Speaker
Description
Cosmological hydrodynamical simulations are able to reproduce many fundamental galaxy properties, but there is no consensus on the impact of 'baryonic feedback' on the matter power spectrum. This uncertainty severely limits the precision of (and potentially biases) small-scale cosmological constraints obtained from weak lensing. It is also possible that baryonic feedback may be more extreme than predicted by the latest simulations, as we found by analysing weak lensing and stacked kSZ measurements. However, the question remains whether such extreme feedback is physically possible. I will present an exploration of a range of empirical AGN feedback models within the FABLE simulation suite, including XFABLE, a novel model with jets thermalising at relatively large cluster-centric distances. I will demonstrate that XFABLE agrees well with the latest constraints of 'extreme feedback', whilst maintaining consistency with observations of galaxy groups/clusters. Finally, to establish stacked kSZ as a benchmark for galaxy formation models, I will describe a comprehensive study of the effect across a number of hydrodynamical simulations. By isolating the effects of different feedback implementations, halo properties, and redshift evolution on the kSZ signal, this work is crucial for interpreting new and future measurements and for building a galaxy formation model that is consistent across a wide range of observables.
| Primary Theme | Small to large scales |
|---|---|
| Secondary Theme | Galactic to intergalactic scales |
| Presenter's Name | Leah Bigwood |
| Presenter's Email Address | [email protected] |
| Recording Permission | YES |
| Virtual Audience Permission | YES |
| Photography Permission | YES |
| If your talk is not accepted for a contributed talk, would you be interested in presenting a fireslide/lightning talk? | Yes |
| If your fireslide/lighting talk is not accepted, would you be interested in presenting a poster? | Yes |
Author
External references
- 25070008
- 389d7e8a-a53e-471f-975c-30ac0af9364a
