A new approach to color-coherent parton evolution

We present a simple parton-shower model that replaces the explicit angular ordering of the coherent branching formalism with a differentially accurate simulation of soft-gluon radiation by means of a non-trivial dependence of the splitting functions on azimuthal angles. We introduce a global kinematics mapping and provide an analytic proof that it satisfies the criteria for next-to leading logarithmic accuracy. In the new algorithm, initial and final state evolution are treated on the same footing. We provide an implementation for final-state evolution in the numerical code Alaric and present a first comparison to experimental data

Interference effects in the H(→ γγ) + 2 jets channel at the LHC

We compute the interference between the resonant process pp → H(→ γγ) + 2 jets and the corresponding continuum background at leading order in QCD. For the Higgs signal, we include gluon fusion (GF) and vector boson fusion (VBF) production channels, while for the background we consider all tree-level contributions, including pure EW effects (O(α 4 QED)) and QCD contributions (O(α 2 QEDα 2 s )), plus the loopinduced gluon-initiated process. After convolution with the experimental mass resolution, the main effect of the interference is to shift the position of the mass peak, as in the inclusive GF case studied previously. The apparent mass shift is small in magnitude but strongly dependent on the Higgs width, potentially allowing for a measurement of, or bound on, the width itself. In the H(→ γγ) + 2 jets channel, the VBF and GF contributions generate shifts of opposite signs which largely cancel, depending on the sets of cuts used, to as little as 5 MeV (toward a lower Higgs mass). The small magnitude of the shift makes this channel a good reference mass for measuring the inclusive mass shift of around 60 MeV in the Standard Model.Fil: Coradeschi, F.. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentina. Università degli Studi di Firenze; ItaliaFil: de Florian, Daniel Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; ArgentinaFil: Dixon, L. J.. University of Stanford; Estados Unidos. Walter Burke Institute for Theoretical Physics; Estados UnidosFil: Fidanza Romani, Nerina Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; ArgentinaFil: Höche, S.. University of Stanford; Estados UnidosFil: Ita, H.. Albert Ludwigs Universität Freiburg; AlemaniaFil: Li, Y.. University of Stanford; Estados UnidosFil: Mazzitelli, Javier Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física; Argentin

Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra

The use of sophisticated machine learning (ML) models, such as graph neural networks (GNNs), to predict complex molecular properties or all kinds of spectra has grown rapidly. However, ensuring the interpretability of these models’ predictions remains a challenge. For example, a rigorous understanding of the predicted X-ray absorption spectrum (XAS) generated by such ML models requires an in-depth investigation of the respective black-box ML model used. Here, this is done for different GNNs based on a comprehensive, custom-generated XAS data set for small organic molecules. We show that a thorough analysis of the different ML models with respect to the local and global environments considered in each ML model is essential for the selection of an appropriate ML model that allows a robust XAS prediction. Moreover, we employ feature attribution to determine the respective contributions of various atoms in the molecules to the peaks observed in the XAS spectrum. By comparing this peak assignment to the core and virtual orbitals from the quantum chemical calculations underlying our data set, we demonstrate that it is possible to relate the atomic contributions via these orbitals to the XAS spectrum

Integrating Explainability into Graph Neural Network Models for the Prediction of X-ray Absorption Spectra

The use of sophisticated machine learning (ML) models, such as graph neural networks (GNNs), to predict complex molecular properties or all kinds of spectra has grown rapidly. However, ensuring the interpretability of these models’ predictions remains a challenge. For example, a rigorous understanding of the predicted X-ray absorption spectrum (XAS) generated by such ML models requires an in-depth investigation of the respective black-box ML model used. Here, this is done for different GNNs based on a comprehensive, custom-generated XAS data set for small organic molecules. We show that a thorough analysis of the different ML models with respect to the local and global environments considered in each ML model is essential for the selection of an appropriate ML model that allows a robust XAS prediction. Moreover, we employ feature attribution to determine the respective contributions of various atoms in the molecules to the peaks observed in the XAS spectrum. By comparing this peak assignment to the core and virtual orbitals from the quantum chemical calculations underlying our data set, we demonstrate that it is possible to relate the atomic contributions via these orbitals to the XAS spectrum

