Theoretical study of ionization profiles of molecular clouds near supernova remnants: Tracing the hadronic origin of GeV gamma radiation

Context: Since a few years, signatures of supernova remnants associated with molecular clouds have been detected in gamma rays. Whether these gamma rays are generated by cosmic ray electrons or by cosmic ray protons is usually not known. The detection of hadronic ionization signatures in spatial coincidence with gamma ray signatures can help to unambiguously identify supernova remnants as sources of cosmic ray protons. Methods: In order to calculate hadronic signatures from cosmic ray-induced ionization for an examination of the origin of the observed gamma rays, the transport equation for cosmic ray protons propagating in a molecular cloud, including the relevant momentum loss processes, is solved analytically and the proton flux at any position in the cloud is determined. Results: Since the solution of the transport equation is obtained for arbitrary source functions, it can be used for a variety of supernova remnants. The corresponding theoretical ionization rate, as a function of the penetration depth, is derived and compared to photoinduced ionization profiles in a case study with four supernova remnants associated with molecular clouds. Three of the remnants show a clear dominance of the hadronically induced ionization rate, while for one remnant, X-ray emission seems to dominate by a factor of 10. Conclusions: This is the first derivation of position-dependent profiles for cosmic ray-induced ionization with an analytic solution for arbitrary cosmic ray source spectra. The cosmic ray-induced ionization has to be compared to X-ray ionization for strong X-ray sources. For sources dominated by cosmic ray-induced ionization (e.g., W49B), the ionization profiles can be used in the future to map the spatial structure of hadronic gamma rays and rotation-vibrational lines induced by cosmic ray protons, helping to identify sources of hadronic cosmic rays.Comment: published in Astronomy and Astrophysics, 20 pages, 17 figure

Which One is Me?: Identifying Oneself on Public Displays

While user representations are extensively used on public displays, it remains unclear how well users can recognize their own representation among those of surrounding users. We study the most widely used representations: abstract objects, skeletons, silhouettes and mirrors. In a prestudy (N=12), we identify five strategies that users follow to recognize themselves on public displays. In a second study (N=19), we quantify the users' recognition time and accuracy with respect to each representation type. Our findings suggest that there is a significant effect of (1) the representation type, (2) the strategies performed by users, and (3) the combination of both on recognition time and accuracy. We discuss the suitability of each representation for different settings and provide specific recommendations as to how user representations should be applied in multi-user scenarios. These recommendations guide practitioners and researchers in selecting the representation that optimizes the most for the deployment's requirements, and for the user strategies that are feasible in that environment

Semi-Supervised Manifold Learning for Hyperspectral Data

There are real world data sets where a linear approximation like the principalcomponents might not capture the intrinsic characteristics of the data. Nonlineardimensionality reduction ormanifoldlearning uses a graph-based approach tomodel the local structure of the data. Manifold learning algorithms assumethat the data resides on a low-dimensional manifold that is embedded in ahigher-dimensional space. For real world data sets this assumption might not beevident. However, using manifold learning for a classification task can reveal abetter performance than using a corresponding procedure that uses the principalcomponents of the data. We show that this is the case for our hyperspectral dataset using the two manifold learning algorithms Laplacian eigenmaps and locallylinear embedding

Ultracold Chemistry and its Reaction Kinetics

We study the reaction kinetics of chemical processes occurring in the ultracold regime and systematically investigate their dynamics. Quantum entanglement is found to play a key role in driving an ultracold reaction towards a dynamical equilibrium. In case of multiple concurrent reactions Hamiltonian chaos dominates the phase space dynamics in the mean field approximation.Comment: 15 pages, 5 figure

Accelerating Parametric Probabilistic Verification

We present a novel method for computing reachability probabilities of parametric discrete-time Markov chains whose transition probabilities are fractions of polynomials over a set of parameters. Our algorithm is based on two key ingredients: a graph decomposition into strongly connected subgraphs combined with a novel factorization strategy for polynomials. Experimental evaluations show that these approaches can lead to a speed-up of up to several orders of magnitude in comparison to existing approache