Tracing planet-induced structures in circumstellar disks using molecular lines

Circumstellar disks are considered to be the birthplace of planets. Specific structures like spiral arms, gaps, and cavities are characteristic indicators of planet-disk interaction. Investigating these structures can provide insights into the growth of protoplanets and the physical properties of the disk. We investigate the feasibility of using molecular lines to trace planet-induced structures in circumstellar disks. Based on 3D hydrodynamic simulations of planet-disk interactions, we perform self-consistent temperature calculations and produce N-LTE molecular line velocity-channel maps and spectra of these disks using our new N-LTE line radiative transfer code Mol3D. Subsequently, we simulate ALMA observations using the CASA simulator. We consider two nearly face-on inclinations, 5 disk masses, 7 disk radii, and 2 different typical pre-main-sequence host stars (T Tauri, Herbig Ae). We calculate up to 141 individual velocity-channel maps for five molecules/isotopoloques in a total of 32 rotational transitions to investigate the frequency dependence of the structures indicated above. We find that the majority of protoplanetary disks in our parameter space could be detected in the molecular lines considered. However, unlike the continuum case, gap detection is not straightforward in lines. For example, gaps are not seen in symmetric rings but are masked by the pattern caused by the global (Keplerian) velocity field. We identify specific regions in the velocity-channel maps that are characteristic of planet-induced structures. Simulations of high angular resolution molecular line observations demonstrate the potential of ALMA to provide complementary information about the planet-disk interaction as compared to continuum observations. In particular, the detection of planet-induced gaps is possible under certain conditions.(abridged)Comment: 19 pages, 19 figures, accepted for publication in A&

On Primal-Dual Approach for Distributed Stochastic Convex Optimization over Networks

We introduce a primal-dual stochastic gradient oracle method for distributed convex optimization problems over networks. We show that the proposed method is optimal in terms of communication steps. Additionally, we propose a new analysis method for the rate of convergence in terms of duality gap and probability of large deviations. This analysis is based on a new technique that allows to bound the distance between the iteration sequence and the optimal point. By the proper choice of batch size, we can guarantee that this distance equals (up to a constant) to the distance between the starting point and the solution

Accelerating Incremental Gradient Optimization with Curvature Information

This paper studies an acceleration technique for incremental aggregated gradient ({\sf IAG}) method through the use of \emph{curvature} information for solving strongly convex finite sum optimization problems. These optimization problems of interest arise in large-scale learning applications. Our technique utilizes a curvature-aided gradient tracking step to produce accurate gradient estimates incrementally using Hessian information. We propose and analyze two methods utilizing the new technique, the curvature-aided IAG ({\sf CIAG}) method and the accelerated CIAG ({\sf A-CIAG}) method, which are analogous to gradient method and Nesterov's accelerated gradient method, respectively. Setting $\kappa$ to be the condition number of the objective function, we prove the $R$ linear convergence rates of $1 - \frac{4c_0 \kappa}{(\kappa+1)^2}$ for the {\sf CIAG} method, and $1 - \sqrt{\frac{c_1}{2\kappa}}$ for the {\sf A-CIAG} method, where $c_0,c_1 \leq 1$ are constants inversely proportional to the distance between the initial point and the optimal solution. When the initial iterate is close to the optimal solution, the $R$ linear convergence rates match with the gradient and accelerated gradient method, albeit {\sf CIAG} and {\sf A-CIAG} operate in an incremental setting with strictly lower computation complexity. Numerical experiments confirm our findings. The source codes used for this paper can be found on \url{http://github.com/hoitowai/ciag/}.Comment: 22 pages, 3 figures, 3 tables. Accepted by Computational Optimization and Applications, to appea

Fast Convergence Rates for Distributed Non-Bayesian Learning

We consider the problem of distributed learning, where a network of agents collectively aim to agree on a hypothesis that best explains a set of distributed observations of conditionally independent random processes. We propose a distributed algorithm and establish consistency, as well as a non-asymptotic, explicit and geometric convergence rate for the concentration of the beliefs around the set of optimal hypotheses. Additionally, if the agents interact over static networks, we provide an improved learning protocol with better scalability with respect to the number of nodes in the network