Kinematics of the Outer Stellar Halo

We have tested whether the simple model for the kinematics of the Galactic stellar halo (in particular the outer halo) proposed by Sommer-Larsen, Flynn and Christensen (SLFC) is physically realizable, by directly integrating particles in a 3-D model of the Galactic potential. We are able to show that the SLFC solution can be realized in terms of a distribution of particles with stationary statistical properties in phase-space. Hence, the SLFC model, which shows a notable change in the anisotropy from markedly radial at the sun to markedly tangential beyond about Galactocentric radius r=20 kpc, seems a tenable description of outer halo kinematics.Comment: 13 pages, 6 figures, accepted for publication in MNRAS. also available at http://astro.utu.fi/~cflynn/papers/fslc4/fslc4.htm

Current-induced forces and hot-spots in biased nano-junctions

We investigate theoretically the interplay of current-induced forces (CIF), Joule heating, and heat transport inside a current-carrying nano-conductor. We find that the CIF, due to the electron-phonon coherence, can control the spatial heat dissipation in the conductor. This yields a significant asymmetric concentration of excess heating (hot-spot) even for a symmetric conductor. When coupled to the electrode phonons, CIF drive different phonon heat flux into the two electrodes. First-principles calculations on realistic biased nano-junctions illustrate the importance of the effect.Comment: Phys. Rev. Lett. accepted versio

perms: Marginal likelihood estimation for binary Bayesian nonparametric models in Python and R

Binary responses arise in a multitude of statistical problems, including binary classification, bioassay, current status data problems and sensitivity estimation. There has been an interest in such problems in the Bayesian nonparametrics community since the early 1970s, but inference given binary data is intractable for a wide range of modern simulation-based models, even when employing MCMC methods. Recently, Christensen (2023) introduced a novel simulation technique based on counting permutations, which can estimate both posterior distributions and marginal likelihoods for any model from which a random sample can be generated. However, the accompanying implementation of this technique struggles when the sample size is too large (n > 250). Here we present perms, a new implementation of said technique which is substantially faster and able to handle larger data problems than the original implementation. It is available both as an R package and a Python library. The basic usage of perms is illustrated via two simple examples: a tractable toy problem and a bioassay problem. A more complex example involving changepoint analysis is also considered. We also cover the details of the implementation and illustrate the computational speed gain of perms via a simple simulation study