The sizes of mini-voids in the local universe: an argument in favor of a warm dark matter model?

Using high-resolution simulations within the Cold and Warm Dark Matter models we study the evolution of small scale structure in the Local Volume, a sphere of 8 Mpc radius around the Local Group. We compare the observed spectrum of mini-voids in the Local Volume with the spectrum of mini-voids determined from the simulations. We show that the \LWDM model can easily explain both the observed spectrum of mini-voids and the presence of low-mass galaxies observed in the Local Volume, provided that all haloes with circular velocities greater than 20 km/s host galaxies. On the contrary within the LCDM model the distribution of the simulated mini-voids reflects the observed one if haloes with maximal circular velocities larger than 35 km/s host galaxies. This assumption is in contradiction with observations of galaxies with circular velocities as low as 20 km/s in our Local Universe. A potential problem of the LWDM model could be the late formation of the haloes in which the gas can be efficiently photo-evaporated. Thus star formation is suppressed and low-mass haloes might not host any galaxy at all.Comment: 13 pages, 10 figures, version 2, subsection 3.1 added, accepted to MNRA

Kernel-based stochastic collocation for the random two-phase Navier-Stokes equations

In this work, we apply stochastic collocation methods with radial kernel basis functions for an uncertainty quantification of the random incompressible two-phase Navier-Stokes equations. Our approach is non-intrusive and we use the existing fluid dynamics solver NaSt3DGPF to solve the incompressible two-phase Navier-Stokes equation for each given realization. We are able to empirically show that the resulting kernel-based stochastic collocation is highly competitive in this setting and even outperforms some other standard methods

Filtering Random Graph Processes Over Random Time-Varying Graphs

Graph filters play a key role in processing the graph spectra of signals supported on the vertices of a graph. However, despite their widespread use, graph filters have been analyzed only in the deterministic setting, ignoring the impact of stochastic- ity in both the graph topology as well as the signal itself. To bridge this gap, we examine the statistical behavior of the two key filter types, finite impulse response (FIR) and autoregressive moving average (ARMA) graph filters, when operating on random time- varying graph signals (or random graph processes) over random time-varying graphs. Our analysis shows that (i) in expectation, the filters behave as the same deterministic filters operating on a deterministic graph, being the expected graph, having as input signal a deterministic signal, being the expected signal, and (ii) there are meaningful upper bounds for the variance of the filter output. We conclude the paper by proposing two novel ways of exploiting randomness to improve (joint graph-time) noise cancellation, as well as to reduce the computational complexity of graph filtering. As demonstrated by numerical results, these methods outperform the disjoint average and denoise algorithm, and yield a (up to) four times complexity redution, with very little difference from the optimal solution