Freeze-in is a well-established mechanism for the early-universe production
of Dark Matter (DM). In the standard freeze-in scenario, a feeble coupling
is required to reproduce the observed DM abundance. Moreover, it typically
assumes zero or negligible initial DM abundance.In this talk, I will
discuss how the mere existence of gravity can lead to non-negligible
initial abundances. I will show how late-time reheating provides a natural
solution to this issue. An advantage of this solution is that it leads to a
regime of Boltzmann-suppressed production. This allows freeze-in to occur
with stronger couplings, opening a new parameter space for well-studied
models such as those with a Z' mediator. Within this framework, I will show
how current and future direct detection experiments play a crucial role in
constraining these models.
Recent strides in machine learning have shown that computation can be performed by practically any controllable physical system that responds to physical stimuli encoding data [1]. This perspective opens new frontiers for computational approaches using Physical Neural Networks (PNNs) and provides a framework to deepen our understanding of their biological counterparts—neural circuits in living organisms. To fully leverage this potential, PNNs must be trained with a nuanced awareness of the physical nature of signal and noise, where signal is defined relative to the specific computational task. This perspective aligns closely with approaches to determining the fundamental limits to sensing as set by quantum theory but extends these ideas to a new level to encompass broader computational opportunities. I will share insights from our approach to this emerging domain of inquiry and highlight some recent results [2, 3, 4].
Based on work with Fangjun Hu, Saeed A. Khan, Gerasimos Angelatos, Marti Vives, Esin Türeci, Graham E. Rowlands, Guilhem J. Ribeill, Nicholas Bronn.
[1] Aspen Center for Physics Winter Conference, Computing with Physical Systems, https://computingwithphysicalsystems.com/2024/
[2] F. Hu et al. `Tackling Sampling Noise in Physical Systems for Machine Learning Applications: Fundamental Limits and Eigentasks." Phys. Rev. X 13, 041020 (2023).
[3] S. A. Khan et al., `A neural processing approach to quantum state discrimination", arxiv:2409.03748.
[4] F. Hu et al. `Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data", Nature Commun. 15, 7491 (2024).
In recent years, we have seen major progress in the non-equilibrium control of many-body quantum systems. One tool, which has been applied successfully, is Floquet engineering, i.e. the use of strong time-periodic driving for effectively changing the properties of the system. A prominent example is the realization of effective magnetic fields for charge neutral particles (like atoms in optical lattices or photons in superconducting circuits). Another approach is known as reservoir engineering. Here the system is coupled to a controlled environment, which is designed to either cool the system or to stabilize a non-equilibrium steady state of interest. I will report on recent work, where we combine both approaches in open Floquet systems. One motivation is to use dissipation in order to counteract unwanted heating as it necessarily occurs in Floquet engineered systems, e.g. for the preparation of Floquet engineered topological states of matter. Another motivation is the stabilization of non-equilibrium steady states. Here, I will in discuss ordering away from equilibrium, like driving-induced Bose-condensation.
Primordial germ cells of the zebrafish migrate by the formation of blebs, cell membrane protrusions generated by local membrane-cortex detachments. A potential cellular mechanism for blebbing in a controlled site is based on intracellular flows that redistribute the membrane-cortex linker molecule Ezrin. I will present a corresponding model of coupled partial differential equations in the cell plasma and on the cell membrane. The model rests upon intracellular Darcy flow and a diffusion-advection-reaction system, describing the temporal evolution from a homogeneous to a strongly anisotropic Ezrin distribution. A qualitative comparison between simple simulations and experimental observations suggests the viability of the model. Along the talk I will try to give insights into the employed mathematical concepts as well as into what questions are of particular interest from a mathematical perspective.
