Conditioning CAMELS-SAM simulations on the stellar mass function or stellar-to-halo mass relation reduces uncertainty in b_phi by 88-97% for DESI emission line galaxy samples while remaining consistent across galaxy formation variations.
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A GAN framework is trained on EAGLE simulation merger trees to generate new realistic trees for semi-analytic galaxy models at modest computational cost.
TNG50 shows galactic outflow mass loading is non-monotonic with stellar mass, rising rapidly above 10^10.5 Msun due to black hole feedback, and produces fast multi-phase outflows with emergent collimation.
The TNG SAM reproduces TNG hydro simulation gas and metal flows plus galaxy and halo properties within 30% accuracy out to z=6 via five targeted updates to the Santa Cruz SAM calibrated on stellar feedback-dominated galaxies.
citing papers explorer
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Informative Priors on Primordial Non-Gaussianity Bias $b_{\phi}$ From Galaxy Formation
Conditioning CAMELS-SAM simulations on the stellar mass function or stellar-to-halo mass relation reduces uncertainty in b_phi by 88-97% for DESI emission line galaxy samples while remaining consistent across galaxy formation variations.
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A Halo Merger Tree Generation and Evaluation Framework
A GAN framework is trained on EAGLE simulation merger trees to generate new realistic trees for semi-analytic galaxy models at modest computational cost.