Shy Genel

We perform field-level likelihood-free inference of the matter density parameter Ω _m from simulated galaxy catalogs using machine learning models with differing inductive biases. Using hydrodynamic simulations from CAMELS, we examine how observable choice and architecture govern cosmological information extraction. We consider galaxy positions and line-of-sight peculiar velocities, separately and jointly, and compare permutation-invariant deep sets, implemented with either multilayer perceptrons (MLPs) or Kolmogorov–Arnold networks (KANs), to graph neural networks (GNNs), which explicitly encode spatial relations. We test in-distribution and out-of-distribution (OOD) performance across simulations with different subgrid galaxy-formation prescriptions. Deep sets infer Ω _m from velocities alone with mean relative errors of approximately 18% in-distribution and ∼25% OOD, with KANs and MLPs achieving comparable performance. In contrast, the same set-based approach does not yield useful σ ₈ predictions in either in-distribution or cross-suite tests. Adding positions does not improve deep sets, while GNNs infer Ω _m with mean relative errors of about 10% in-distribution and 10%–17% OOD. These results indicate that peculiar velocities provide the dominant source of Ω _m information for set-based models in this setting, while spatial information is most effectively used by architectures that explicitly encode galaxy–galaxy relations. Because the velocity inputs are exact simulated peculiar velocities, applications to survey data will require validation under realistic velocity-measurement noise, selection effects, and survey geometry.

Learning cosmology from galaxy surveys requires large suites of simulations spanning the cosmological and astrophysical parameter space, yet hydrodynamical simulations of galaxy formation remain prohibitively expensive. Semianalytic models offer an inexpensive, physically grounded alternative, but still require halo merger trees from N -body simulations, and densely sampling cosmological parameters in sufficient volume remains expensive. We address this by extending cosmological rescaling to operate directly on merger trees and applying it in the Ω _m – σ ₈ plane, running the Santa Cruz semianalytic model for galaxy formation on the rescaled trees to produce galaxy populations across new cosmological and astrophysical parameters at negligible additional cost. A novel halo-profile-based correction, controlled by a single free parameter, suppresses systematic bias in rescaled halo masses to below the percent level. We apply the method to parameter estimation of Ω _m and σ ₈ given either the stellar mass function or the two-point correlation function, finding that as few as 64, and potentially fewer, base N -body simulations, rescaled to ∼1000 training samples, match the accuracy of 750 dedicated N -body simulations; rescaling to 3200 realizations improves the prediction of Ω _m by ∼25%. Rescaling all merger trees from a single CAMELS-SAM N -body simulation costs ∼0.1 CPUh, compared to several thousand CPUh to run the simulation itself. We demonstrate a practical route to obtaining predictions of galaxy summary statistics across cosmological and astrophysical parameters, even with a relatively small number of base N -body simulations.

The circumgalactic medium (CGM) is known to contain multiphase gas in various stages of evolution and interaction with the galaxy. In order to characterize its detailed behavior on short timescales, we use a subregion of the TNG100 cosmological simulation to study the evolution of the z = 1 CGM around six galaxies in 10 ^11.5 –10 ¹² M _⊙ halos at a high time cadence of ≈2 Myr. We use Monte Carlo tracer particles to follow this CGM gas forward in time in a Lagrangian way and determine how its thermodynamic and kinematic properties change. We find that CGM gas mixes between different temperature and density phases quickly and within ≈500 Myr evolves into distinct cold ( T ≈ 10 ⁴ K) and warm–hot ( T ≈ 10 ^5.5 K) phases at small and large distances from the galaxy, respectively, regardless of its initial ( z = 1) halo-centric radius. This is largely driven by feedback from the galaxy, which heats and ejects cold gas that had previously cooled and accreted toward and occasionally into the galaxy from the outer CGM. We see signatures of this process in autocorrelations of kinematic quantities, which take ≈400 Myr to fully decorrelate from their initial values, suggesting a timescale over which feedback disrupts and reprocesses CGM gas. We also examine gas in narrow temperature and density ranges associated with commonly observed ions and find that gas that is O vi –like stays in its phase for hundreds of Myr longer than gas that is Mg ii –like or C iv –like, suggesting that CGM observations of different species could probe gas in different evolutionary states, even if the gas is cospatial.

We measure and analyze the inflows and outflows of mass, energy, and metals through the interstellar medium (ISM) and circumgalactic medium (CGM) of galaxies in the TNG100 simulation. We identify the dominant feedback mechanism in bins of halo virial mass and redshift by computing the integrated energy input from supernovae (SNe) and the “kinetic” and “thermal” modes of active galactic nucleus (AGN) feedback. We measure all quantities in a shell at the virial radius (“halo scale”) and one chosen to be approximately at the interface of the CGM and the ISM (“ISM scale”). At z ≳ 2, we find that galaxies have strong net positive inflows on halo scales, and weaker but still net positive inflows on ISM scales. At later times, partially due to the onset of kinetic AGN feedback in massive halos, inflows and outflows nearly balance one another, leading to the familiar effects of the slowdown of galaxy growth and the onset of quenching. Halos dominated by SN feedback show only weak evidence of preventative feedback on halo scales, and we see excess ISM-scale accretion indicative of rapid gas recycling. Wind mass loadings decrease with increasing halo mass, and with increasing redshift, while energy loadings are nearly independent of both mass and redshift. The detailed measurements of these mass, metal, and energy inflow and outflow rates on galaxy and halo scales can be used to guide empirical and semianalytic models, and provide deeper insight into how galaxy growth and quenching are regulated in the IllustrisTNG simulations.

