Old and new anomalies in charm

The recent LHCb determination of the direct CP asymmetries in the decays $D^0 \to K^+ K^-, \pi^+ \pi^-$ hints at a sizeable breaking of two approximate symmetries of the SM: CP and U-spin. We aim at explaining the data with BSM physics and use the framework of flavorful $Z^\prime$ models. Interestingly, experimental and theoretical constraints very much narrow down the shape of viable models: Viable, anomaly-free models are electron- and muon-phobic and feature a light $Z^\prime$ of 10-20 GeV coupling only to right-handed fermions. The $Z^\prime$ can be searched for in low mass dijets or at the LHC as well as dark photon searches. A light $Z^\prime$ of $\sim$ 3 GeV or $\sim$ 5-7 GeV can moreover resolve the longstanding discrepancy in the $J/\psi, \psi^\prime$ branching ratios with pion form factors from fits to $e^+ e^- \to \pi^+ \pi^-$ data, and simultaneously explain the charm CP asymmetries. Smoking gun signatures for this scenario are $\Upsilon$ and charmonium decays into pions, taus or invisbles.Comment: 2 pages, 4 figure

Two is better than one: The U-spin-CP anomaly in charm

The recent measurement of the CP-asymmetry in the decay $D \to K^+ K^-$ by LHCb, combined with $\Delta A_{\text{CP}}$, evidences a sizable CP-asymmetry in the decay $D \to \pi^+ \pi^-$, which requires a dynamical enhancement of standard model higher-order contributions over tree-level ones by a factor of two. The data furthermore imply huge U-spin breaking, about 4-5 times larger than the nominal standard model one of $\lesssim 30 \%$ in charm. Enhanced breakdown of the two approximate symmetries points to models that violate U-spin and CP and disfavors flavor singlet contributions such as chromomagnetic dipole operators as explanations of the data. We analyze the reach of flavorful $Z^\prime$ models for charm CP-asymmetries. Models generically feature explicit U-spin and isospin breaking, allowing for correlations with $D \to \pi^0 \pi^0$ and $D^+ \to \pi^+ \pi^0$ decays with corresponding CP-asymmetries at similar level and sign as $D \to \pi^+ \pi^-$, about ${\cal{O}}(1-2) \cdot 10^{-3}$. Experimental and theoretical constraints very much narrow down the shape of viable models: Viable, anomaly-free models are leptophobic, -- or at least electron- and muon-phobic -- with light $Z^\prime$ below ${\cal{O}}(20)$ GeV, and can be searched for in low mass dijets at the LHC or in $\Upsilon$ and charmonium decays, as well as dark photon searches. Models can also feature sizable branching ratios into light right-handed neutrinos or vector-like dark fermions, which can be searched for in $e^+ e^- \to \text{hadrons + invisibles}$ at Belle II and BESIII. Due to the low new physics scale dark fermions can easily induce an early Landau pole, requiring models to be UV-completed near the TeV-scale.Comment: 13 pages, 5 figures. v2: Fig.1 and discussion on U-Spin improved, minor corrections and references adde

Numerical Investigation on the Fixed-Stress Splitting Scheme for Biot’s Equations: Optimality of the Tuning Parameter

We study the numerical solution of the quasi-static linear Biot equations solved iteratively by the fixed-stress splitting scheme. In each iteration the mechanical and flow problems are decoupled, where the flow problem is solved by keeping an artificial mean stress fixed. This introduces a numerical tuning parameter which can be optimized. We investigate numerically the optimality of the parameter and compare our results with physically and mathematically motivated values from the literature, which commonly only depend on mechanical material parameters. We demonstrate, that the optimal value of the tuning parameter is also affected by the boundary conditions and material parameters associated to the fluid flow problem suggesting the need for the integration of those in further mathematical analyses optimizing the tuning parameter.acceptedVersio

Nutzung von KI-Methoden für die Kupplungsentwicklung in automobilen Antriebssträngen = Use of AI methods for clutch development in automotive drivetrains

