A significant number of Lyman-break galaxies (LBGs) with redshifts are expected to be observed by the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). This will enable us to probe the Universe at higher redshifts than is currently possible with cosmological galaxy clustering and weak lensing surveys. However, accurate inference of cosmological parameters requires precise knowledge of the redshift distributions of selected galaxies, where the number of faint objects expected from LSST alone will make spectroscopic based methods of determining these distributions extremely challenging. To overcome this difficulty, it may be possible to leverage the information in the large volume of photometric data alone to precisely infer these distributions. This could be facilitated using forward models, where in this paper we use stellar population synthesis (SPS) to estimate uncertainties on LBG redshift distributions for a 10 yr LSST (LSSTY10) survey. We characterize some of the modelling uncertainties inherent to SPS by introducing a flexible parametrization of the galaxy population prior, informed by observations of the galaxy stellar mass function (GSMF) and cosmic star formation rate density (CSFRD). These uncertainties are subsequently marginalised over and propagated to cosmological constraints in a Fisher forecast, leveraging galaxy clustering and lensing of the cosmic microwave background (CMB). Assuming a known dust attenuation model for LBGs, we forecast constraints on the σ₈ parameter comparable to Planck CMB constraints.

We study star formation over (Formula presented) 12 Gyr using pop-cosmos, a generative model trained on 26-band photometry of (Formula presented) 420 000 COSMOS2020 galaxies (Spitzer IRAC (Formula presented)). The model learns distributions over 16 stellar population synthesis parameters via score-based diffusion, matching observed colours and magnitudes. We use pop-cosmos to compute the cosmic star formation rate density (SFRD) to (Formula presented) by directly integrating individual galaxy SFRs. The SFRD peaks at (Formula presented), (Formula presented) later than previous canonical estimates, with peak value (Formula presented) (Formula presented). We classify star-forming (SF) and quiescent (Q) galaxies using specific SFR (sSFR) (Formula presented) yr(Formula presented), comparing with (Formula presented) colour selection. The sSFR criterion yields up to 20 per cent smaller Q fractions across (Formula presented), with (Formula presented) -selected samples contaminated by galaxies with sSFR up to (Formula presented) yr(Formula presented). Our sSFR-selected stellar mass function shows a negligible number density of low-mass ((Formula presented)) Q galaxies at (Formula presented), where colour-selection shows a prominent increase. Non-parametric star formation histories around the SFRD peak reveal distinct patterns: SF galaxies show gradually weakening correlations between their recent and earlier SFRs, implying increasingly stochastic star formation towards early epochs. Q galaxies exhibit full correlation ((Formula presented)) during the most recent (Formula presented) 300 Myr, then sharp decorrelation with earlier SF epochs, marking clear quenching transitions. Massive ((Formula presented)) galaxies quench on a time-scale of (Formula presented) Gyr, with mass assembly concentrated in their first 3.5 Gyr. Finally, active galactic nucleus (AGN) activity (infrared torus luminosity fraction) peaks as massive ((Formula presented)) galaxies approach the transition between SF and Q states, declining sharply once quiescence is established. This provides evidence that AGN feedback operates in a critical regime during the (Formula presented) Gyr quenching transition.

We present a simple method for assessing the predictive performance of high-dimensional models directly in data space when only samples are available. Our approach is to compare the quantiles of observables predicted by a model to those of the observables themselves. In cases where the dimensionality of the observables is large (e.g., multiband galaxy photometry), we advocate that the comparison is made after projection onto a set of principal axes to reduce the dimensionality. We demonstrate our method on a series of two-dimensional examples. We then apply it to results from a state-of-the-art generative model for galaxy photometry () that generates predictions of colors and magnitudes by forward simulating from a 16-dimensional distribution of physical parameters represented by a score-based diffusion model. We validate the predictive performance of this model directly in a space of nine broadband colors. Although motivated by this specific example, we expect that the techniques we present will be broadly useful for evaluating the performance of flexible, nonparametric population models of this kind, and other settings where two sets of samples are to be compared.

