We present a new approach to measuring cosmic shear: the one-component Kinematic Lensing (KL) method. This technique provides a simplified implementation of KL that reduces shape noise in weak lensing (WL) by combining kinematic information with imaging data, while requiring less observational effort than the full two-component KL. We perform simulated likelihood analyses to assess the performance of the one-component KL and demonstrate its applicability to future radio surveys. Our forecasts indicate that, for radio surveys, the one-component KL is not yet competitive with traditional WL due to the shallow redshift distribution of HI-selected galaxies. However, when applying this method to deeper spectroscopic surveys with stronger emission lines, the one-component KL approach could surpass WL in constraining power, offering a promising and efficient pathway for future shear analyses.

Machine learning can accelerate cosmological inferences that involve many sequential evaluations of computationally expensive data vectors. Previous works in this series have examined how machine learning architectures impact emulator accuracy and training time for optical shear and galaxy clustering two-point function. In this final manuscript, we explore neural network performance when emulating cosmic microwave background (CMB) temperature and polarization power spectra. We maximize the volume of applicability in the parameter space of our emulators within the standard Lambda-cold-dark-matter model while ensuring that errors are below cosmic variance. Relative to standard multi-layer perceptron architectures, we find that the dot-product-attention mechanism reduces the number of outliers among testing cosmologies, defined as the fraction of testing points with Delta chi 2 > 0.2 relative to CAMB outputs, for a wide range of training set sizes. Such precision enables attention-based emulators to be directly applied to real data without requiring any additional correction via importance sampling. Combined with preprocessing techniques and optimized activation and loss functions, attention-based models can meet the precision criteria set by current and future CMB and lensing experiments. For each of Planck, Simons Observatory, CMB-S4, and CMB-HD, we find the fraction of outlier points to be less than 10% with around 2 & times; 10(5) to 4 & times; 10(5) training data vectors. We further explore the applications of these methods to supernova distance, weak lensing, and galaxy clustering, as well as alternative architectures and preprocessing techniques.

Fast Radio Bursts (FRBs) probe baryons permeating the cosmic web through their dispersion measures (DMs), which encode the integrated electron density along cosmological sightlines. Using 3,455 unique FRB sources from CHIME/FRB with ∼ 15 arcmin localizations, we present an anthology of DM correlations with tracers of large-scale structure and baryonic matter at redshifts z ≲ 1.5. We measure statistically significant correlations at 2.6-5σ with ten probes, including galaxies (2.8σ), weak gravitational lensing (2.6σ), cosmic infrared background (4.0σ), cosmic microwave background (CMB) lensing (3.3σ), thermal Sunyaev Zel'dovich (tSZ) effect (3.8σ), X-ray emission tracing galaxy clusters (5.0σ) and superclusters (3.3σ), soft X-ray background (SXRB, 4.1σ), and radio continuum emission (3.2σ). These measurements reveal a consistent picture in which FRB sightlines intersecting overdense environments carry systematically larger DMs. Correlations with hot-gas tracers provide additional leverage on the strength of feedback, as they are strongly weighted towards the dense, bound gas. The measured amplitude of tSZ×DM and SXRB×DM correlations are consistent with theoretical predictions of baryon distribution from a DM-z relation-inferred model with moderate feedback at ∼ 0.5σ level. Weaker feedback scenario is ruled out at ∼ 3.5σ by the SXRB×DM correlation. Taken together, these measurements constitute a quantitative multi-tracer foundation for a new era in which FRBs from next generation facilities, such as BURSTT, CHORD, DSA, and SKA, in harmony with other probes, will map the baryon content of the full extent of the cosmic web.

