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We extend the derivation of second-order relativistic viscous hydrodynamics to incorporate the effects of baryon current, a non-vanishing chemical potential, and a realistic equation of state. Starting from a microscopic quantum theory, we employ a quasiparticle approximation to describe the evolution of hydrodynamic degrees of freedom and establish its connection to the Wigner formalism. Using methods from relativistic kinetic theory, we perform a second-order expansion to derive a closed set of equations for the components of the stress-energy tensor and the baryon current. The resulting transport coefficients, which depend on the equation of state, are obtained through a unified prescription that ensures thermodynamic consistency., Comment: 30 pages with no figures. Comments are welcome

Slippery covalently attached liquid surfaces (SCALS) are a family of nanothin polymer layers with remarkably low static droplet friction, characterised by a low contact angle hysteresis (CAH$< 5 \deg$), which makes them ideally suited to self-cleaning, water harvesting, and anti-fouling applications. Recently, a Goldilocks zone of lowest CAH has been identified for polydimethyl siloxane (PDMS) SCALS of intermediate thickness (3.5 nm), yet, molecular-level insights are missing to reveal the underlying physical mechanism of this elusive, slippery optimum. In this work, the agreement between coarse-grained molecular dynamics simulations and atomic force microscopy data shows that nanoscale defects, as well as deformation for thicker layers, are key to explaining the existence of this `just right' regime. At low thickness values, insufficient substrate coverage gives rise to chemical patchiness; at large thickness values, two features appear: 1) a waviness forms on the surface of the liquid layer due to a previously overlooked lateral microphase separation occurring in polydisperse brushes, and 2) layer deformation due to the contact line is larger than in thinner layers. The most pronounced slippery behaviour occurs for smooth PDMS layers that do not exhibit nanoscale waviness. The converging insights from molecular simulations, experiments, and a contact angle hysteresis theory provide design guidelines for tethered polymer layers with ultra-low contact angle hysteresis., Comment: 4 figures

We study a lattice model describing the non-equilibrium dynamics emerging from the pulling of a tracer particle through a disordered medium occupied by randomly placed obstacles. The model is considered in a restricted geometry pertinent for the investigation of confinement-induced effects. We analytically derive exact results for the characteristic function of the moments valid to first order in the obstacle density. By calculating the velocity autocorrelation function and its long-time tail we find that already in equilibrium the system exhibits a dimensional crossover. This picture is further confirmed by the approach of the drift velocity to its terminal value attained in the non-equilibrium stationary state. At large times the diffusion coefficient is affected by both the driving and confinement in a way that we quantify analytically. The force-induced diffusion coefficient depends sensitively on the presence of confinement. The latter is able to modify qualitatively the non-analytic behavior in the force observed for the unbounded model. We then examine the fluctuations of the tracer particle along the driving force. We show that in the intermediate regime superdiffusive anomalous behavior persists even in the presence of confinement. Stochastic simulations are employed in order to test the validity of the analytic results, exact to first order in the obstacle density and valid for arbitrary force and confinement., Comment: 24 pages, 7 figures

We consider a lattice model in which a tracer particle moves in the presence of randomly distributed immobile obstacles. The crowding effect due to the obstacles interplays with the quasi-confinement imposed by wrapping the lattice onto a cylinder. We compute the velocity autocorrelation function and show that already in equilibrium the system exhibits a dimensional crossover from two- to one-dimensional as time progresses. A pulling force is switched on and we characterize analytically the stationary state in terms of the stationary velocity and diffusion coefficient. Stochastic simulations are used to discuss the range of validity of the analytic results. Our calculation, exact to first order in the obstacle density, holds for arbitrarily large forces and confinement size., Comment: 6 pages, 3 figures

Finding proper collective variables for complex systems and processes is one of the most challenging tasks in simulations, which limits the interpretation of experimental and simulated data and the application of enhanced sampling techniques. Here, we propose a machine learning approach able to distill few, physically relevant variables by associating instantaneous configurations of the system to their corresponding inherent structures as defined in liquids theory. We apply this approach to the challenging case of structural transitions in nanoclusters, managing to characterize and explore the structural complexity of an experimentally relevant system constituted by 147 gold atoms. Our inherent-structure variables are shown to be effective at computing complex free-energy landscapes, transition rates, and at describing non-equilibrium melting and freezing processes. The effectiveness of this machine learning strategy guided by the generally-applicable concept of inherent structures shows promise to devise collective variables for a vast range of systems, including liquids, glasses, and proteins.

