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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. 

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 impro v ed inferences. Applying eRASS:4 surv e y limits shows that X-ray is not powerful enough to infer individual haloes with masses log ( M halo /M  ) < 12.5. The multifield impro v es 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. 

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 no v el 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.