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We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ and X-ray images as conditional inputs, and generates realizations of corresponding gas and dark matter maps by sampling from a learned data posterior. We train and validate the performance of our model by using mock data from a hydrodynamical cosmological simulation. The model accurately reconstructs both the mean and spread of the radial density profiles in the spatial domain, indicating that the model is able to distinguish between clusters of different mass sizes. In the spectral domain, the model achieves close-to-unity values for the bias and cross-correlation coefficients, indicating that the model can accurately probe cluster structures on both large and small scales. Our experiments demonstrate the ability of score models to learn a strong, nonlinear, and unbiased mapping between input observables and fundamental density distributions of galaxy clusters. These diffusion models can be further fine-tuned and generalized to not only take in additional observables as inputs, but also real observations and predict unknown density distributions of galaxy clusters. 

Abstract Cosmological simulations like CAMELS and IllustrisTNG characterize hundreds of thousands of galaxies using various internal properties. Previous studies have demonstrated that machine learning can be used to infer the cosmological parameter Ω_mfrom the internal properties of even a single randomly selected simulated galaxy. This ability was hypothesized to originate from galaxies occupying a low-dimensional manifold within a higher-dimensional galaxy property space, which shifts with variations in Ω_m. In this work, we investigate how galaxies occupy the high-dimensional galaxy property space, particularly the effect of Ω_mand other cosmological and astrophysical parameters on the putative manifold. We achieve this by using an autoencoder with an information-ordered bottleneck, a neural layer with adaptive compression, to perform dimensionality reduction on individual galaxy properties from CAMELS simulations, which are run with various combinations of cosmological and astrophysical parameters. We find that for an autoencoder trained on the fiducial set of parameters, the reconstruction error increases significantly when the test set deviates from fiducial values of Ω_mandA_SN1, indicating that these parameters shift galaxies off the fiducial manifold. In contrast, variations in other parameters such asσ₈cause negligible error changes, suggesting galaxies shift along the manifold. These findings provide direct evidence that the ability to infer Ω_mfrom individual galaxies is tied to the way Ω_mshifts the manifold. Physically, this implies that parameters likeσ₈produce galaxy property changes resembling natural scatter, while parameters like Ω_mandA_SN1create unsampled properties, extending beyond the natural scatter in the fiducial model. 

Abstract We present a new suite of numerical simulations of the star-forming interstellar medium (ISM) in galactic disks using the TIGRESS-NCR framework. Distinctive aspects of our simulation suite are (1) sophisticated and comprehensive numerical treatments of essential physical processes including magnetohydrodynamics, self-gravity, and galactic differential rotation, as well as photochemistry, cooling, and heating coupled with direct ray-tracing UV radiation transfer and resolved supernova feedback and (2) wide parameter coverage including the variation in metallicity over $Z\prime \equiv Z/Z_{\odot }\sim 0.1-3$, gas surface density Σ_gas∼ 5–150M_⊙pc⁻², and stellar surface density Σ_star∼ 1–50M_⊙pc⁻². The range of emergent star formation rate surface density is Σ_SFR∼ 10⁻⁴–0.5M_⊙kpc⁻²yr⁻¹, and ISM total midplane pressure isP_tot/k_B= 10³–10⁶cm⁻³K, withP_totequal to the ISM weight $\mathcal{W}$. For given Σ_gasand Σ_star, we find ${\mathrm{\Sigma }}_{\mathrm{SFR}}\propto Z{\prime }^{0.3}$. We provide an interpretation based on the pressure-regulated feedback-modulated (PRFM) star formation theory. The total midplane pressure consists of thermal, turbulent, and magnetic stresses. We characterize feedback modulation in terms of the yield ϒ, defined as the ratio of each stress to Σ_SFR. The thermal feedback yield varies sensitively with both weight and metallicity as ${\mathrm{\Upsilon }}_{\mathrm{th}}\propto {\mathcal{W}}^{-0.46}Z{\prime }^{-0.53}$, while the combined turbulent and magnetic feedback yield shows weaker dependence ${\mathrm{\Upsilon }}_{\mathrm{turb}+\mathrm{mag}}\propto {\mathcal{W}}^{-0.22}Z{\prime }^{-0.18}$. The reduction in Σ_SFRat low metallicity is due mainly to enhanced thermal feedback yield, resulting from reduced attenuation of UV radiation. With the metallicity-dependent calibrations we provide, PRFM theory can be used for a new subgrid star formation prescription in cosmological simulations where the ISM is unresolved. 

This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology. The pipeline includes software for implementing various neural architectures, training schema, priors, and density estimators in a manner easily adaptable to any research workflow. It includes comprehensive validation metrics to assess posterior estimate coverage, enhancing the reliability of inferred results. Additionally, the pipeline is easily parallelizable, designed for efficient exploration of modeling hyperparameters. To demonstrate its capabilities, we present real applications across a range of astrophysics and cosmology problems, such as: estimating galaxy cluster masses from X-ray photometry; inferring cosmology from matter power spectra and halo point clouds; characterising progenitors in gravitational wave signals; capturing physical dust parameters from galaxy colors and luminosities; and establishing properties of semi-analytic models of galaxy formation. We also include exhaustive benchmarking and comparisons of all implemented methods as well as discussions about the challenges and pitfalls of ML inference in astronomical sciences. All code and examples are made publicly available at https://github.com/maho3/ltu-ili.