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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SHAMe-SF modeling of small-scale DESI ELG clustering delivers 6% precision on σ8 and Ωm h², matching full DR1 results with 1% volume.
GalCatDiff applies category embeddings and a novel Astro-RAB block inside diffusion models to produce galaxy images whose color and size distributions match observations more closely than prior generative approaches.
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.
Multiple galaxy formation simulations show that low-mass quenched galaxies at z>3 are predominantly environmentally quenched satellites, often only temporarily so, and match JWST observations.
The SFR-M_* relation develops a high-mass decline at low redshifts, driven mainly by morphological quenching from internal structure rather than environmental effects on star-forming galaxies.
The Sagittarius dwarf progenitor had a metallicity gradient of roughly -0.3 dex per kpc prior to infall.
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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Cosmological constraints from the small scale clustering of Emission Line Galaxies
SHAMe-SF modeling of small-scale DESI ELG clustering delivers 6% precision on σ8 and Ωm h², matching full DR1 results with 1% volume.
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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.