We present an extension of the pop-cosmos model for the evolving galaxy population up to redshift z ∼ 6. The model is trained on distributions of observed colors and magnitudes, from 26-band photometry of ∼420,000 galaxies in the COSMOS2020 catalog with Spitzer IRAC Channel 1 < 26 mag. The generative model includes a flexible distribution over 16 stellar population synthesis (SPS) parameters, and a depth-dependent photometric uncertainty model, both represented using score-based diffusion models. We use the trained model to predict scaling relationships for the galaxy population, such as the stellar mass function, star-forming main sequence, and gas phase and stellar metallicity versus mass relations, demonstrating reasonable to excellent agreement with previously published results. We explore the connection between mid-infrared emission from active galactic nuclei (AGN) and star formation rate, finding high AGN activity for galaxies above the star-forming main sequence at 1 ≲ z ≲ 2. Using the trained population model as a prior distribution, we perform inference of the redshifts and SPS parameters for 429,669 COSMOS2020 galaxies, including 39,588 with publicly available spectroscopic redshifts. The resulting redshift estimates exhibit minimal bias (median[Δz] = −8 × 10−4), scatter (σMAD = 0.0132), and outlier fraction (6.19%) for the full 0 < z < 6 spectroscopic compilation. These results establish that pop-cosmos can achieve the accuracy and realism needed to forward model modern wide, deep surveys for Stage IV cosmology. We publicly release pop-cosmos software, mock galaxy catalogs, and COSMOS2020 redshift and SPS parameter posteriors.

Gravitational-wave (GW) observations of neutron star-black hole (NSBH) mergers are sensitive to the nuclear equation of state (EOS). We present a new methodology for EOS inference with nonparametric Gaussian process priors, enabling direct constraints on the pressure at specific densities and the length-scale of correlations on the EOS. Using realistic simulations of NSBH mergers, incorporating both GW and electromagnetic selection to ensure sample purity, we find that a GW detector network operating at O5 sensitivities will constrain the radius of a 1.4M⊙ NS and the maximum NS mass with 1.6% and 13% precision, respectively. With the same sample, the projected constraint on the length-scale of correlations in the EOS is ≥3.2 MeV fm-3. These results demonstrate strong potential for insights into the nuclear EOS from NSBH systems, provided they are robustly identified.

We present pop-cosmos: a comprehensive model characterizing the galaxy population, calibrated to 140,938 (r < 25 selected) galaxies from the Cosmic Evolution Survey (COSMOS) with photometry in 26 bands from the ultraviolet to the infrared. We construct a detailed forward model for the COSMOS data, comprising: a population model describing the joint distribution of galaxy characteristics and its evolution (parameterized by a flexible score-based diffusion model); a state-of-the-art stellar population synthesis model connecting galaxies’ intrinsic properties to their photometry; and a data model for the observation, calibration, and selection processes. By minimizing the optimal transport distance between synthetic and real data, we are able to jointly fit the population and data models, leading to robustly calibrated population-level inferences that account for parameter degeneracies, photometric noise and calibration, and selection. We present a number of key predictions from our model of interest for cosmology and galaxy evolution, including the mass function and redshift distribution; the mass-metallicity-redshift and fundamental metallicity relations; the star-forming sequence; the relation between dust attenuation and stellar mass, star formation rate, and attenuation-law index; and the relation between gas-ionization and star formation. Our model encodes a comprehensive picture of galaxy evolution that faithfully predicts galaxy colors across a broad redshift (z < 4) and wavelength range.

We present an efficient Bayesian method for estimating individual photometric redshifts and galaxy properties under a pretrained population model (pop-cosmos) that was calibrated using purely photometric data. This model specifies a prior distribution over 16 stellar population synthesis (SPS) parameters using a score-based diffusion model, and includes a data model with detailed treatment of nebular emission. We use a GPU-accelerated affine-invariant ensemble sampler to achieve fast posterior sampling under this model for 292,300 individual galaxies in the COSMOS2020 catalog, leveraging a neural network emulator (Speculator) to speed up the SPS calculations. We apply both the pop-cosmos population model and a baseline prior inspired by Prospector-α, and compare these results to published COSMOS2020 redshift estimates from the widely used EAZY and LePhare codes. For the ∼12,000 galaxies with spectroscopic redshifts, we find that pop-cosmos yields redshift estimates that have minimal bias (∼10−4), high accuracy (σ MAD = 7 × 10−3), and a low outlier rate (1.6%). We show that the pop-cosmos population model generalizes well to galaxies fainter than its r < 25 mag training set. The sample we have analyzed is ≳3× larger than has previously been possible via posterior sampling with a full SPS model, with average throughput of 15 GPU-sec per galaxy under the pop-cosmos prior, and 0.6 GPU-sec per galaxy under the Prospector prior. This paves the way for principled modeling of the huge catalogs expected from upcoming Stage IV galaxy surveys.