In this study, we investigate the impact of covariance within uncertainties on the inference of cosmological and astrophysical parameters, specifically focusing on galaxy stellar mass functions derived from the CAMELS simulation suite. Utilizing both Fisher analysis and implicit likelihood inference, we explore how different covariance structures, including simple toy models and physics-motivated uncertainties, affect posterior distributions and parameter variances. Our methodology utilizes forward modeling via emulators that are trained on CAMELS simulations to produce stellar mass functions based on input parameters, subsequently incorporating Gaussian noise as defined by covariance matrices. We examine both toy model covariance matrices and physically motivated covariance matrices derived from observational factors like the stellar initial mass function and photometric aperture size. Our results demonstrate that covariance terms significantly influence parameter inference, often leading to tighter constraints or revealing complex, multimodal posterior distributions. These findings underscore the necessity of accounting for covariance when interpreting astrophysical observations, especially in fields where accurate parameter estimation is critical for model validation and hypothesis testing.

Accurate and parsimonious quantification of baryonic feedback on matter distribution is of crucial importance for understanding both cosmology and galaxy formation from observational data. This is, however, challenging given the large discrepancy among different models of galaxy formation simulations and their distinct subgrid physics parameterizations. Using 5072 simulations from four different models covering broad ranges in their parameter spaces, we find a unified 2D latent representation. Compared to the simulations and other phenomenological models, our representation is independent of both time and cosmology, much lower dimensional, and disentangled in its impacts on the matter power spectra. The common latent space facilitates the comparison of parameter spaces of different models and is readily interpretable by correlation with each. The two latent dimensions provide a complementary representation of baryonic effects, linking black hole and supernova feedback to distinct and interpretable impacts on both the matter power spectrum and field level. Our approach enables developing robust and economical analytic models for optimal gain of physical information from data and is generalizable to other fields with significant modeling uncertainty.

The next generation of weak-gravitational-lensing surveys has the potential to place stringent constraints on cosmological parameters. However, their analysis is limited by systematics such as the intrinsic alignments of galaxies, which alter weak-lensing convergence and can lead to biases in cosmological parameter estimations. For the first time, in this work, we investigate the impact of intrinsic alignments on non-Gaussian statistics of the weak-lensing field using galaxy shapes derived from the IllustrisTNG hydrodynamical simulation. We create two catalogs of ray-traced convergence maps: one that includes the measured intrinsic shape of each galaxy and another where all galaxies are randomly rotated to eliminate intrinsic alignments. We compare a range of weak-lensing statistics between the two catalogs, including the shear–shear correlation function, the map-level angular power spectrum, one-point, peak count, and minimum distribution functions, and Minkowski functionals. For each statistic, we assess the level of statistical distinguishability between catalogs for a set of future survey angular areas. Our results reveal strong small-scale correlation in the alignment of galaxies and statistically significant boosts in weak-lensing convergence in both positive and negative directions for high-significance peaks and minima, respectively. We note that our analysis is at a fixed number density of ∼ 5 arcmin − 2 , drawn from a single realization of initial conditions, and does not include observational uncertainties or supersample covariance contributions. Weak-lensing analyses utilizing non-Gaussian statistics must account for intrinsic alignments to avoid significantly compromised cosmological inferences.

Mergers are believed to play a pivotal role in galaxy evolution, and measuring the galaxy merger fraction is a longstanding goal of both observational and theoretical studies. In this work, we extend the consideration of the merger fraction from the standard measure of binary mergers, namely those comprising two merging galaxies, to multiple mergers, namely mergers involving three or more galaxies. We use the Illustris and IllustrisTNG cosmological hydrodynamical simulations to provide a theoretical prediction for the fraction of galaxy systems that are involved in a multiple merger as a function of various parameters, with a focus on the relationship between the multiple merger fraction f _m and the total merger fraction f _t . We generally find that binary mergers dominate the total fraction and that f m ≈ ( 0.5 − 0.7 ) f t 5 / 3 , a prediction that can be tested observationally. We further compare the empirical simulation results with toy models where mergers occur, on the evolution timeline of a galaxy, either at constant intervals or as a Poisson process at a constant rate. From these comparisons, where the toy models typically produce lower multiple merger fractions, we conclude that in cosmological simulations, mergers are more strongly clustered in time than in these toy scenarios, likely reflecting the hierarchical nature of cosmological structure formation.

Star formation in galaxies is regulated by the interplay of a range of processes that shape the multiphase gas in the interstellar and circumgalactic media. Using the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) suite of cosmological simulations, we study the effects of varying feedback and cosmology on the average star formation histories (SFHs) of galaxies at z ∼ 0 across the IllustrisTNG, SIMBA, and ASTRID galaxy formation models. We find that galaxy SFHs in all three models are sensitive to changes in stellar feedback, which affect the efficiency of baryon cycling and the rates at which central black holes grow, whereas the effects of varying active galactic nucleus (AGN) feedback depend on model-specific implementations of black hole seeding, accretion, and feedback. We also find strong interaction terms that couple stellar and AGN feedback, usually by regulating the amount of gas available for the central black hole to accrete. Using a double power law to describe the average SFHs, we derive a general set of equations relating the shape of the SFHs to physical quantities like baryon fraction and black hole mass across all three models. We find that a single set of equations (albeit with different coefficients) can describe the SFHs across all three CAMELS models, with cosmology dominating the SFH at early times, followed by halo accretion, and feedback and baryon cycling at late times. Galaxy SFHs provide a novel, complementary probe to constrain cosmology and feedback, and can connect the observational constraints from current and upcoming galaxy surveys with the physical mechanisms responsible for regulating galaxy growth and quenching.