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The upcoming commissioning of the superconducting (SC) continuous wave Helmholtz linear accelerators first of series cryomodule is going to demand precise alignment of the four internal SC cavities and two SC solenoids. For optimal results, a beam-based alignment method is used to reduce the misalignment of the whole cryomodule, as well as its individual components. A symmetric beam of low transverse emittance is required for this method, which is to be formed by a collimation system. It consists of two separate plates with milled slits, aligned in the horizontal and vertical direction. The collimation system and alignment measurements are proposed, investigated, and realized. The complete setup of this system and its integration into the existing environment at the GSI High Charge State Injector are presented, as well as the results of the recent reference measurements.
A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector simulation are used to generate Au+Au collision events at 10 AGeV which are then used to train and evaluate PointNet based architectures. The models can be trained on features like the hit position of particles in the CBM detector planes, tracks reconstructed from the hits or combinations thereof. The Deep Learning models reconstruct impact parameters from 2-14 fm with a mean error varying from -0.33 to 0.22 fm. For impact parameters in the range of 5-14 fm, a model which uses the combination of hit and track information of particles has a relative precision of 4-9% and a mean error of -0.33 to 0.13 fm. In the same range of impact parameters, a model with only track information has a relative precision of 4-10% and a mean error of -0.18 to 0.22 fm. This new method of event-classification is shown to be more accurate and less model dependent than conventional methods and can utilize the performance boost of modern GPU processor units.
The HADES experiment at GSI has recently provided data on the flow coefficients v1,..., v4 for protons in Au+Au reactions at Elab = 1.23 AGeV (or √sNN = 2.4 GeV). This data allows to estimate the shear viscosity over entropy density ratio, η/s at low energies via a coarse graining analysis of the UrQMD transport simulations of the flow harmonics in comparison to the experimental data. By this we can provide for the first time an estimate of η/s ≈ 0.65 ± 0.15 (or (8 ± 2)(4π)−1) at such low energies.
Using more than a million randomly generated equations of state that satisfy theoretical and observational constraints, we construct a novel, scale-independent description of the sound speed in neutron stars, where the latter is expressed in a unit cube spanning the normalized radius, r/R, and the mass normalized to the maximum one, M/MTOV. From this generic representation, a number of interesting and surprising results can be deduced. In particular, we find that light (heavy) stars have stiff (soft) cores and soft (stiff) outer layers, or that the maximum of the sound speed is located at the center of light stars but moves to the outer layers for stars with M/MTOV ≳ 0.7, reaching a constant value of cs = 1 2 2 as M → MTOV. We also show that the sound speed decreases below the conformal limit cs = 1 3 2 at the center of stars with M = MTOV. Finally, we construct an analytic expression that accurately describes the radial dependence of the sound speed as a function of the neutron-star mass, thus providing an estimate of the maximum sound speed expected in a neutron star.
Model frameworks, based on Floquet theory, have been shown to produce effective tools for accurately predicting phase-noise response of single (free-running) oscillator systems. This method of approach, referred to herein as macro-modeling, has been discussed in several highly influential papers and now constitutes an established branch of modern circuit theory. The increased application of, for example, injection-locked oscillators and oscillator arrays in modern communication systems has subsequently exposed the demand for similar rigorous analysis tools aimed at coupled oscillating systems. This paper presents a novel solution in terms of a macro-model characterizing the phase-response of synchronized coupled oscillator circuits and systems perturbed by weak noise sources. The framework is generalized and hence applicable to all circuit configurations and coupling topologies generating a synchronized steady-state. It advances and replaces the phenomenological descriptions currently found in the published literature pertaining to this topic and, as such, represents a significant breakthrough w.r.t. coupled oscillator noise modeling. The proposed model is readily implemented numerically using standard routines.
Motivated by recent reports of a quantum-disordered ground state in the triangular lattice compound NaRuO2, we derive a jeff = 1/2 magnetic model for this system by means of first-principles calculations. The pseudospin Hamiltonian is dominated by bond-dependent off-diagonal Γ interactions, complemented by a ferromagnetic Heisenberg exchange and a notably antiferromagnetic Kitaev term. In addition to bilinear interactions, we find a sizable four-spin ring exchange contribution with a strongly anisotropic character, which has been so far overlooked when modeling Kitaev materials. The analysis of the magnetic model, based on the minimization of the classical energy and exact diagonalization of the quantum Hamiltonian, points toward the existence of a rather robust easy-plane ferromagnetic order, which cannot be easily destabilized by physically relevant perturbations.
The coordinate and momentum space configurations of the net baryon number in heavy ion collisions that undergo spinodal decomposition, due to a first-order phase transition, are investigated using state-of-the-art machine-learning methods. Coordinate space clumping, which appears in the spinodal decomposition, leaves strong characteristic imprints on the spatial net density distribution in nearly every event which can be detected by modern machine learning techniques. On the other hand, the corresponding features in the momentum distributions cannot clearly be detected, by the same machine learning methods, in individual events. Only a small subset of events can be systematically differ- entiated if only the momentum space information is available. This is due to the strong similarity of the two event classes, with and without spinodal decomposition. In such sce- narios, conventional event-averaged observables like the baryon number cumulants signal a spinodal non-equilibrium phase transition. Indeed the third-order cumulant, the skewness, does exhibit a peak at the beam energy (Elab = 3–4 A GeV), where the transient hot and dense system created in the heavy ion collision reaches the first-order phase transition.
The development of epilepsy (epileptogenesis) involves a complex interplay of neuronal and immune processes. Here, we present a first-of-its-kind mathematical model to better understand the relationships among these processes. Our model describes the interaction between neuroinflammation, blood-brain barrier disruption, neuronal loss, circuit remodeling, and seizures. Formulated as a system of nonlinear differential equations, the model reproduces the available data from three animal models. The model successfully describes characteristic features of epileptogenesis such as its paradoxically long timescales (up to decades) despite short and transient injuries or the existence of qualitatively different outcomes for varying injury intensity. In line with the concept of degeneracy, our simulations reveal multiple routes toward epilepsy with neuronal loss as a sufficient but non-necessary component. Finally, we show that our model allows for in silico predictions of therapeutic strategies, revealing injury-specific therapeutic targets and optimal time windows for intervention.
For experiments on fission-fragment induced desorption the detection of significant correlations between desorbed ions has been reported [1]. In this paper the method for the detection and quantitative description of these correlations will be described. The statistics of the desorption-process leads to equations for mass-line intensities of ion spectra. Using a time-to-amplitude-converter for flight-time measurements these intensities depend on interdependences of different ions desorbed by the same fission-fragment. The equations allow the computation of correlationcoefficients whose interdependence with desorption probabilities of the respective ions can be shown in Venn-diagrams. Results are given and an interpretation is suggested for fission-fragment desorbed thiamine molecular and fragment ions.
In this Letter we study the radiation measured by an accelerated detector, coupled to a scalar field, in the presence of a fundamental minimal length. The latter is implemented by means of a modified momentum space Green's function. After calibrating the detector, we find that the net flux of field quanta is negligible, and that there is no Planckian spectrum. We discuss possible interpretations of this result, and we comment on experimental implications in heavy ion collisions and atomic systems.