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SUMMARY:Shedding light on Dark Energy with Weak Lensing and Hybrid Statist
 ics - T. Lucas Makinen (DAMTP)
DTSTART:20260224T140000Z
DTEND:20260224T150000Z
UID:TALK245089@talks.cam.ac.uk
CONTACT:Sven Krippendorf
DESCRIPTION:How much can we learn about Dark Energy and its equation of st
 ate from weak gravitational lensing surveys ? Two-point functions offer so
 me insight but miss non-Gaussian information. Simulation-based inference (
 SBI) offers a way to combine and learn higher-order statistics via neural 
 compression\, but does not always a) leverage or b) exceed human domain kn
 owledge in physical inference problems in terms of bits extracted from dat
 a\, especially when simulations are large and limited in number.\nI will p
 resent an information-theoretic approach to illustrate SBI \, which can be
  naturally extended to derive hybrid statistics\, an optimal framework for
  combining domain knowledge and learned neural summaries. These statistics
  improve information extraction from the field-level compared to neural su
 mmaries alone or their concatenation to existing summaries and makes infer
 ence robust in settings with low training data.\nI will show an applicatio
 n of hybrid statistics for constraining wCDM from DES Y3 data. By changing
  the optimisation objective alone\, the method is forecast to provide the 
 most competitive Dark Energy and weak lensing parameter constraints to dat
 e. Furthermore\, the modular nature of hybrid statistics may shed light on
  where non-Gaussian signatures of Dark Energy information might lie in wea
 k lensing maps\, to be exploited in upcoming Stage IV analyses.
LOCATION:DAMTP\, room MR4
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