tSZ cross-correlations with large-scale structure tracers prefer low S8 and strong baryonic feedback, yielding S8 = 0.72 and low group baryon fraction in FLAMINGO simulations.
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Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
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FLAMINGO: The thermal history of the Universe from tSZ effect cross-correlations and its dependencies on cosmology and baryon physics
tSZ cross-correlations with large-scale structure tracers prefer low S8 and strong baryonic feedback, yielding S8 = 0.72 and low group baryon fraction in FLAMINGO simulations.
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Machine-learning applications for weak-lensing cosmology
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.