Multifidelity simulation-based inference enables accurate field-level weak lensing cosmology with 60-100 high-fidelity N-body simulations via pre-training on log-normal mocks.
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12 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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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.
tSZ cross-correlations with large-scale structure tracers prefer low S8 and strong baryonic feedback, yielding S8 = 0.72 and low group baryon fraction in FLAMINGO simulations.
SHAMe-SF modeling of small-scale DESI ELG clustering delivers 6% precision on σ8 and Ωm h², matching full DR1 results with 1% volume.
FRB dispersion measures directly constrain suppression of the matter power spectrum due to feedback at k ~ 0.1-3 h/Mpc, reduce posterior variance by a factor of ~8 at k~1 h/Mpc, and exclude extreme large-scale feedback scenarios at ~2 sigma.
First systematic validation shows Hybrid Bias Expansion model for galaxy bispectrum remains accurate up to k=0.25 h/Mpc in DESI-like mocks, outperforming tree-level EFT.
A minimal bias model yields unbiased LambdaCDM constraints up to k_max=0.7 h/Mpc but biases neutrino mass estimates, while higher-order bias mimics baryonic suppression in LSST 3x2pt analyses using the new MGL pipeline.
Simulated likelihood analysis shows Limber approximation, neglected RSD, and approximate nonlinear power spectra each induce cosmological biases of ~1 sigma or more (exceeding 2 sigma for Rubin) in Roman and Rubin 3x2pt studies.
cloelike is a new open Python package implementing composable Gaussian likelihoods for WL, GCph, GGL, full-shape spectra, and BAO in joint probe combinations for Euclid analyses.
cloelib is a modular JAX-based Python library for cosmological observables intended as reference infrastructure for Euclid's first data release.
FRBs serve as cosmological probes via dispersion measure, scattering, and Faraday rotation to constrain baryon distribution, expansion history, magnetic fields, and fundamental physics effects.
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.
citing papers explorer
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Field-level weak lensing cosmology with $<100$ simulations using multifidelity simulation-based inference
Multifidelity simulation-based inference enables accurate field-level weak lensing cosmology with 60-100 high-fidelity N-body simulations via pre-training on log-normal mocks.
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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.
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FLAMINGO: The thermal history of the Universe from tSZ effect cross-correlations and its dependencies on cosmology and baryon physics
tSZ cross-correlations with large-scale structure tracers prefer low S8 and strong baryonic feedback, yielding S8 = 0.72 and low group baryon fraction in FLAMINGO simulations.
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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.
-
Signatures of Suppressed Matter Clustering revealed by Fast Radio Bursts
FRB dispersion measures directly constrain suppression of the matter power spectrum due to feedback at k ~ 0.1-3 h/Mpc, reduce posterior variance by a factor of ~8 at k~1 h/Mpc, and exclude extreme large-scale feedback scenarios at ~2 sigma.
-
Validation of the Hybrid Bias Expansion model for the galaxy bispectrum
First systematic validation shows Hybrid Bias Expansion model for galaxy bispectrum remains accurate up to k=0.25 h/Mpc in DESI-like mocks, outperforming tree-level EFT.
-
Balancing bias, baryons, and scale cuts in LSST 3x2pt analysis
A minimal bias model yields unbiased LambdaCDM constraints up to k_max=0.7 h/Mpc but biases neutrino mass estimates, while higher-order bias mimics baryonic suppression in LSST 3x2pt analyses using the new MGL pipeline.
-
Accurate modeling for 3$\times$2pt analyses in Roman and Rubin: a study of model approximations
Simulated likelihood analysis shows Limber approximation, neglected RSD, and approximate nonlinear power spectra each induce cosmological biases of ~1 sigma or more (exceeding 2 sigma for Rubin) in Roman and Rubin 3x2pt studies.
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cloelike: A Python Library for Cosmological Likelihood Inference in the Euclid Era
cloelike is a new open Python package implementing composable Gaussian likelihoods for WL, GCph, GGL, full-shape spectra, and BAO in joint probe combinations for Euclid analyses.
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cloelib: A Flexible Python Library for Computing Cosmological Observables in the Euclid Era
cloelib is a modular JAX-based Python library for cosmological observables intended as reference infrastructure for Euclid's first data release.
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Fast Radio Bursts as Cosmological Probes
FRBs serve as cosmological probes via dispersion measure, scattering, and Faraday rotation to constrain baryon distribution, expansion history, magnetic fields, and fundamental physics effects.
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Interpreting "Interpretability" and Explaining "Explainability" in Machine Learning in Physics
The paper defines interpretability as model structural transparency and explainability as scientific content mapping, discusses their trade-offs, and frames both as deliberate modeling choices for ML in physics.