| Thursday, December 12, 2024 |
| Fast radio bursts, monster shocks and chaos | |
| Amir Levinson, Tel Aviv University | |
| Event Type: Informal Astro Talk | |
| Time: 2:00 PM - 3:00 PM | |
| Location: 726 Broadway, 901, Sm Conf | |
| Abstract: It has been proposed recently that the breaking of MHD waves in the inner magnetosphere of strongly magnetized neutron stars can power different types of high-energy transients. In the first part of this talk, I’ll present recent results of PIC simulations of monster shocks that, for the first time, reveal the generation of a high-frequency precursor wave at the foot of the shock, carrying a fraction of 0.001 of the total energy dissipated in the shock. The spectrum of the precursor wave exhibits several sharp harmonic peaks, with frequencies in the GHz band under conditions anticipated in magnetars. Such signals may appear as fast radio bursts. In the second part, I’ll describe an analytic solution that reproduces the results of our PIC simulations, as well as other simulations of magnetized shocks, and show that dissipation in such shocks does not require collective plasma effects. Rather, dissipation and thermalization in such shocks relies on the onset of chaos in orbital dynamics within quasiperiodic solitary structures, as quantified by the spectrum of Lyapunov exponents. | |
| Hogg/Blanton | |
| Event Type: Astro Research Group | |
| Time: 2:00 PM - 3:45 PM | |
| Location: 726 Broadway, 940, CCPP Seminar | |
| Pullen Group Meeting | |
| Event Type: Pullen Group Meeting | |
| Time: 3:00 PM - 4:00 PM | |
| Location: 726 Broadway, 901, Sm Conf | |
| Learning and Active Mechanics | |
| Vincenzo Vitelli, University of Chicago | |
| Event Type: Physics Dept Colloquium | |
| Time: 4:00 PM - 5:30 PM | |
| Location: 726 Broadway, 940, CCPP Seminar | |
| Abstract: Physical learning is an emerging paradigm whereby materials acquire behaviors by exposure to examples. So far, it has been applied to static properties encoded in energy minima. In this talk, we extend it to dynamic functionalities, such as motion and shape change. Using a generalized Hopfield model, we delineate the key physical ingredients needed and illustrate them with LEGO toys as well as potential active matter platforms based on oil droplets with chemotactic signaling that learn life-like functionalities. Next, we turn to investigate how living organisms themselves exploit active mechanics to change their shape. Using machine learning, we infer an interpretable model of morphogenesis in Drosophila embryos that captures how tissue flow is regulated by protein dynamics and validate it with a mutant analysis. This data driven model taken together with experiments on human stem cells suggest that our machine-learned mechanism for early neuroectoderm morphogenesis is conserved across species. | |