The parameter f_NL measures the local non-Gaussianity in the primordial energy fluctuations of the Universe, with any deviation from f_NL=0 providing key constraints on inflationary models. Galaxy clustering is sensitive to f_NL at large scale modes and the next generation of galaxy surveys will approach a statistical error of σ_f_NL∼1. However, the systematic errors on these constraints are dominated by the degeneracy of f_NL with the galaxy bias parameters b_1 (galaxy overdensities caused by mass perturbations) and b_ϕ (galaxy overdensities caused by primordial potential perturbations). It has been shown that the assumed scaling of b_ϕ(z)=2δ_c (b_1(z)-1) is not accurate for realistically simulated galaxies, and depends both on the galaxy selection and the way that galaxies are modeled. To address this, we leverage the CAMELS-SAM pipeline to explore how varying parameters of galaxy formation affects b_ϕ and b_1 for various galaxy selections. We run separate-universe N-body simulations of L=205 h^-1 cMpc and N=1280^3 to measure b_ϕ, and run 55 unique instances of the Santa Cruz semi-analytic model with varying parameters of stellar and AGN feedback. We find the behavior and evolution of a SC-SAM model's stellar-, SFR- and sSFR- to halo mass relationships track well with how b_1 and b_ϕ(b_1) change across redshift and selection for the SC-SAM. We find our variations of the SC-SAM encapsulate the b_ϕ behavior previously measured in IllustrisTNG, the Munich SAM, and Galacticus.Finally, we identify sSFR selections as particularly robust to varied galaxy modeling.

Characterization of the redshift distribution of ensembles of galaxies is pivotal for large scale structure cosmological studies. In this work, we focus on improving the Self-Organizing Map (SOM) methodology for photometric redshift estimation (SOMPZ), specifically in anticipation of the Dark Energy Survey Year 6 (DES Y6) data. This data set, featuring deeper and fainter galaxies than DES Year 3 (DES Y3), demands adapted techniques to ensure accurate recovery of the underlying redshift distribution. We investigate three strategies for enhancing the existing SOM-based approach used in DES Y3: 1) Replacing the Y3 SOM algorithm with one tailored for redshift estimation challenges; 2) Incorporating $\textit{g}$-band flux information to refine redshift estimates (i.e. using $\textit{griz}$ fluxes as opposed to only $\textit{riz}$); 3) Augmenting redshift data for galaxies where available. These methods are applied to DES Y3 data, and results are compared to the Y3 fiducial ones. Our analysis indicates significant improvements with the first two strategies, notably reducing the overlap between redshift bins. By combining strategies 1 and 2, we have successfully managed to reduce redshift bin overlap in DES Y3 by up to 66$\%$. Conversely, the third strategy, involving the addition of redshift data for selected galaxies as an additional feature in the method, yields inferior results and is abandoned. Our findings contribute to the advancement of weak lensing redshift characterization and lay the groundwork for better redshift characterization in DES Year 6 and future stage IV surveys, like the Rubin Observatory.

The pristine underdense patches of the Universe, cosmic voids, are powerful cosmological laboratories, uniquely sensitive to dark energy, modified gravity, and neutrino masses, yet their baryonic content remains uncharacterized. We present the first constraint on baryon underdensity in voids, exploiting the dispersion measures (DMs) of fast radio bursts (FRBs) as tracers of the electron column. By stacking 3455 sight lines from CHIME/FRB with similar to 15 ' localizations on 1228 Sloan Digital Sky Survey (SDSS) BOSS voids over redshifts 0.2 < z < 0.7, we measure a DM deficit toward void centers at 3.2 sigma significance, indicating that diffuse baryons inhabit the emptiest corners of the cosmic web at a suppressed level. The measured signal amplitude is consistent with an effective Universe model built directly from the observed galaxy underdensity in these voids, and a baryonic model calibrated to the FRB DM-redshift relation (alpha(v) = 1.80 +/- 0.87). A uniform-density void model yields an electron density contrast of delta(e,v) = -0.58 +/- 0.30, implying a tentative similar to 60% +/- 30% underdensity of baryons in void interiors relative to the cosmic mean. Jointly interpreting our FRB measurement with existing stacks of the thermal Sunyaev-Zel'dovich effect on voids further constrains the mean gas temperature to T-e less than or similar to (1.1 +/- 0.7) & times; 10(6) K, pointing to a warm-hot diffuse phase, consistent with hydrodynamical simulations. With forthcoming FRB and galaxy surveys, this approach opens a new window onto baryon mapping, with direct implications for feedback models governing gas expulsion into low-density environments, and for the use of cosmic voids to extract cosmological constraints.

Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx), a NASA Explorer satellite launched on 2025 March 11, is carrying out the first all-sky near-infrared spectral survey. The satellite observes in 102 spectral bands from 0.75 to 5.0 mu m with a resolving power ranging from lambda/Delta lambda = 35-130 in 6 .'' 2 pixels. The observatory obtains a 5 sigma depth of 19.5-19.9 AB mag for 0.75 < lambda < 3.8 mu m with lambda/Delta lambda similar to 40 and 17.8-18.8 AB mag for 3.8 < lambda < 5.0 mu m with lambda/Delta lambda similar to 120 after mapping the full sky four times over two years. Scientifically, SPHEREx will produce a large galaxy redshift survey over the full sky to constrain the amplitude of inflationary non-Gaussianity. The observations will produce two deep spectral maps near the ecliptic poles that use intensity mapping to probe the evolution of galaxies over cosmic history. By mapping the depth of infrared absorption features over the Galactic plane, SPHEREx will comprehensively survey the abundance and composition of water and other biogenic ice species in the interstellar medium. The project will release initial data rapidly in the form of spectral images, and specialized data products over the life of the mission as the surveys proceed. The science team will also produce spectral catalogs of planet-bearing and low-mass stars, solar system objects, and galaxy clusters three years after launch. We describe the design of the instrument and spacecraft, which flow from the core science requirements. Finally, we present an initial evaluation of the satellite's in-flight performance and key characteristics.

Constraining primordial non-Gaussianity via its scale-dependent imprint on galaxy clustering requires knowledge of the bias parameter b_ϕ, which is exactly degenerate with f^loc_NL at leading order. To break this degeneracy, current analyses adopt the relation (b_ϕ = 2δ_c(b_1 - 1)) based on the assumption of a universal mass function. This relation is known to break down for physically motivated galaxy selections, introducing systematic errors in the inferred f^loc_NL that scale directly with the assumed b_ϕ prior. We present a framework to construct physically motivated, observation-conditioned priors on b_ϕ by marginalizing over galaxy formation uncertainties. We use the CAMELS-SAM simulation suite, augmented by separate Universe simulations, to measure galaxy formation observables, like the stellar mass function (SMF) and the stellar-to-halo mass relationship (SHMR), and b_ϕ across a range of galaxy formation parameters. From these measurements, we construct a distribution of b_ϕ conditioned on observations, and we select our galaxy sample to resemble the DESI Emission Line Galaxy (ELG) sample. Conditioning on the SMF or SHMR decreases σ_b_ϕ from 0.69 to 0.08 and 0.02 respectively – reductions of 88% and 97% – with consistent results when conditioning on the observed data directly. Despite substantial shifts in the galaxy formation posteriors driven by known SC-SAM discrepancies at high halo masses, the resulting b_ϕ distributions remain mutually consistent across all observables. The SMF and SHMR are found to carry sufficient constraining power to reduce the galaxy formation uncertainty in b_ϕ relevant for f^loc_NL inference with next-generation spectroscopic surveys

