Exhaustive symbolic regression identifies low-complexity functional forms for luminosity and mass functions that outperform Schechter and Press-Schechter parametrizations while satisfying physical extrapolation and integration constraints.
Villaescusa-Navarro, C
9 Pith papers cite this work, alongside 325 external citations. Polarity classification is still indexing.
9
Pith papers citing it
325
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
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.
Rescaling merger trees with a one-parameter halo-profile correction plus a semi-analytic model matches dedicated N-body suites for Ω_m and σ_8 inference at a fraction of the cost.
Generative models for cosmological field-level inference can reproduce posterior means and cross-correlations yet fail to capture correct uncertainty geometry when validated against HMC reference samples.
New symmetry-based FFT estimators cut the cost of power-spectrum and bispectrum multipole measurements by roughly half while providing analytic shot-noise subtraction and an open Python package.
A field-level CNN emulator converts MG-PICOLA runs into near N-body accuracy for f(R) gravity and neutrino cosmologies, achieving sub-percent errors on power spectra and bispectra while generalizing beyond its training set.
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.
GRAMSCI v2 replaces binary-search N-point enumeration with O(m) merge-walks, adds parity-decomposed and connected 4pCF, and ports the query engine to OpenACC GPUs with out-of-core tiling.
Lecture series on the physics, phenomenology, and statistics of large-scale cosmic structure evolution and non-Gaussian predictions.
citing papers explorer
-
The functional form of galaxy and halo luminosity and mass functions
Exhaustive symbolic regression identifies low-complexity functional forms for luminosity and mass functions that outperform Schechter and Press-Schechter parametrizations while satisfying physical extrapolation and integration constraints.
-
Perturbation-theory informed integrators for cosmological simulations
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.
-
Learning the Universe with cosmological rescaling of merger trees and semi-analytic galaxy formation models
Rescaling merger trees with a one-parameter halo-profile correction plus a semi-analytic model matches dedicated N-body suites for Ω_m and σ_8 inference at a fraction of the cost.
-
Learning the Universe: Posterior Reliability of Neural Generative Models in High-Dimensional Field-Level Inference of Cosmic Initial Conditions
Generative models for cosmological field-level inference can reproduce posterior means and cross-correlations yet fail to capture correct uncertainty geometry when validated against HMC reference samples.
-
Efficient estimators for power spectrum and bispectrum multipole measurements
New symmetry-based FFT estimators cut the cost of power-spectrum and bispectrum multipole measurements by roughly half while providing analytic shot-noise subtraction and an open Python package.
-
MG-NECOLA: A Field-Level Emulator for $f(R)$ Gravity and Massive Neutrino Cosmologies
A field-level CNN emulator converts MG-PICOLA runs into near N-body accuracy for f(R) gravity and neutrino cosmologies, achieving sub-percent errors on power spectra and bispectra while generalizing beyond its training set.
-
Efficiently emulating distribution functions in gigaparsec volumes for varying cosmological parameters
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.
-
Fast Graph-based Higher-Order Clustering Statistics on the GPU
GRAMSCI v2 replaces binary-search N-point enumeration with O(m) merge-walks, adds parity-decomposed and connected 4pCF, and ports the query engine to OpenACC GPUs with out-of-core tiling.
-
Large-scale structures of the Universe: physics, phenomenology, statistics
Lecture series on the physics, phenomenology, and statistics of large-scale cosmic structure evolution and non-Gaussian predictions.