Abstract: Over the last decade, several exciting directions have been initiated by laser-driven plasmas, e.g. compact particle accelerators, inertial fusion and laboratory astrophysics. The first has known rapid progress, in terms of current, energy, stability; fusion has gone through a historic step, with the news of ignition being achieved at NIF in 2022; and laboratory astrophysics has known also spectacular developments, demonstrating the possibility to perform fully scalable experiments relevant to various objects such as forming stars and supernovae. A particularly interesting aspect is that all these fields are strongly synergistic, i.e. that advances in one can push the others as well. I will present examples of such synergies, through recent results we have obtained in all these domains, and in particular how ultra-bright neutron beams can be developed using latest generation multi-PW lasers. These could open up interesting perspectives in terms of cargo inspection, but also for fusion plasma measurements. I will also show how fusion can benefit from external magnetization. Finally I will discuss advances in laboratory astrophysics, particularly the first-stage acceleration of ions leading to cosmic rays, understanding the universal nature of collimated outflows in the Universe, and probing the intricacy of 3D magnetic reconnection.

Abstract: In this talk, I will present our recent results on the statistical properties of the non-hierarchical planar three-body problem, relevant to astrophysical systems with nearly planar dynamics. Using the Flux-based statistical theory of the three-body problem, previously studied in three dimensions, we derive theoretical predictions for several statistical observables in the planar case. We compare these predictions directly with numerical simulations and contrast the results with the corresponding unconstrained three-dimensional case. Our analysis is based on one million simulations for each of eight distinct mass sets. We examine escape probabilities, properties of marginal escape events, eccentricity distributions, lifetime distributions, and relative prevalence of prograde and retrograde escapes. While several of the theoretical predictions are in good agreement with the simulations, others exhibit unexpected discrepancies.

Abstract: TBA

Abstract: Natural selection favors traits that improve fitness: the ability to survive and reproduce. But how many traits matter? We show that recovery after catastrophe can reveal the answer. When selection acts on abundant existing variation in a population, the gap between average and highest fitness shrinks inversely with time. Interestingly, the prefactor of this decay is proportional to the number of traits under selection. This law is a dynamical analog of the equipartition principle in statistical mechanics, with time playing the role of inverse temperature and the relevant trait axes acting as effective degrees of freedom. It therefore allows us to count these axes in two large experimental fitness landscapes measured in E. coli under antibiotic stress. We further show that, rather than following gradient ascent on a fitness landscape, adaptation follows a common optimization algorithm, the Levenberg-Marquardt method. Near fitness peaks, the adaptation trajectories become increasingly biased against greediness: from an optimization perspective, post-catastrophic selection becomes progressively optimistic over time. The pace and path of recovery from catastrophe thus quantitatively probe the underlying fitness landscape geometry.