The concentration of SARS-CoV-2 RNA in faeces is not well characterised, posing challenges for quantitative wastewater-based epidemiology (WBE). We developed hierarchical models for faecal RNA shedding and fitted them to data from six studies. A mean concentration of 1.9 × 10⁶ mL^-1 (2.3 × 10⁵–2.0 × 10⁸ 95% credible interval) was found among unvaccinated inpatients, not considering differences in shedding between viral variants. Limits of quantification could account for negative samples based on Bayesian model comparison. Inpatients represented the tail of the shedding profile with a half-life of 34 hours (28–43 95% credible interval), suggesting that WBE can be a leading indicator for clinical presentation. Shedding among inpatients could not explain the high RNA concentrations found in wastewater, consistent with more abundant shedding during the early infection course. 

We present a forward-modeling framework for estimating galaxy redshift distributions from photometric surveys. Our forward model is composed of: a detailed population model describing the intrinsic distribution of the physical characteristics of galaxies, encoding galaxy evolution physics; a stellar population synthesis model connecting the physical properties of galaxies to their photometry; a data model characterizing the observation and calibration processes for a given survey; and explicit treatment of selection cuts, both into the main analysis sample and for the subsequent sorting into tomographic redshift bins. This approach has the appeal that it does not rely on spectroscopic calibration data, provides explicit control over modeling assumptions and builds a direct bridge between photo-z inference and galaxy evolution physics. In addition to redshift distributions, forward modeling provides a framework for drawing robust inferences about the statistical properties of the galaxy population more generally. We demonstrate the utility of forward modeling by estimating the redshift distributions for the Galaxy And Mass Assembly (GAMA) survey and the Vimos VLT Deep Survey (VVDS), validating against their spectroscopic redshifts. Our baseline model is able to predict tomographic redshift distributions for GAMA and VVDS with respective biases of Δz ≲ 0.003 and Δz ≃ 0.01 on the mean redshift—comfortably accurate enough for Stage III cosmological surveys—without any hyperparameter tuning (i.e., prior to doing any fitting to those data). We anticipate that with additional hyperparameter fitting and modeling improvements, forward modeling will provide a path to accurate redshift distribution inference for Stage IV surveys.

We present a Bayesian hierarchical framework to analyze photometric galaxy survey data with stellar population synthesis (SPS) models. Our method couples robust modeling of spectral energy distributions with a population model and a noise model to characterize the statistical properties of the galaxy populations and real observations, respectively. By self-consistently inferring all model parameters, from high-level hyperparameters to SPS parameters of individual galaxies, one can separate sources of bias and uncertainty in the data. We demonstrate the strengths and flexibility of this approach by deriving accurate photometric redshifts for a sample of spectroscopically confirmed galaxies in the COSMOS field, all with 26-band photometry and spectroscopic redshifts. We achieve a performance competitive with publicly released photometric redshift catalogs based on the same data. Prior to this work, this approach was computationally intractable in practice due to the heavy computational load of SPS model calls; we overcome this challenge by the addition of neural emulators. We find that the largest photometric residuals are associated with poor calibration for emission-line luminosities and thus build a framework to mitigate these effects. This combination of physics-based modeling accelerated with machine learning paves the path toward meeting the stringent requirements on the accuracy of photometric redshift estimation imposed by upcoming cosmological surveys. The approach also has the potential to create new links between cosmology and galaxy evolution through the analysis of photometric data sets.