About
Pulsar Timing Array data analysis faces severe computational challenges as parameter spaces scale with the number of pulsars. I present two Normalizing Flows (NFs) based strategies to accelerate and improve Bayesian inference for stochastic gravitational wave background (SGWB). First, integrating NFs into the importance nested sampling framework i-nessai yields speedups of one to three orders of magnitude over standard methods, with robust posteriors and reliable evidence estimates. Second, a dual NFs architecture implementing parameter decorrelation via orthogonal projection disentangles pulsar noise from hyperprior parameters in hierarchical Bayesian modeling, enhancing noise constraining power even in the presence of a SGWB signal.
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The talk
Credits and licence
- Institution
- CERN
- Speaker
- Eleonora Villa
- Year
- 2026
- Language
- English
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
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