Fast radio bursts (FRBs) have emerged as powerful probes of baryonic matter in the Universe, offering constraints on cosmological and feedback parameters through their extragalactic dispersion measure-redshift (DMexgal-z) relation. However, the observed FRB population is shaped by complex selection effects arising from instrument sensitivity, DM-dependent search efficiency, and FRB source population redshift evolution. In this work, we quantify the impact of such observational and population selection effects on cosmological inference derived from the conditional distribution p(DMexgal divided by z). Using forward-modeled FRB population simulations, we explore progressively realistic survey scenarios incorporating redshift evolution, luminosity function, and instrument DM selection function. To enable rapid likelihood evaluations, we build a neural network emulator for the variance in cosmic DM, sigma 2[DMcosmic(z)], trained on 5 & times; 104 baryonification halo model simulations, achieving <= 4% accuracy up to z = 4. We demonstrate that while redshift- and DM-dependent selection effects substantially alter the joint distribution p(DM, z), they have a negligible impact on the conditional distribution p(DMexgal divided by z) for current sample sizes. The parameter biases are less than or similar to 0.8 sigma for 102 FRBs, indicating that conditional analyses are robust for present surveys. However, depending on the survey DM-dependent search efficiency, these biases may exceed 3 sigma for 104 FRBs, thus implying that explicit modeling of selection effects will be essential for next-generation samples.

Complex astrophysical feedback processes regulate the growth of galaxies by redistributing baryons across megaparsec scales. The clustering of matter on these scales, measured via weak lensing of galaxies, encodes critical cosmological information—including on the dynamical dark energy, the nature of dark matter and the sum of neutrino masses. Probes of baryons, including X-rays and Sunyaev–Zel’dovich effects, have attempted to quantify the impact of feedback on matter clustering. The dispersion measures of fast radio bursts (FRBs) have emerged as a powerful new baryon probe, with current samples sensitive to low-redshift (z ≲ 0.3) groups and clusters—the regime critical for interpreting weak lensing measurements and for arbitrating tensions in X-ray observations. Here, using 114 FRBs, we infer spatial fluctuations in the baryon density, quantifying the effects of feedback on the matter power spectrum at k ≈ 0.1–3 h Mpc−1 scales, and the gas mass fraction in ≳1013 M⊙ halos. Strikingly, these constraints are already competitive with the legacy measurements from Atacama Cosmology Telescope and eROSITA. This work establishes FRBs as a sensitive probe of feedback-regulated structure formation, poised to deliver leading constraints on baryonic physics in the era of precision cosmology. Analysis of fast radio bursts reveals how gas ejected by astrophysical feedback suppresses small-scale cosmic structure, delivering constraints competitive with X-ray and microwave surveys. Future rapid gains are expected from facilities such as the Deep Synoptic Array.

Upcoming Stage-IV surveys will deliver measurements of distribution of matter with unprecedented precision, demanding highly accurate theoretical models for cosmological parameter inference. A major source of modeling uncertainty lies in astrophysical processes associated with galaxy formation and evolution, which remain poorly understood. Probes such as the thermal and kinematic Sunyaev-Zel'dovich effects, X-rays, and dispersion measure from fast radio bursts offer a promising avenue for mapping the distribution and thermal properties of cosmic baryons. A unified analytical framework capable of jointly modeling these observables is essential for fully harnessing the complementary information while mitigating probe-specific systematics. In this work, we present a detailed assessment of existing analytical models, which differ in their assumptions and prescriptions for simultaneously describing the distribution of matter and baryons in the universe. Using the Magneticum hydrodynamical simulation, we test these models by jointly analyzing the 3D auto- and cross-power spectra of the matter and baryonic fields that underpin the above probes. We find that all models can reproduce the power spectra at sub-percent to few-percent accuracy, depending on the tracer combination and number of free parameters. Their ability to recover underlying halo properties, such as the evolution of gas abundance and thermodynamic profiles with halo mass, varies considerably. Our results suggest that these models require further refinement and testing for reliable interpretation of multi-wavelength datasets.

