Infrared singularities in one-loop amplitudes

In this talk we discuss a purely numerical approach to next-to-leading order calculations in QCD. We present a simple formula, which provides a local infrared subtraction term for the integrand of a one-loop amplitude. In addition we briefly comment on local ultraviolet subtraction terms and on the required deformation of the contour of integration.Comment: 6 pages, talk given at the conference "Loops and Legs", Woerlitz, 201

Kulturanthropologie des Textilen an der Technischen Universität Dortmund

Die Kulturanthropologie des Textilen an der Technischen Universität Dortmund präsentiert die Studiengänge Lehramt Textilgestaltung sowie Kulturanalyse und Kulturvermittlung sowie das Nebenfach Kulturanthropologie des Textilen mit Studienprojekten, Exkursionen, Auslandskooperationen, der Fachschaft und Studiensammlungen. Es vereint wissenschaftliche, didaktische und gestalterische Ansätze und Betrachtungen auf dem Feld des Vestimentären und der materiellen Kultur

Efficiency improvements for the numerical computation of NLO corrections

In this paper we discuss techniques, which lead to a significant improvement of the efficiency of the Monte Carlo integration, when one-loop QCD amplitudes are calculated numerically with the help of the subtraction method and contour deformation. The techniques discussed are: holomorphic and non-holomorphic division into sub-channels, optimisation of the integration contour, improvement of the ultraviolet subtraction terms, importance sampling and antithetic variates in loop momentum space, recurrence relations.Comment: 34 pages, version to be publishe

Quadratic Unconstrained Binary Optimization Approach for Incorporating Solvency Capital into Portfolio Optimization

In this paper, we consider the inclusion of the solvency capital requirement (SCR) into portfolio optimization by the use of a quadratic proxy model. The Solvency II directive requires insurance companies to calculate their SCR based on the complete loss distribution for the upcoming year. Since this task is, in general, computationally challenging for insurance companies (and therefore, not taken into account during portfolio optimization), employing more feasible proxy models provides a potential solution to this computational difficulty. Here, we present an approach that is also suitable for future applications in quantum computing. We analyze the approximability of the solvency capital ratio in a quadratic form using machine learning techniques. This allows for an easier consideration of the SCR in the classical mean-variance analysis. In addition, it allows the problem to be formulated as a quadratic unconstrained binary optimization (QUBO), which benefits from the potential speedup of quantum computing. We provide a detailed description of our model and the translation into a QUBO. Furthermore, we investigate the performance of our approach through experimental studies