Role of Phase Composition of PEO Coatings on AA2024 for In-Situ LDH Growth

Plasma electrolytic oxidation (PEO) is an environmentally friendly anodizing technique leading to the formation of a ceramic-like coatings under high-voltage discharges. Layered double hydroxides (LDHs) were grown directly on γ, α, and amorphous Al2O3 powders, respectively, in order to investigate the phase responsible for in-situ LDH growth on PEO coating. Furthermore, it is shown that LDH growth is limited by the high tortuosity of the PEO layer and the accessibility of Al(OH) − 4 anions from the substrate covered with thin amorphous aluminum oxide, through the pores

Design of a nitrogen-implanted titanium-based superelastic alloy with optimized properties for biomedical applications

International audienceIn this study, a superelastic Ni-free Ti-based biomedical alloy was treated in surface by the implantation of nitrogen ions for the first time. The N-implanted surface was characterized by X-ray diffraction, X-ray photoelectron spectroscopy, and secondary ion mass spectroscopy, and the superficial mechanical properties were evaluated by nano-indentation and by ball-on-disk tribological tests. To investigate the biocompatibility, the corrosion resistance of the N-implanted Ti alloy was evaluated in simulated body fluids (SBF) complemented by in-vitro cytocompatibility tests on human fetal osteoblasts. After implantation, surface analysis methods revealed the formation of a titanium-based nitride on the substrate surface. Consequently, an increase in superficial hardness and a significant reduction of friction coefficient were observed compared to the non-implanted sample. Also, a better corrosion resistance and a significant decrease in ion release rates have been obtained. Cell culture experiments indicated that the cytocompatibility of the N-implanted Ti alloy was superior to that of the corresponding non-treated sample. Thus, this new functional N-implanted titanium-based superelastic alloy presents the optimized properties that are required for various medical devices: superelasticity, high superficial mechanical properties, high corrosion resistance and excellent cytocompatibility

The Stability and Chloride Entrapping Capacity of ZnAl-NO2 LDH in High-Alkaline/Cementitious Environment

In this work, the ZnAl-NO2 LDH (layered double hydroxide) is investigated as a possible additive for mitigating the chloride-induced corrosion of steel in reinforced concrete. The investigation focused on the stability and chloride binding capacity of this LDH in the pH range typical of cementitious materials. Until pH = 12.5 the material was stable and effective in capturing chloride ions from the surrounding aqueous environment. For higher pH, precisely that of hydrated cement, the LDH was partially dissolved and OH− preferentially entrapped instead of Cl−. These results suggested that ZnAl-NO2 has excellent chloride entrapping capability at neutral pH, but this is reduced with increasing pH. However, when the LDH was incorporated into mortars, the chloride ingress was delayed, signifying that the dissolution of LDH leads to a secondary mechanism responsible for chloride capture

Computational modelling of plasma electrolytic oxidation process induced damage in extruded Mg material

Magnesium (Mg) alloys are an attractive constructive material due to their light weight and high mechanical strength. Plasma electrolyte oxidation (PEO) treatment of Mg alloys creates a thin ceramic coating with protective effects against mechanical wear and corrosion. The coating properties like its porosity and thickness can be adjusted by PEO process parameters and at the same time affects the material behaviour under tensile strength. In this work, dedicated slow-strain rate experiments of differently PEO coated Mg alloy dog-bone shaped specimen were conducted and the coating porosity, thickness and crack spacing were analyzed in order to deduce a predictive Finite Element Method (FEM) damage model. The results indicate that the thicker, more porous coatings lead to material failure at smaller strains in plastic regions. The effect can be implemented via partial differential equation into the FEM model