Realistic black hole dynamics in Astrid reduce baryonic suppression of the matter power spectrum at low redshifts compared to repositioning schemes used in other simulations.
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3 Pith papers cite this work. Polarity classification is still indexing.
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astro-ph.GA 3years
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UNVERDICTED 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.
A large collaboration compiles and compares merger rate predictions for massive black holes across multiple galaxy formation models to forecast LISA detections and quantify uncertainties.
citing papers explorer
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Matter Clustering in Astrid: Reduced Baryonic Suppression from Realistic Black Hole Dynamics
Realistic black hole dynamics in Astrid reduce baryonic suppression of the matter power spectrum at low redshifts compared to repositioning schemes used in other simulations.
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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.
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The LISA Astrophysics MBHcatalogues Project: A comparison of predictions of simulated massive black hole binaries
A large collaboration compiles and compares merger rate predictions for massive black holes across multiple galaxy formation models to forecast LISA detections and quantify uncertainties.