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.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3representative citing papers
Machine learning on cosmological simulations achieves 91-94% accuracy classifying over-massive versus under-massive SMBH growth regimes from LSST photometry, with 83-89% cross-simulation transfer accuracy driven primarily by host galaxy colors.
Super-Eddington accretion boosts predicted LISA detections of high-redshift black hole binaries to ~64 per year while dropping ET detections to ~4 per year, compared to ~32 and ~64 under Eddington-limited growth.
citing papers explorer
-
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.
-
Classifying Supermassive Black Hole Growth Regimes to Observables Across Cosmological Simulations with Forecasts for LSST
Machine learning on cosmological simulations achieves 91-94% accuracy classifying over-massive versus under-massive SMBH growth regimes from LSST photometry, with 83-89% cross-simulation transfer accuracy driven primarily by host galaxy colors.
-
Gravitational Waves from the Cosmic Dawn: Tracing Cosmic Black Hole Binaries with ET, LGWA and LISA
Super-Eddington accretion boosts predicted LISA detections of high-redshift black hole binaries to ~64 per year while dropping ET detections to ~4 per year, compared to ~32 and ~64 under Eddington-limited growth.