Multi-method-modeling of interacting galaxies. I. A unique scenario for NGC 4449?

(abridged) We combined several N-body methods in order to investigate the interaction scenario between NGC 4449 and DDO 125, a close companion in projected space. In a first step fast restricted N-body models are used to confine a region in parameter space reproducing the main observational features. In a second step a genetic algorithm is applied for a uniqueness test of our preferred parameter set. We show that our genetic algorithm reliably recovers orbital parameters, provided that the data are sufficiently accurate, i.e. all the key features are included. In the third step the results of the restricted N-body models are compared with self-consistent N-body simulations. In the case of NGC 4449, the applicability of the simple restricted N-body calculations is demonstrated. Additionally, it is shown that the HI gas can be modeled here by a purely stellar dynamical approach. In a series of simulations, we demonstrate that the observed features of the extended HI disc can be explained by a gravitational interaction between NGC 4449 and DDO 125. According to these calculations the closest approach between both galaxies happened $\sim 4-6 \cdot 10^8$ yr ago at a minimum distance of $\sim 25$ kpc on a parabolic or slightly elliptic orbit. In the case of an encounter scenario, the dynamical mass of DDO 125 should not be smaller than 10% of NGC 4449's mass. Before the encounter, the observed HI gas was arranged in a disc with a radius of 35-40 kpc around the center of NGC 4449. It had the same orientation as the central ellipsoidal HI structure. The origin of this disc is still unclear, but it might have been caused by a previous interaction.Comment: 19 pages with 19 figures, accepted for publication in Astron. & Astrophys., a full PostScript version is available at http://www.astrophysik.uni-kiel.de/pershome/theis/pub.htm

Image registration with sparse approximations in parametric dictionaries

We examine in this paper the problem of image registration from the new perspective where images are given by sparse approximations in parametric dictionaries of geometric functions. We propose a registration algorithm that looks for an estimate of the global transformation between sparse images by examining the set of relative geometrical transformations between the respective features. We propose a theoretical analysis of our registration algorithm and we derive performance guarantees based on two novel important properties of redundant dictionaries, namely the robust linear independence and the transformation inconsistency. We propose several illustrations and insights about the importance of these dictionary properties and show that common properties such as coherence or restricted isometry property fail to provide sufficient information in registration problems. We finally show with illustrative experiments on simple visual objects and handwritten digits images that our algorithm outperforms baseline competitor methods in terms of transformation-invariant distance computation and classification

Bounding right-arm rotation distances

Rotation distance measures the difference in shape between binary trees of the same size by counting the minimum number of rotations needed to transform one tree to the other. We describe several types of rotation distance where restrictions are put on the locations where rotations are permitted, and provide upper bounds on distances between trees with a fixed number of nodes with respect to several families of these restrictions. These bounds are sharp in a certain asymptotic sense and are obtained by relating each restricted rotation distance to the word length of elements of Thompson's group F with respect to different generating sets, including both finite and infinite generating sets.Comment: 30 pages, 11 figures. This revised version corrects some typos and has some clearer proofs of the results for the lower bounds and better figure

Protein Docking by the Underestimation of Free Energy Funnels in the Space of Encounter Complexes

Similarly to protein folding, the association of two proteins is driven by a free energy funnel, determined by favorable interactions in some neighborhood of the native state. We describe a docking method based on stochastic global minimization of funnel-shaped energy functions in the space of rigid body motions (SE(3)) while accounting for flexibility of the interface side chains. The method, called semi-definite programming-based underestimation (SDU), employs a general quadratic function to underestimate a set of local energy minima and uses the resulting underestimator to bias further sampling. While SDU effectively minimizes functions with funnel-shaped basins, its application to docking in the rotational and translational space SE(3) is not straightforward due to the geometry of that space. We introduce a strategy that uses separate independent variables for side-chain optimization, center-to-center distance of the two proteins, and five angular descriptors of the relative orientations of the molecules. The removal of the center-to-center distance turns out to vastly improve the efficiency of the search, because the five-dimensional space now exhibits a well-behaved energy surface suitable for underestimation. This algorithm explores the free energy surface spanned by encounter complexes that correspond to local free energy minima and shows similarity to the model of macromolecular association that proceeds through a series of collisions. Results for standard protein docking benchmarks establish that in this space the free energy landscape is a funnel in a reasonably broad neighborhood of the native state and that the SDU strategy can generate docking predictions with less than 5 � ligand interface Ca root-mean-square deviation while achieving an approximately 20-fold efficiency gain compared to Monte Carlo methods