Inverting Fisher biases enables quick checks on whether specific systematics explain cosmological tensions.
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Modelling baryonic feedback for survey cosmology
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abstract
Observational cosmology in the next decade will rely on probes of the distribution of matter in the redshift range between $0<z<3$ to elucidate the nature of dark matter and dark energy. In this redshift range, galaxy formation is known to have a significant impact on observables such as two-point correlations of galaxy shapes and positions, altering their amplitude and scale dependence beyond the expected statistical uncertainty of upcoming experiments at separations under 10 Mpc. Successful extraction of information in such a regime thus requires, at the very least, unbiased models for the impact of galaxy formation on the matter distribution, and can benefit from complementary observational priors. This work reviews the current state of the art in the modelling of baryons for cosmology, from numerical methods to approximate analytical prescriptions, and makes recommendations for studies in the next decade, including a discussion of potential probe combinations that can help constrain the role of baryons in cosmological studies. We focus, in particular, on the modelling of the matter power spectrum, $P(k,z)$, as a function of scale and redshift, and of the observables derived from this quantity. This work is the result of a workshop held at the University of Oxford in November of 2018.
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UNVERDICTED 10representative citing papers
Velocityformer achieves 35% higher velocity correlation than linear theory by matching graph transformer inductive bias to the line-of-sight broken symmetry and conditioning on long-wavelength physics, while training efficiently on only four low-fidelity simulations.
The GW-galaxy cross-correlation method, unified with spectral sirens in a harmonic framework, can measure H0 to 1% and Omega_m to 5% precision with 2 years of data from next-generation detectors like Einstein Telescope and Cosmic Explorer.
CosmoPostProcess delivers simulation-calibrated radial corrections for projection-induced selection bias (20-40% amplitude near 1 h^{-1} Mpc) and baryonic effects in Euclid richness-selected cluster weak lensing profiles.
Joint kSZ-clustering analysis of ACT DR6 and CMASS yields a halo optical depth profile more extended than Websky simulations, suggesting stronger baryonic feedback.
FRB DMs correlate at 2.6-5 sigma with galaxies, weak lensing, CIB, CMB lensing, tSZ, X-ray clusters, SXRB and radio continuum, consistent with moderate feedback models while ruling out weak feedback at 3.5 sigma via SXRB-DM.
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.
Baryonic feedback affects galaxy-galaxy, galaxy-shear, and shear-shear three-point correlation functions more strongly than two-point functions on small scales, reaching up to 90 percent suppression depending on redshift and model parameters.
Baryonic effects on dark matter halo density profiles exhibit strong secondary dependence on concentration (up to 15% variations at small scales for lower-mass halos) and weaker dependence on large-scale environment (~2%).
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.
citing papers explorer
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Inverting Fisher biases for fast systematics exploration
Inverting Fisher biases enables quick checks on whether specific systematics explain cosmological tensions.
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Velocityformer: Broken-Symmetry-Matched Equivariant Graph Transformers for Cosmological Velocity Reconstruction
Velocityformer achieves 35% higher velocity correlation than linear theory by matching graph transformer inductive bias to the line-of-sight broken symmetry and conditioning on long-wavelength physics, while training efficiently on only four low-fidelity simulations.
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A unified harmonic framework for dark siren cosmology
The GW-galaxy cross-correlation method, unified with spectral sirens in a harmonic framework, can measure H0 to 1% and Omega_m to 5% precision with 2 years of data from next-generation detectors like Einstein Telescope and Cosmic Explorer.
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Euclid preparation. CosmoPostProcess: A simulation calibrated framework for weak lensing selection bias in richness-selected galaxy clusters
CosmoPostProcess delivers simulation-calibrated radial corrections for projection-induced selection bias (20-40% amplitude near 1 h^{-1} Mpc) and baryonic effects in Euclid richness-selected cluster weak lensing profiles.
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Constraints on Halo Gas Profiles from Joint kSZ and Galaxy Clustering Analysis of ACT DR6 and CMASS
Joint kSZ-clustering analysis of ACT DR6 and CMASS yields a halo optical depth profile more extended than Websky simulations, suggesting stronger baryonic feedback.
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Backlighting the Cosmic Web with Fast Radio Bursts: An Anthology of Dispersion Measure Cross-Correlations with Large-Scale Structure and Baryon Tracers
FRB DMs correlate at 2.6-5 sigma with galaxies, weak lensing, CIB, CMB lensing, tSZ, X-ray clusters, SXRB and radio continuum, consistent with moderate feedback models while ruling out weak feedback at 3.5 sigma via SXRB-DM.
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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.
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$4\times3$ Point Correlation Functions in Galaxy Surveys: Impact of Baryonic Feedback
Baryonic feedback affects galaxy-galaxy, galaxy-shear, and shear-shear three-point correlation functions more strongly than two-point functions on small scales, reaching up to 90 percent suppression depending on redshift and model parameters.
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Secondary Dependence of Baryonic Effects on the Density Profile of Dark Matter Halos
Baryonic effects on dark matter halo density profiles exhibit strong secondary dependence on concentration (up to 15% variations at small scales for lower-mass halos) and weaker dependence on large-scale environment (~2%).
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Machine-learning applications for weak-lensing cosmology
Machine learning techniques can mitigate limitations in traditional weak-lensing analyses and enhance extraction of cosmological information from galaxy imaging surveys.