The impact of galaxy formation processes on the matter power spectrum is uncertain. Fast radio bursts (FRBs), through their dispersion measures (DMs) encoding the integrated baryon density, offer a unique window into gas distribution. In this work, we investigate the constraining power of a 3 × 2-point correlation statistic of DMs and galaxies. We present the correlation formalism, derive covariance matrices, and forecast signal-to-noise ratios (SNRs) and Fisher constraints. Assuming a host DM variance of 90 pc cm ^−3 , for 10 ^4 (10 ^5 ) FRBs across 35% of the sky, the angular DM power spectrum is noise dominated at multipoles ℓ ≳ 20 (100), implying that the analysis can be conducted using arcminute localizations. In practice, this will be limited by uncertain host associations, which are expected to impact the mean DM subtraction—and thus our assumed value of σ _host = 90 pc cm ^−3 —in DM perturbations. While 10 ^4 (10 ^5 ) FRBs can constrain cosmological parameters at the ∼40%–70% (30%–40%) level, this is a factor of ∼2–3 (1.5–2) weaker than the precision attainable with galaxy clustering alone due to shot noise from the FRB number density, variance of the field, and host DMs. On the contrary, feedback-sensitive scales are not currently accessible in galaxy surveys. We demonstrate that combining DM and galaxy correlations in a 3 × 2-point analysis breaks feedback–cosmology degeneracies, yielding ∼10%–18% (7%–13%) precision on cosmological parameters and ∼3% (2%) constraints on feedback using 10 ^4 (10 ^5 ) FRBs. For the range of accessible scales, the SNR does not vary significantly with feedback. This work positions the DM–galaxy 3 × 2 point statistic as a promising multiprobe strategy.

We introduce an updated To&Krause2021 model for joint analyses of cluster abundances and large-scale two-point correlations of weak lensing and galaxy and cluster clustering (termed CL thorn 3 x 2 pt analysis) and validate that this model meets the systematic accuracy requirements of analyses with the statistical precision of the final Dark Energy Survey (DES) Year 6 (Y6) dataset. The validation program consists of two distinct approaches, (i) identification of modeling and parametrization choices and impact studies using simulated analyses with each possible model misspecification and (ii) end-to-end validation using mock catalogs from customized Cardinal simulations that incorporate realistic galaxy populations and DES-Y6-specific galaxy and cluster selection and photometric redshift modeling, which are the key observational systematics. In combination, these validation tests indicate that the model presented here meets the accuracy requirements of DES-Y6 for CL thorn 3 x 2 pt based on a large list of tests for known systematics. In addition, we also validate that the model is sufficient for several other data combinations: the CL thorn GC subset of this data vector (excluding galaxy-galaxy lensing and cosmic shear two-point statistics) and the CL thorn 3 x 2 pt thorn BAO thorn SN (combination of CL thorn 3 x 2 pt with the previously published Y6 DES baryonic acoustic oscillation and Y5 supernovae data).

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the Universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements (3 x 2pt) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining Lambda cold dark matter (Lambda CDM) parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure Omega(m) = 0.300 +/- 0.017 and sigma(8) = 0.797 +/- 0.026. Compared to constraints from Planck primary cosmic microwave background (CMB) anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 (1.2 sigma) for the two-parameter difference. We further obtain S-8 equivalent to sigma(8)(Omega(m)/0.3)(0.5) = 0.796 +/- 0.013 which is lower than the Planck measurement at the 1.6 sigma level. The combined SPT cluster, DES 3 x 2pt, and Planck datasets mildly prefer a nonzero positive neutrino mass, with a 95% upper limit Sigma m(nu) < 0.25 eV on the sum of neutrino masses. Assuming a wCDM model, we constrain the dark energy equation of state parameter w = -1.15(-0.17)(+0.23) and when combining with Planck primary CMB anisotropies, we recover w = -1.20(-0.09)(+0.15), a 1.7 sigma difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology and our analysis paves the way for upcoming joint analyses of next-generation datasets.

