Investigation of the Galactic Magnetic Field with Ultra-High Energy Cosmic Rays

We present a method to correct for deflections of ultra-high energy cosmic rays in the galactic magnetic field. We perform these corrections by simulating the expected arrival directions of protons using a parameterization of the field derived from Faraday rotation and synchrotron emission measurements. To evaluate the method we introduce a simulated astrophysical scenario and two observables designed for testing cosmic ray deflections. We show that protons can be identified by taking advantage of the galactic magnetic field pattern. Consequently, cosmic ray deflection in the galactic field can be verified experimentally. The method also enables searches for directional correlations of cosmic rays with source candidates.Comment: 12 pages, 3 figures, presented at the Eur. Phys. Soc. Conf. on High Energy Physics, Jul. 2015, Vienna, Austria, and the 34th Intern. Cosmic Ray Conf., Jul. 2015, The Hague, The Netherland

Crowdbreaks: Tracking Health Trends using Public Social Media Data and Crowdsourcing

In the past decade, tracking health trends using social media data has shown great promise, due to a powerful combination of massive adoption of social media around the world, and increasingly potent hardware and software that enables us to work with these new big data streams. At the same time, many challenging problems have been identified. First, there is often a mismatch between how rapidly online data can change, and how rapidly algorithms are updated, which means that there is limited reusability for algorithms trained on past data as their performance decreases over time. Second, much of the work is focusing on specific issues during a specific past period in time, even though public health institutions would need flexible tools to assess multiple evolving situations in real time. Third, most tools providing such capabilities are proprietary systems with little algorithmic or data transparency, and thus little buy-in from the global public health and research community. Here, we introduce Crowdbreaks, an open platform which allows tracking of health trends by making use of continuous crowdsourced labelling of public social media content. The system is built in a way which automatizes the typical workflow from data collection, filtering, labelling and training of machine learning classifiers and therefore can greatly accelerate the research process in the public health domain. This work introduces the technical aspects of the platform and explores its future use cases

Learning to Understand by Evolving Theories

In this paper, we describe an approach that enables an autonomous system to infer the semantics of a command (i.e. a symbol sequence representing an action) in terms of the relations between changes in the observations and the action instances. We present a method of how to induce a theory (i.e. a semantic description) of the meaning of a command in terms of a minimal set of background knowledge. The only thing we have is a sequence of observations from which we extract what kinds of effects were caused by performing the command. This way, we yield a description of the semantics of the action and, hence, a definition.Comment: KRR Workshop at ICLP 201

Parameter Estimation and Confidence Regions in the Method of Light Curve Simulations for the Analysis of Power Density Spectra

The Method of Light Curve Simulations is a tool that has been applied to X-ray monitoring observations of Active Galactic Nuclei (AGN) for the characterization of the Power Density Spectrum (PDS) of temporal variability and measurement of associated break frequencies (which appear to be an important diagnostic for the mass of the black hole in these systems as well as their accretion state). It relies on a model for the PDS that is fit to the observed data. The determination of confidence regions on the fitted model parameters is of particular importance, and we show how the Neyman construction based on distributions of estimates may be implemented in the context of light curve simulations. We believe that this procedure offers advantages over the method used in earlier reports on PDS model fits, not least with respect to the correspondence between the size of the confidence region and the precision with which the data constrain the values of the model parameters. We plan to apply the new procedure to existing RXTE and XMM observations of Seyfert I galaxies as well as RXTE observations of the Seyfert II galaxy NGC 4945.Comment: 9 pages, 2 figures, accepted for publication in Ap

A higher order TV-type variational problem related to the denoising and inpainting of images

We give a comprehensive survey on a class of higher order variational problems which are motivated by applications in mathematical imaging. The overall aim of this note is to investigate if and in which manner results from the first author's previous work on variants of the TV-regularization model (see e.g. [BF1], [BF2], [BF3] and [FT]) can be extended to functionals which involve higher derivatives. This seems to be not only of theoretical interest, but also relevant to applications since higher order TV-denoising appears to maintain the advantages of the classical model as introduced by Rudin, Osher and Fatemi in [ROF] while avoiding the unpleasant "staircasing" effect (see e.g. [BKP] or [LLT]). Our paper features results concerning generalized solutions in spaces of functions of higher order bounded variation, dual solutions as well as partial regularity of minimizers

Convex Regularization of Multi-Channel Images Based on Variants of the TV-Model

We discuss existence and regularity results for multi-channel images in the setting of isotropic and anisotropic variants of the TV-model

Production of Jet Pairs at Large Relative Rapidity in Hadron-Hadron Collisions as a Probe of the Perturbative Pomeron

The production of jet pairs with small transverse momentum and large relative rapidity in high energy hadron-hadron collisions is studied. The rise of the parton-level cross section with increasing rapidity gap is a fundamental prediction of the BFKL `perturbative pomeron' equation of Quantum Chromodynamics. However, at fixed collider energy it is difficult to disentangle this effect from variations in the cross section due to the parton distributions. It is proposed to study instead the distribution in the azimuthal angle difference of the jets as a function of the rapidity gap. The flattening of this distribution with increasing dijet rapidity gap is shown to be a characteristic feature of the BFKL behaviour. Predictions for the Fermilab proton-antiproton collider are presented.Comment: 17 pages, 11 figures, preprint DTP/94/0