Renormalization Group Running of Dimension-Six Sources of Parity and Time-Reversal Violation

We perform a systematic study of flavor-diagonal parity- and time-reversal-violating operators of dimension six which could arise from physics beyond the SM. We begin at the unknown high-energy scale where these operators originate. At this scale the operators are constrained by gauge invariance which has important consequences for the form of effective operators at lower energies. In particular for the four-quark operators. We calculate one-loop QCD and, when necessary, electroweak corrections to the operators and evolve them down to the electroweak scale and subsequently to hadronic scales. We find that for most operators QCD corrections are not particularly significant. We derive a set of operators at low energy which is expected to dominate hadronic and nuclear EDMs due to physics beyond the SM and obtain quantitative relations between these operators and the original dimension-six operators at the high-energy scale. We use the limit on the neutron EDM to set bounds on the dimension-six operators.Comment: Matches published version, 35 pages, 6 figures, minor correction

On-line nonparametric regression to learn state-dependent disturbances

A combination of recursive least squares and weighted least squares is made which can adapt its structure such that a relation between in- and output can he approximated, even when the structure of this relation is unknown beforehand.\ud This method can adapt its structure on-line while it preserves information offered by previous samples, making it applicable in a control setting. This method has been tested with compntergenerated data, and it b used in a simulation to learn the non-linear state-dependent effects, both with good success

Evaluation of the impact of low versus high resolution data on nitrous oxide emissions from a rural landscape

We compared N2O emission results of the simple process based model INITIATOR, using landscape scale data, national scale data and European scale data. All three methods where applied to the Noordelijke Friese Wouden. Abstract about a research project

Pruning Error Minimization in Least Squares Support Vector Machines

The support vector machine (SVM) is a method for classification and for function approximation. This method commonly makes use of an /spl epsi/-insensitive cost function, meaning that errors smaller than /spl epsi/ remain unpunished. As an alternative, a least squares support vector machine (LSSVM) uses a quadratic cost function. When the LSSVM method is used for function approximation, a nonsparse solution is obtained. The sparseness is imposed by pruning, i.e., recursively solving the approximation problem and subsequently omitting data that has a small error in the previous pass. However, omitting data with a small approximation error in the previous pass does not reliably predict what the error will be after the sample has been omitted. In this paper, a procedure is introduced that selects from a data set the training sample that will introduce the smallest approximation error when it will be omitted. It is shown that this pruning scheme outperforms the standard one

On Using a Support Vector Machine in Learning Feed-Forward Control

For mechatronic motion systems, the performance increases significantly if, besides feedback control, also feed-forward control is used. This feed-forward part should contain the (stable part of the) inverse of the plant. This inverse is difficult to obtain if non-linear dynamics are present. To overcome this problem, learning feed-forward control can be applied. The properties of the learning mechanism are of importance in this setting. In the paper, a support vector machine is proposed as the learning mechanism. It is shown that this mechanism has several advantages over other learning techniques when applied to learning feed-forward control. The method is tested with simulation

Phase correction for Learning Feedforward Control

Intelligent mechatronics makes it possible to compensate for effects that are difficult to compensate for by construction or by linear control, by including some intelligence into the system. The compensation of state dependent effects, e.g. friction, cogging and mass deviation, can be realised by learning feedforward control. This method identifies these disturbing effects as function of their states and compensates for these, before they introduce an error. Because the effects are learnt as function of their states, this method can be used for non-repetitive motions. The learning of state dependent effects relies on the update signal that is used. In previous work, the feedback control signal was used as an error measure between the approximation and the true state dependent effect. If the effects introduce a signal that contains frequencies near the bandwidth, the phase shift between this signal and the feedback signal might seriously degenerate the performance of the approximation. The use of phase correction overcomes this problem. This is validated by a set of simulations and experiments that show the necessity of the phase corrected scheme

Beyond-the-Standard-Model matrix elements with the gradient flow

At the Forschungszentrum Juelich (FZJ) we have started a long-term program that aims to determine beyond-the-Standard-Model (BSM) matrix elements using the gradient flow, and to understand the impact of BSM physics in nucleon and nuclear observables. Using the gradient flow, we propose to calculate the QCD component of key beyond the Standard Model (BSM) matrix elements related to quark and strong theta CP violation and the strange content within the nucleon. The former set of matrix elements impacts our understanding of Electric Dipole Moments (EDMs) of nucleons and nuclei (a key signature of BSM physics), while the latter contributes to elastic recoil of Dark Matter particles off nucleons and nuclei. If successful, these results will lay the foundation for extraction of BSM observables from future low-energy, high-intensity and high-accuracy experimental measurements.Comment: 7 pages, 2 figures, presented at the 32nd International Symposium on Lattice Field Theory (Lattice 2014). Correct version of proceedings. Different wording of few paragraphs and different notation on few formulas. Added 1 referenc

First-principle calculations of Dark Matter scattering off light nuclei

We study the scattering of Dark Matter particles off various light nuclei within the framework of chiral effective field theory. We focus on scalar interactions and include one- and two-nucleon scattering processes whose form and strength are dictated by chiral symmetry. The nuclear wave functions are calculated from chiral effective field theory interactions as well and we investigate the convergence pattern of the chiral expansion in the nuclear potential and the Dark Matter-nucleus currents. This allows us to provide a systematic uncertainty estimate of our calculations. We provide results for ${}^2$H, ${}^3$H, and ${}^3$He nuclei which are theoretically interesting and the latter is a potential target for experiments. We show that two-nucleon currents can be systematically included but are generally smaller than predicted by power counting and suffer from significant theoretical uncertainties even in light nuclei. We demonstrate that accurate high-order wave functions are necessary in order to incorporate two-nucleon currents. We discuss scenarios in which one-nucleon contributions are suppressed such that higher-order currents become dominant