Statistical properties of LSS serve as powerful tools to constrain the cosmological properties of our Universe. Tracing the gas pressure, the tSZ effect is a biased probe of mass distribution and can be used to test the physics of feedback or cosmological models. Therefore, it is crucial to develop robust modeling of hot gas pressure for applications to tSZ surveys. Since gas collapses into bound structures, it is expected that most of the tSZ signal is within halos produced by cosmic accretion shocks. Hence, simple empirical halo models can be used to predict the tSZ power spectra. In this study, we employed the HMx halo model to compare the tSZ power spectra with those of several hydrodynamical simulations: the Horizon suite and the Magneticum simulation. We examined various contributions to the tSZ power spectrum across different redshifts, including the one- and two-halo term decomposition, the amount of bound gas, the importance of different masses and the electron pressure profiles. Our comparison of the tSZ power spectrum reveals discrepancies that increase with redshift. We find a 20 and predicted tSZ angular power spectrum over the multipole range ℓ=10^3-10^4. Our analysis reveals that these differences are driven by the excess of power in the predicted two-halo term at low k and in the one-halo term at high k. At higher redshifts (z 3), simulations indicate that more power comes from outside the virial radius than from inside suggesting a limitation in the applicability of the halo model. We observe differences in the pressure profiles, despite the fair level of agreement on the tSZ power spectrum at low redshift with the default calibration of the halo model. In conclusion, our study suggests that the properties of the halo model need to be carefully controlled against real or mock data to be proven useful for cosmological purposes.

Statistical properties of large-scale cosmological structures serve as powerful tools for constraining the cosmological properties of our Universe. Tracing the gas pressure, the thermal Sunyaev-Zel'dovich (tSZ) effect is a biased probe of mass distribution and, hence, can be used to test the physics of feedback or cosmological models. Therefore, it is crucial to develop robust modelling of hot gas pressure for applications to tSZ surveys. Since gas collapses into bound structures, it is expected that most of the tSZ signal is within halos produced by cosmic accretion shocks. Hence, simple empirical halo models can be used to predict the tSZ power spectra. In this study, we employed the HMx halo model to compare the tSZ power spectra with those of several hydrodynamical simulations: the Horizon suite and the Magneticum simulation. We examine various contributions to the tSZ power spectrum across different redshifts, including the one- and two-halo term decomposition, the amount of bound gas, the importance of different masses, and the electron pressure profiles. Our comparison of the tSZ power spectrum reveals discrepancies between the halo model and cosmological simulations that increase with redshift. We find a 20% to 50% difference between the measured and predicted tSZ angular power spectrum over the multipole range & ell; = 10(3) - 10(4). Our analysis reveals that these differences are driven by the excess of power in the predicted two-halo term at low k and in the one-halo term at high k. At higher redshifts (z similar to 3), simulations indicate that more power comes from outside the virial radius than from inside, suggesting a limitation in the applicability of the halo model. We also observe differences in the pressure profiles, despite the fair level of agreement on the tSZ power spectrum at low redshift with the default calibration of the halo model. In conclusion, our study suggests that the properties of the halo model need to be carefully controlled against real or mock data to be proven useful for cosmological purposes.

The impact of galaxy formation processes on the matter power spectrum is uncertain and may bias cosmological parameters inferred by large-scale structure surveys. Fast Radio Bursts (FRBs), through their dispersion measures (DMs) encoding the integrated column density of baryons, offer a unique window into the distribution of gas. In this work, we investigate the constraining power of a 3x2-point correlation statistic of FRB DMs and galaxies. We present the correlation formalism, derive covariance matrices and forecast signal-to-noise ratios and Fisher parameter constraints. Assuming host galaxy DM variance of 90 pc cm^-3, for 10^4 (10^5) FRBs across 35