Semiconductor nanocrystals (quantum dots) are light emitters with high quantum yield that are relatively easy to manufacture. There is therefore much interest in their possible application for the development of high-performance scintillators for use in high-energy physics. However, few previous studies have focused on the response of these materials to high-energy particles. To evaluate the potential for the use of nanocomposite scintillators in calorimetry, we are performing side-by-side tests of fine-sampling shashlyk calorimeter prototypes with both conventional and nanocomposite scintillators using electron and minimum-ionizing particle beams, allowing direct comparison of the performance obtained., Comment: 3 pages, 2 figures, 1 table

In this paper, we address the problem of compact model parameter extraction to simultaneously extract tens of parameters via derivative-free optimization. Traditionally, parameter extraction is performed manually by dividing the complete set of parameters into smaller subsets, each targeting different operational regions of the device, a process that can take several days or weeks. Our approach streamlines this process by employing derivative-free optimization to identify a good parameter set that best fits the compact model without performing an exhaustive number of simulations. We further enhance the optimization process to address three critical issues in device modeling by carefully choosing a loss function that focuses on relative errors rather than absolute errors to ensure consistent performance across different orders of magnitude, prioritizes accuracy in key operational regions above a specific threshold, and reduces sensitivity to outliers. Furthermore, we utilize the concept of train-test split to assess the model fit and avoid overfitting. We demonstrate the effectiveness of our approach by successfully modeling a diamond Schottky diode with the SPICE diode model and a GaN-on-SiC HEMT with the ASM-HEMT model. For the latter, which involves extracting 35 parameters for the ASM-HEMT DC model, we identified the best set of parameters in under 6,000 trials. Additional examples using both devices are provided to demonstrate robustness to outliers, showing that an excellent fit is achieved even with over 25% of the data purposely corrupted. These examples demonstrate the practicality of our approach, highlighting the benefits of derivative-free optimization in device modeling.

The Martini model, a coarse-grained forcefield for biomolecular simulations, has experienced a vast increase in popularity in the past decade. Its building-block approach balances computational efficiency with high chemical specificity, enabling the simulation of various organic and inorganic molecules. The modeling of coarse-grained beads as Lennard-Jones particles poses challenges for the accurate reproduction of liquid-vapour interfacial properties, which are crucial in various applications, especially in the case of water. The latest version of the forcefield introduces refined interaction parameters for water beads, tackling the well-known artefact of Martini water freezing at room temperature. Additionally, multiple sizes of water beads are available for simulating the solvation of small cavities, including the smallest pockets of proteins. This work focuses on studying the interfacial properties of Martini water, including surface tension, surface thickness, and bulk densities for the liquid and vapour phases. Employing the test-area method, we systematically compute the liquid-vapour surface tension across various combinations of water bead sizes and for temperatures in the range from 300 to 350 K. Our findings provide a comprehensive characterization of Martini 3.0 water intefacial properties. These findings are of interest to the Martini community as they allow users to account for the low interfacial tension of Martini water by properly adjusting observables computed via coarse-grained simulations (e.g., capillary forces) to allow for accurate matching against all-atom or experimental results. Surface tension data are also interpreted in terms of local enrichment of the various mixture components at the liquid-vapour interface by means of Gibbs' adsorption formalism, Comment: 32 pages, 9 tables, 7 figures

Historically it has been believed that a time-independent classical source has no effect on the scattering of relativistic uncharged field, in contrast with single particle quantum mechanics. In this work we show that the dynamics is not trivial. We solve exactly for the scattering amplitudes and find that a key ingredient is the production of particles from the unstable vacuum, conceptually similar to the Schwinger mechanism. We compute exactly the probabilities for the vacuum to decay in $n$ particles. The time dependence of such probabilities displays interesting properties such as the quantum Zeno effect and in particular has no regime where the exponential decay law is a good approximation. We show that the trivial scattering found in the past is the byproduct of the adiabatic switching of the interaction. In fact, it is not possible to switch off the interaction (adiabatically or otherwise) at distant times and recover the exact results. Finally, this non trivial vacuum behavior is a source of particle production. We argue that such non-perturbative calculations can be phenomenologically relevant for the production processes that are suppressed at the lower orders in perturbation theory, for instance dilaton production in a medium., Comment: 33 pages, 3 figures

Earthquakes are rupture-like processes that propagate along tectonic faults and cause seismic waves. The propagation speed and final area of the rupture, which determine an earthquake's potential impact, are directly related to the nature and quantity of the energy dissipation involved in the rupture process. Here we present the challenges associated with defining and measuring the energy dissipation in laboratory and natural earthquakes across many scales. We discuss the importance and implications of distinguishing between energy dissipation that occurs close to and far behind the rupture tip and we identify open scientific questions related to a consistent modeling framework for earthquake physics that extends beyond classical Linear Elastic Fracture Mechanics., Comment: 8 pages, 2 figures

In this paper we present D.A.V.I.D.E. (Development for an Added Value Infrastructure Designed in Europe), an innovative and energy efficient High Performance Computing cluster designed by E4 Computer Engineering for PRACE (Partnership for Advanced Computing in Europe). D.A.V.I.D.E. is built using best-in-class components (IBM's POWER8-NVLink CPUs, NVIDIA TESLA P100 GPUs, Mellanox InfiniBand EDR 100 Gb/s networking) plus custom hardware and an innovative system middleware software. D.A.V.I.D.E. features (i) a dedicated power monitor interface, built around the BeagleBone Black Board that allows high frequency sampling directly from the power backplane and scalable integration with the internal node telemetry and system level power management software; (ii) a custom-built chassis, based on OpenRack form factor, and liquid cooling that allows the system to be used in modern, energy efficient, datacenter; (iii) software components designed for enabling fine grain power monitoring, power management (i.e. power capping and energy aware job scheduling) and application power profiling, based on dedicated machine learning components. Software APIs are offered to developers and users to tune the computing node performance and power consumption around on the application requirements. The first pilot system that we will deploy at the beginning of 2017, will demonstrate key HPC applications from different fields ported and optimized for this innovative platform.

