative Approximation for Likelihoods (DALI) in  gravitational wav
 e and cosmological data analysis. DALI is a more  time-efficient approach 
 that incorporates higher-order terms in the  Taylor expansion of likelihoo
 ds\, which is particularly advantageous when  the expected posterior distr
 ibutions deviate from Gaussianity\,  rendering the Fisher Matrix approxima
 tion unreliable. Such deviations  often occur in the context of GW signals
  from compact binary  coalescences\, especially in cases of parameter dege
 neracies\, such as the  distance-inclination degeneracy. In this presentat
 ion\, we explore the  behavior of gravitational wave DALI-posteriors under
  various  parameterizations of GW signals and demonstrate how their accura
 cy can  be enhanced using automatic differentiation (autodiff). 
LAST-MODIFIED:20241112T154658Z
LOCATION:DF Seminar Room (2-8.3)\, 2nd floor of Physics Building
URL:http://df.vps.tecnico.ulisboa.pt/en/events/gwdali-derivative-approxima
 tion-for-gravitational-wave-likelihoods/
X-ALT-DESC;FMTTYPE=text/html:<p data-block-key="z4h4o"> In the next decade
 \, third-generation gravitational wave (GW)  observatories\, such as the E
 instein Telescope and Cosmic Explorer\, will  begin operation\, enabling t
 he detection of compact object coalescences  at unprecedented distances (u
 p to z &lt\; 100). Analyzing these sources  cosmologically necessitates GW
  parameter inference\, which typically  involves around 15 parameters for 
 each detection—a process that is  computationally intensive.<br/><br/> T
 o address this challenge\, we discuss the  application of Derivative Appro
 ximation for Likelihoods (DALI) in  gravitational wave and cosmological da
 ta analysis. DALI is a more  time-efficient approach that incorporates hig
 her-order terms in the  Taylor expansion of likelihoods\, which is particu
 larly advantageous when  the expected posterior distributions deviate from
  Gaussianity\,  rendering the Fisher Matrix approximation unreliable.<br/>
 <br/> Such deviations  often occur in the context of GW signals from compa
 ct binary  coalescences\, especially in cases of parameter degeneracies\, 
 such as the  distance-inclination degeneracy. In this presentation\, we ex
 plore the  behavior of gravitational wave DALI-posteriors under various  p
 arameterizations of GW signals and demonstrate how their accuracy can  be 
 enhanced using automatic differentiation (autodiff). </p>
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