Simulation-based Deep Sets model with neural posterior estimation halves scatter in cluster mass estimates from galaxy kinematics compared to the M-sigma relation.
The Uchuu simulations: Data Release 1 and dark matter halo concentrations , volume=
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LPT-matched integrators for cosmological simulations outperform FastPM with O(1-100) timesteps while convergence is limited to order 3/2 post-shell-crossing due to acceleration field irregularity.
PHANTOM is a public MATLAB/Octave toolbox for linear field statistics and halo observables in dark matter cosmology, validated to sub-percent agreement with Python packages colossus, hmf, and halomod.
Corrected empirical limits show the most massive galaxies never exceed the theoretical baryonic maximum of 0.16 times halo virial mass, keeping observations consistent with LambdaCDM at all redshifts.
A new overdensity-conditioned emulator trained on small subvolumes from Quijote recovers the global halo mass function via integration over the overdensity distribution at 0.026% of the simulation cost.
Radio contamination must be jointly fit with tSZ and CIB in unWISE-Planck/ACT cross-spectra; its inclusion yields positive signals to ℓ ≃ 6000 that match a two-parameter generalized NFW pressure profile.
Empirical universal fitting formula for the peak (most probable) concentration of dark matter halos derived from lognormal fits to simulation distributions and shown to be invariant across cosmologies.
BayeSN analysis of ZTF Type Ia supernovae confirms a ~0.1 mag intrinsic environmental step in standardized brightness that is not explained by differences in dust extinction properties.
A conditional graph neural network serves as an accurate and fast surrogate for semi-analytic galaxy formation models, predicting key properties across cosmic time.
Lambda CDM with the UniverseMachine model on the Uchuu simulation matches JWST/HST UV observations at z=7-14 and predicts star formation efficiency rising to 2-3% by z=10-12.
Convolutional neural networks can infer galaxy cluster virial masses and scale radii from 2D projected position and line-of-sight velocity distributions with nearly unbiased results and reduced scatter when richness is added or training is limited to relaxed systems.
A new halo occupation model called HOMe reproduces the anisotropic clustering of ELGs and LRGs down to 200 h^{-1} kpc scales by sampling satellites from dark matter particle positions and fitting parameters to two-point statistics.
Weak-lensing analysis of Abell 85 resolves three substructures and finds a ~2:1 mass ratio between the main cluster and southern subcluster, indicating a major ongoing merger.
AnaCal recovers input shear with low bias in high-shear cluster regimes under LSST-like conditions, producing 0.24% mean mass bias.
New CAMELS simulations in larger (50 Mpc/h)^3 boxes with 35 varied parameters produce tighter neural-network constraints on model parameters than prior smaller-volume runs, with public data release.
Lecture series on the physics, phenomenology, and statistics of large-scale cosmic structure evolution and non-Gaussian predictions.
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PHANTOM: A MATLAB and Octave Toolbox Connecting Linear Field Statistics to Dark Matter Halo Observables
PHANTOM is a public MATLAB/Octave toolbox for linear field statistics and halo observables in dark matter cosmology, validated to sub-percent agreement with Python packages colossus, hmf, and halomod.