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SUMMARY:Arianna Scarpa: "Connecting Astrophysics and GW observations with 
 machine learning"
DTSTART:20260107T113000Z
DTEND:20260107T130000Z
DTSTAMP:20260506T213100Z
UID:indico-event-49951@agenda.infn.it
CONTACT:scarpa.1871231@studenti.uniroma1.it
DESCRIPTION:The latest Gravitational Wave Transient Catalogs (GWTCs) provi
 de us with an unprecedented opportunity to understand how the universe is 
 expanding and compact objects are formed. This opportunity comes with the 
 burden of having to model the population distribution of compact binaries\
 , in particular binary black holes and connecting these "phenomenological"
  models with astrophysical processes. To overcome the modelling difficulty
 \, I introduce a non-parametric framework based on the machine learning te
 chnique of Normalizing Flows to model the joint mass-redshift distribution
  of binary black holes. The method replaces standard phenomenological pres
 criptions with Normalizing Flows capable of learning complex and multimoda
 l distributions\, allowing the model to capture mass-redshift correlations
  directly from astrophysical synthesis catalogs or from mock datasets. Nor
 malizing Flows trained on redshift evolving synthetic populations accurate
 ly reconstruct the underlying features and remove the systematic bias on t
 he Hubble constant\, observed when using traditional parametric models. Th
 e framework can be extended to multiple formation channels through multipl
 icative factors\, allowing joint inference of cosmology and absolute chann
 el abundances. This methodology allows to quantify the agreement between a
 strophysical synthesis catalogs and gravitational wave observations: by fi
 tting both simulated datasets and real events from the GWTC catalogs\, the
  model provides a direct way to test the agreement between population synt
 hesis predictions and current GW data.\n\nhttps://agenda.infn.it/event/499
 51/
LOCATION:Sala Lauree
URL:https://agenda.infn.it/event/49951/
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