Compact expansion of a repulsive suspension

Short-range repulsion governs the dynamic behavior of matter across length scales, from atoms to animals. As the density increases, the dynamics transition from nearest-neighbor to many-body interactions, posing a challenge for an analytical description. Here we use theory, simulations, and experiments to show that a suspension of particles with short-range repulsion spreads compactly. Unlike the diffusive boundary of a spreading drop of Brownian particles, a compact expansion is characterized by a density profile that is strictly zero beyond a cutoff distance. Starting from the microscopic interactions, we derive an effective, non-linear diffusion equation and find that the dynamics exhibit two distinct transitions: (1) when very dense, particle-particle interactions extend beyond nearest neighbors, and the ensemble grows in a self-similar fashion as time to the power of 1/4. (2) at lower densities, nearest-neighbor interactions dominate, and the expansion slows to logarithmic growth. We examine the second regime experimentally by monitoring the expansion of a dense suspension of charge-stabilized colloids. Using simulations of thousands of particles, we observe the continuous crossover between the self-similar and the logarithmic dynamics. Our results are general and robust, with practical implications in engineering and pharmaceutical industries, where suspensions must operate at extreme densities

Correlated dynamics of inclusions in a supported membrane

The hydrodynamic theory of heterogeneous fluid membranes is extended to the case of a membrane adjacent to a solid substrate. We derive the coupling diffusion coefficients of pairs of membrane inclusions in the limit of large separation compared to the inclusion size. Two-dimensional compressive stresses in the membrane make the coupling coefficients decay asymptotically as $1/r^2$ with interparticle distance $r$. For the common case, where the distance to the substrate is of sub-micron scale, we present expressions for the coupling between distant disklike inclusions, which are valid for arbitrary inclusion size. We calculate the effect of inclusions on the response of the membrane and the associated corrections to the coupling diffusion coefficients to leading order in the concentration of inclusions. While at short distances the response is modified as if the membrane were a two-dimensional suspension, the large-distance response is not renormalized by the inclusions.Comment: 15 page

An Observationally Driven Multifield Approach for Probing the Circum-Galactic Medium with Convolutional Neural Networks

The circum-galactic medium (CGM) can feasibly be mapped by multiwavelength surveys covering broad swaths of the sky. With multiple large datasets becoming available in the near future, we develop a likelihood-free Deep Learning technique using convolutional neural networks (CNNs) to infer broad-scale physical properties of a galaxy's CGM and its halo mass for the first time. Using CAMELS (Cosmology and Astrophysics with MachinE Learning Simulations) data, including IllustrisTNG, SIMBA, and Astrid models, we train CNNs on Soft X-ray and 21-cm (HI) radio 2D maps to trace hot and cool gas, respectively, around galaxies, groups, and clusters. Our CNNs offer the unique ability to train and test on ''multifield'' datasets comprised of both HI and X-ray maps, providing complementary information about physical CGM properties and improved inferences. Applying eRASS:4 survey limits shows that X-ray is not powerful enough to infer individual halos with masses $\log(M_{\rm{halo}}/M_{\odot}) < 12.5$. The multifield improves the inference for all halo masses. Generally, the CNN trained and tested on Astrid (SIMBA) can most (least) accurately infer CGM properties. Cross-simulation analysis -- training on one galaxy formation model and testing on another -- highlights the challenges of developing CNNs trained on a single model to marginalize over astrophysical uncertainties and perform robust inferences on real data. The next crucial step in improving the resulting inferences on physical CGM properties hinges on our ability to interpret these deep-learning models

Dynamics of membranes with immobile inclusions

Cell membranes are anchored to the cytoskeleton via immobile inclusions. We investigate the effect of such anchors on the in-plane dynamics of a fluid membrane and mobile inclusions (proteins) embedded in it. The immobile particles lead to a decreased diffusion coefficient of mobile ones and suppress the correlated diffusion of particle pairs. Due to the long-range, quasi-two-dimensional nature of membrane flows, these effects become significant at a low area fraction (below one percent) of immobile inclusions.Comment: 5 page

Correlated diffusion of membrane proteins and their effect on membrane viscosity

We extend the Saffman theory of membrane hydrodynamics to account for the correlated motion of membrane proteins, along with the effect of protein concentration on that correlation and on the response of the membrane to stresses. Expressions for the coupling diffusion coefficients of protein pairs and their concentration dependence are derived in the limit of small protein size relative to the inter-protein separation. The additional role of membrane viscosity as determining the characteristic length scale for membrane response leads to unusual concentration effects at large separation -- the transverse coupling increases with protein concentration, whereas the longitudinal one becomes concentration-independent.Comment: 13 pages, 2 figure