Search for: All records

Abstract The circumgalactic medium (CGM) around massive galaxies plays a crucial role in regulating star formation and feedback. Using the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) suite, we develop emulators for the X-ray surface brightness profile and the X-ray luminosity–stellar mass scaling relation, to investigate how stellar and active galactic nucleus (AGN) feedback shape the X-ray properties of the hot CGM. Our analysis shows that at CGM scales (10¹²≲M_halo/M_⊙≲ 10¹³, 10 ≲rkpc⁻¹≲ 400), stellar feedback more significantly impacts the X-ray properties than AGN feedback within the parameters studied. Comparing the emulators to recent eROSITA All Sky Survey (eRASS) observations, it is found that stronger feedback than is currently implemented in the IllustrisTNG, SIMBA, and Astrid simulations is required to match the observed CGM properties. However, adopting these enhanced feedback parameters causes deviations in the stellar mass–halo mass relations from observational constraints below the group-mass scale. This tension suggests possible unaccounted-for systematics in X-ray CGM observations or inadequacies in the feedback models of cosmological simulations. 

Abstract The baryonic physics shaping galaxy formation and evolution are complex, spanning a vast range of scales and making them challenging to model. Cosmological simulations rely on subgrid models that produce significantly different predictions. Understanding how models of stellar and active galactic nucleus (AGN) feedback affect baryon behavior across different halo masses and redshifts is essential. Using the SIMBA and IllustrisTNG suites from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, we explore the effect of parameters governing the subgrid implementation of stellar and AGN feedback. We find that while IllustrisTNG shows higher cumulative feedback energy across all halos, SIMBA demonstrates a greater spread of baryons, quantified by the closure radius and circumgalactic medium (CGM) gas fraction. This suggests that feedback in SIMBA couples more effectively to baryons and drives them more efficiently within the host halo. There is evidence that the different feedback modes are highly interrelated in these subgrid models. The parameters controlling the stellar feedback efficiency significantly impact AGN feedback, as seen in the suppression of black hole mass growth and delayed activation of AGN feedback to higher-mass halos with increasing stellar feedback efficiency in both simulations. Additionally, the AGN feedback efficiency parameters affect the CGM gas fraction at low halo masses in SIMBA, hinting at complex, nonlinear interactions between the AGN and supernova feedback modes. Overall, we demonstrate that stellar and AGN feedback are intimately interwoven, especially at low redshift, due to subgrid implementation, resulting in halo property effects that might initially seem counterintuitive. 

Abstract Most diffuse baryons, including the circumgalactic medium (CGM) surrounding galaxies and the intergalactic medium (IGM) in the cosmic web, remain unmeasured and unconstrained. Fast radio bursts (FRBs) offer an unparalleled method to measure the electron dispersion measures (DMs) of ionized baryons. Their distribution can resolve the missing baryon problem and constrain the history of feedback theorized to impart significant energy to the CGM and IGM. We analyze the Cosmology and Astrophysics with Machine Learning Simulations using three suites, IllustrisTNG, SIMBA, and Astrid, each varying six parameters (two cosmological and four astrophysical feedback), for a total of 183 distinct simulation models. We find significantly different predictions between the fiducial models of the suites owing to their different implementations of feedback. SIMBA exhibits the strongest feedback, leading to the smoothest distribution of baryons and reducing the sight-line-to-sight-line variance in DMs betweenz= 0 and 1. Astrid has the weakest feedback and the largest variance. We calculate FRB CGM measurements as a function of galaxy impact parameter, with SIMBA showing the weakest DMs due to aggressive active galactic nucleus (AGN) feedback and Astrid the strongest. Within each suite, the largest differences are due to varying AGN feedback. IllustrisTNG shows the most sensitivity to supernova feedback, but this is due to the change in the AGN feedback strengths, demonstrating that black holes, not stars, are most capable of redistributing baryons in the IGM and CGM. We compare our statistics directly to recent observations, paving the way for the use of FRBs to constrain the physics of galaxy formation and evolution. 

ABSTRACT The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large data sets becoming available in the near future, we develop a likelihood-free Deep Learning technique using convolutional neural networks (CNNs) to infer broad-scale physical properties of a galaxy’s CGM and its halo mass for the first time. Using CAMELS (Cosmology and Astrophysics with MachinE Learning Simulations) data, including IllustrisTNG, SIMBA, and Astrid models, we train CNNs on Soft X-ray and 21-cm (H i) radio two-dimensional maps to trace hot and cool gas, respectively, around galaxies, groups, and clusters. Our CNNs offer the unique ability to train and test on ‘multifield’ data sets comprised of both H i and X-ray maps, providing complementary information about physical CGM properties and improved inferences. Applying eRASS:4 survey limits shows that X-ray is not powerful enough to infer individual haloes with masses log (Mhalo/M⊙) < 12.5. The multifield improves the inference for all halo masses. Generally, the CNN trained and tested on Astrid (SIMBA) can most (least) accurately infer CGM properties. Cross-simulation analysis – training on one galaxy formation model and testing on another – highlights the challenges of developing CNNs trained on a single model to marginalize over astrophysical uncertainties and perform robust inferences on real data. The next crucial step in improving the resulting inferences on the physical properties of CGM depends on our ability to interpret these deep-learning models. 

ABSTRACT Known as the ‘Missing Baryon Problem’, about one-third of baryons in the local universe remain unaccounted for. The missing baryons are thought to reside in the warm–hot intergalactic medium (WHIM) of the cosmic web filaments, which are challenging to detect. Recent Chandra X-ray observations used a novel stacking analysis and detected an O vii absorption line towards the sightline of a luminous quasar, hinting that the missing baryons may reside in the WHIM. To explore how the properties of the O vii absorption line depend on feedback physics, we compare the observational results with predictions obtained from the Cosmology and Astrophysics with MachinE Learning (CAMEL) Simulation suite. CAMELS consists of cosmological simulations with state-of-the-art supernova (SN) and active galactic nuclei (AGNs) feedback models from the IllustrisTNG and SIMBA simulations, with varying strengths. We find that the simulated O vii column densities are higher in the outskirts of galaxies than in the large-scale WHIM, but they are consistently lower than those obtained in the Chandra observations, for all feedback runs. We establish that the O vii distribution is primarily sensitive to changes in the SN feedback prescription, whereas changes in the AGN feedback prescription have minimal impact. We also find significant differences in the O vii column densities between the IllustrisTNG and SIMBA runs. We conclude that the tension between the observed and simulated O vii column densities cannot be explained by the wide range of feedback models implemented in CAMELS.