Im Spannungsfeld steigender Erwartungen an Fahrkomfort und Energieeffizienz stoßen aktuelle Entwicklungsmethoden der Modellbildung und Optimierung für Fahrzeugkupplungen an ihre Grenzen. In diesem Beitrag wird der Einsatz von KI-Methoden für die Kupplungsentwicklung untersucht und ein Überblick anhand verschiedener Anwendungsbeispiele in aktuellen Forschungsprojekten der Mercedes-Benz AG gegeben. Mittels überwachten Lernens und tiefen neuronalen Netzen werden ein Reibungszahlmodell sowie ein Temperaturmodell einer Kupplung mit hoher Abbildungsgüte entwickelt. Verstärkendes Lernen mit tiefen neuronalen Netzen wird zur Synthese von Regelungen für verschiedene Gangwechsel eingesetzt. Fahrzeugmessdaten werden mit Cluster-Algorithmen analysiert, um Handlungsempfehlungen für die Applikation des Motorwiederstarts eines hybriden Antriebsstrangs abzuleiten. Mit den gezeigten Methoden steigt das Automatisierungspotential in der Entwicklung und der Aufwand für die Übernahme komplexer Entwicklungsprozesse auf neue Getriebevarianten sinkt

B-anomalies from flavorful U(1) ^′ extensions, safely

U(1) ′ extensions of the standard model with generation-dependent couplings to quarks and leptons are investigated as an explanation of anomalies in rare B-decays, with an emphasis on stability and predictivity up to the Planck scale. To these ends, we introduce three generations of vector-like standard model singlet fermions, an enlarged, flavorful scalar sector, and, possibly, right-handed neutrinos, all suitably charged under the U(1) ′ gauge interaction. We identify several gauge-anomaly free benchmarks consistent with Bs-mixing constraints, with hints for electron-muon universality violation, and the global b→ s fit. We further investigate the complete two-loop running of gauge, Yukawa and quartic couplings up to the Planck scale to constrain low-energy parameters and enhance the predictive power. A characteristic of models is that the Z′ with TeV-ish mass predominantly decays to invisibles, i.e. new fermions or neutrinos. Z′-production can be studied at a future muon collider. While benchmarks feature predominantly left-handed couplings C9μ and C10μ, right-handed ones can be accommodated as well

Reactive Petri Nets for Workflow Modeling

Petri nets are widely used for modeling and analyzing workflows

Service orchestration with priority constraints

Business process management is an operational management approach that focuses on improving business processes. Business processes, i.e., collections of important activities in an organization, are represented in the form of a workflow, an orchestrated and repeatable pattern of activities amenable to automated analysis and control. Priority is an important concept in modeling workflows. We need priority to model cancelable and compensable tasks within transactional business processes. We use the Reo coordination language to model and formally analyze workflows. In this paper, we propose a constraint-based approach to formalize priority in Reo. We introduce special channels to propagate and block priority flows, define their semantics as constraints, and model priority propagation as a constraint satisfaction problem

Structural Analysis to Determine the Core of Hypoxia Response Network

The advent of sophisticated molecular biology techniques allows to deduce the structure of complex biological networks. However, networks tend to be huge and impose computational challenges on traditional mathematical analysis due to their high dimension and lack of reliable kinetic data. To overcome this problem, complex biological networks are decomposed into modules that are assumed to capture essential aspects of the full network's dynamics. The question that begs for an answer is how to identify the core that is representative of a network's dynamics, its function and robustness. One of the powerful methods to probe into the structure of a network is Petri net analysis. Petri nets support network visualization and execution. They are also equipped with sound mathematical and formal reasoning based on which a network can be decomposed into modules. The structural analysis provides insight into the robustness and facilitates the identification of fragile nodes. The application of these techniques to a previously proposed hypoxia control network reveals three functional modules responsible for degrading the hypoxia-inducible factor (HIF). Interestingly, the structural analysis identifies superfluous network parts and suggests that the reversibility of the reactions are not important for the essential functionality. The core network is determined to be the union of the three reduced individual modules. The structural analysis results are confirmed by numerical integration of the differential equations induced by the individual modules as well as their composition. The structural analysis leads also to a coarse network structure highlighting the structural principles inherent in the three functional modules. Importantly, our analysis identifies the fragile node in this robust network without which the switch-like behavior is shown to be completely absent