Search for: All records

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 Mhalo/Me  1013, 10 r kpc−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. 

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. 

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 between z = 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. 

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. 

ABSTRACT The warm-hot intergalactic medium (WHIM) contains a significant portion of the ‘missing baryons’. Its detection in emission remains a challenge. Integral field spectrometers like X-IFU on board of the Athena satellite will secure WHIM detection in absorption and emission and, for the first time, allow us to investigate its physical properties. In our research, we use the CAMELS simulations to model the surface brightness maps of the OVII and OVIII ion lines and compute summary statistics like photon counts and 2-point correlation functions to infer the properties of the WHIM. Our findings confirm that detectable WHIM emission is primarily associated with galaxy haloes, and the properties of the WHIM show minimal evolution from z ∼ 0.5 to the present time. By exploring a wide range of parameters within the CAMELS suite, we investigate the sensitivity of WHIM properties to cosmology and energy feedback mechanisms influenced by active galactic nuclei and stellar activity. This approach allows us to separate the cosmological aspects from the baryonic processes and place constraints on the latter. Additionally, we provide forecasts for WHIM observations using a spectrometer similar to X-IFU. We anticipate detecting 1–3 WHIM emission lines per pixel and mapping the WHIM emission profile around haloes up to a few tens of arcminutes, surpassing the typical size of a WHIM emitter. Overall, our work demonstrates the potential of emission studies to probe the densest phase of the WHIM, shedding light on its physical properties and offering insights into the cosmological and baryonic processes at play.