Over a Hubble time, a star-forming spiral galaxy like the Milky Way will inject a population of cosmic rays into its low-density circum-galactic medium whose total energy content is comparable to the gravitational binding energy of CGM gas to the host dark matter halo. What happens to those cosmic rays and what, if anything, do they do?
I will discuss the implications of our modelling of this cosmic ray population. This modelling shows that we can neither blithely ignore the hadronic gamma-ray emission from these particles nor their potential dynamical implications. In particular, cosmic rays in the CGM gas of external galaxies contribute sub-dominantly but non-negligibly to the unresolved, isotropic gamma-ray flux measured by Fermi. I will also show that, over much of the relevant parameter space, CRs accumulated into the Milky Way's own CGM make a non-negligible contribution to our Fermi-band gamma-ray sky and that these CRs are responsible for providing a significant fraction of the pressure (gradient) that supports the Milky Way’s CGM in quasi-hydrostatic equilibrium. Furthermore, streaming losses by these CRs can also contribute sub-dominantly to heating the CGM gas. On the other hand, towards higher mass dark matter halos, star-formation cosmic rays are not dynamically important.
When zooming in on the fabric of spacetime, quantum effects become important. Recent theoretical results strongly suggest that spacetime is a form of quantum entanglement. However, there are many open questions about how to reconstruct spacetime from quantum entanglement. In this seminar, we discuss possibilities to experimentally test the relation between spacetime and entanglement.
Biomolecules are incredibly dynamic, constantly shifting between various conformations within a network connected by infrequent structural intermediates. This collection of structures, known as their conformational ensemble, including these rare intermediate structures, dictates how biomolecules function within a cell. However, comprehensively mapping these ensembles remains a significant challenge for both computational and experimental methods. Computer simulations, enhanced by machine learning, offer a promising solution to these challenges in biomolecular sciences.
In the first part of my talk, I'll showcase our work on integrating path sampling with machine learning. This empowers us to simulate rare conformational transitions more effectively. Our algorithm provides efficient sampling and delivers crucial mechanistic, thermodynamic, and kinetic insights into these rare molecular events, all at a moderate computational cost.
The second part of my talk will focus on using simulation-based inference to identify biomolecular conformations in cryo-electron microscopy (cryo-EM) data. Cryo-EM is a powerful tool for characterizing protein conformational ensembles. Even though a frozen sample contains information about the entire ensemble, accurately identifying rare or disordered molecular conformations from a single cryo-EM image is still difficult. To address this, we developed the cryo-EM simulation-based inference (cryoSBI) framework by integrating physics-based simulations, Bayesian inference, and deep learning. This framework allows us to infer molecular conformations and their associated uncertainties directly from individual cryo-EM images. We've validated cryoSBI using both synthetic and experimental data. This approach opens new avenues for characterizing entire conformational ensembles using experimental data.
Images of black holes, like those first provided by the Event Horizon
Telescope, can reveal how black holes interact with their environment and test theories of gravity. I will review early imaging results and describe what progress can be expected in the near future. Numerical models are essential to understanding black hole images. I will describe what current models predict about the asymmetry of the characteristic ring seen in Event Horizon Telescope images and how that is related to the magnitude and direction of black hole spin.