We present a new class of machine-learning emulators that accurately model the cosmic shear, galaxy-galaxy lensing, and galaxy clustering real space correlation functions in the context of Rubin Observatory year one simulated data. To illustrate its capabilities in forecasting models beyond the standard $\Lambda$CDM, we forecast how well LSST Year 1 data will be able to probe the consistency between geometry $\Omega^{\rm geo}_\mathrm{m}$ and growth $\Omega^{\rm growth}_\mathrm{m}$ dark matter densities in the so-called split $\Lambda$CDM parameterization. When trained with a few million samples, our emulator shows uniform accuracy across a wide range in an 18-dimensional parameter space. We provide a detailed comparison of three neural network designs, illustrating the importance of adopting state-of-the-art Transformer blocks. Our study also details their performance when computing Bayesian evidence for cosmic shear on three fiducial cosmologies. The transformers-based emulator is always accurate within PolyChord's precision. As an application, we use our emulator to study the degeneracies between dark energy models and growth geometry split parameterizations. We find that the growth-geometry split remains to be a meaningful test of the smooth dark energy assumption.

Understanding the impact of baryonic feedback on the small-scale ( k ≳ 1 h Mpc ^−1 ) matter power spectrum is a key astrophysical challenge, and essential for interpreting data from upcoming weak-lensing surveys, which require percent-level accuracy to fully harness their potential. Astrophysical probes, such as the kinematic and thermal Sunyaev–Zel’dovich effects, have been used to constrain feedback at large scales ( k ≲ 5 h Mpc ^−1 ). The sightline-to-sightline variance in the fast radio bursts (FRBs) dispersion measure (DM) correlates with the strength of baryonic feedback and offers unique sensitivity at scales up to k ∼ 10 h Mpc ^−1 . We develop a new simulation-based formalism in which we parameterize the distribution of DM at a given redshift, p (DM∣ z ), as a log-normal with its first two moments computed analytically in terms of cosmological parameters and the feedback-dependent electron power spectrum P _ee ( k , z ). We find that the log-normal parameterization provides an improved description of the p (DM∣ z ) distribution observed in hydrodynamical simulations as compared to the standard F -parameterization. Our model robustly captures the baryonic feedback effects across a wide range of baryonic feedback prescriptions in hydrodynamical simulations, including IllustrisTNG , SIMBA , and Astrid . Leveraging simulations incorporates the redshift evolution of the DM variance by construction and facilitates the translation of constrained feedback parameters to the suppression of matter power spectrum relative to gravity-only simulations. We show that with 10 ^4 FRBs, the suppression can be constrained to percent-level precision at large scales and ∼10% precision at scales k ≳ 10 h Mpc ^−1 with prior-to-posterior 1 σ constraint width ratio ≳20.

We combine weak lensing, galaxy clustering, cosmic microwave background (CMB) lensing, and their cross-correlations (so-called 6×2pt) to constrain cosmology and baryonic feedback scenarios using data from the Dark Energy Survey (DES) Y3 Maglim catalog and the Planck satellite PR4 data release. We include all data points in the DES Y3 cosmic shear two-point correlation function (2PCF) down to 2.^'5 and model baryonic feedback processes via principal components (PCs) that are constructed from the ANTILLES simulations. We find a tight correlation between the amplitude of the first PC Q_1 and mean normalized baryon mass fraction Y̅_̅b̅=f̅_b/(Ω_b/Ω_m) from the ANTILLES simulations and employ an independent Y̅_̅b̅ measurement from Akino et al. (2022) as a prior of Q_1. We train a neural network 6×2pt emulator to boost the analysis speed by 𝒪(10^3), which enables us to run an impressive number of simulated analyses to validate our analysis against various systematics. For our 6×2pt analysis, we find S_8=0.8073±0.0094 when including a Q_1 prior from Y̅_̅b̅ observations. This level of cosmological constraining power allows us to put tight constraints on the strength of baryonic feedback. We find Q_1=0.025^+0.024_-0.029 for our 6×2pt analysis and Q_1=0.043±0.016 when combining with external information from Planck, ACT, DESI. All these results indicate weak feedback, e.g., the tensions to Illustris (Q_1=0.095) and OWLS AGN T8.7 (Q_1=0.137) are 2.9σ-3.3σ and 4.7σ-5.9σ, respectively.