A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large dataset of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and to distill a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold, in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences, such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables which has the potential to be used for exploration and enhanced sampling purposes., Comment: 28 pages, 13 figures

Heavy-ion collisions are a unique tool for testing the behaviour of matter under extreme conditions. The momentum correlations of charm and bottom hadrons have been considered for testing heavy quarks' thermalization in the hot, dense medium produced by the collisions. In this respect, two effects have been considered: the decrease of the initial back-to-back correlations and the increase of correlations due to heavy-quark interactions with collectively flowing medium. Here, we show that, in the case of a single charm and anti-charm hadron pair production, the collective flow allows for testing heavy-quark production locality and spatial diffusion. Using an example of central Pb+Pb collisions at the CERN SPS energies, we demonstrate that the azimuthal correlations of charm and anti-charm hadrons are particularly sensitive to their spatial correlations. We argue that the existing experimental technology and beam intensities at the CERN SPS should allow for the corresponding measurements soon. The correlation measurements in collisions with a single heavy-quark pair produced will provide a unique input constraining the diffusion of charm quarks and verifying assumptions concerning production locality of a charm and anti-charm quark pair., Comment: 16 pages main body, 21 with bibliography. 5 figures

ITER envisages the use of two heating neutral beam injectors plus an optional one as part of the auxiliary heating and current drive system. The 16.5 MW expected neutral beam power per injector is several notches higher than worldwide existing facilities. A Neutral Beam Test Facility (NBTF) was established at Consorzio RFX, exploiting the synergy of two test beds, SPIDER and MITICA. SPIDER is dedicated to developing and characterizing large efficient negative ion sources at relevant parameters in ITER-like conditions: source and accelerator located in the same vacuum where the beam propagates, immunity to electromagnetic interferences of multiple radio-frequency (RF) antennas, avoidance of RF-induced discharges on the outside of the source. Three years of experiments on SPIDER have addressed to the necessary design modifications to enable full performances. The source is presently under a long shut-down phase to incorporate learnings from the experimental campaign. Parallelly, developments on MITICA, the full-scale prototype of the ITER NBI featuring a 1 MV accelerator and ion neutralization, are underway including manufacturing of in-vessel components, while power supplies and auxiliary plants are already under final testing and commissioning. Integration, commissioning and tests of the 1MV power supplies are essential for this first-of-kind system, unparalleled both in research and industry field. The integrated test to confirm 1MV output by combining invertor systems, DC generators and transmission lines extracted errors/accidents in some components. To realize a concrete system for ITER, solutions for the repair and the improvement of the system were developed. Hence, NBTF is emerging as a necessary facility, due to the large gap with existing injectors, effectively dedicated to identify issues and find solutions to enable successful ITER NBI operations in a time bound fashion.

The collisionless Boltzmann equation, also called free-streaming, is a convenient approximation. It is rather simple to implement numerically, and and it is effective at reducing the irregularities of rough initial conditions. It can be obtained as a small $\hbar$ limit from a free scalar quantum field. Namely, by neglecting the $\hbar^2$ term in the dynamical evolution of the Wigner distribution, the quantum precursor of the distribution function. In this work it is presented the general form for the exact solutions of the Wigner distribution, for a scalar field undergoing a $(1+1)$-dimensional expansion. Namely, with the symmetry constraint of rotation and translation invariance in the transverse plane. It is very different from the (on-shell) free-streaming of classical particles. It is shown how to recognize the classical, $\hbar\to 0$, limit from the general form. The numerical analysis for a specific example shows very large quantum corrections to the classical free-streaming., Comment: 15 pages, 1 figure

A timely and long-term programme of kaon decay measurements at a new level of precision is presented, leveraging the capabilities of the CERN Super Proton Synchrotron (SPS). The proposed programme is firmly anchored on the experience built up studying kaon decays at the SPS over the past four decades, and includes rare processes, CP violation, dark sectors, symmetry tests and other tests of the Standard Model. The experimental programme is based on a staged approach involving experiments with charged and neutral kaon beams, as well as operation in beam-dump mode. The various phases will rely on a common infrastructure and set of detectors., Comment: Letter of Intent submitted to CERN SPSC. Address all correspondence to hike-eb@cern.ch

Experiments motivated by predictions of quantum mechanics indicate non-trivial correlations between spacelike-separated measurements. The phenomenon is referred to as a violation of strong-locality and, after Einstein, called ghostly action at a distance. An intriguing and previously unasked question is how the evolution of an assembly of particles to equilibrium-state relates to strong-locality. More specifically, whether, with this respect, indistinguishable particles differ from distinguishable ones. To address the question, we introduce a Markov-chain based framework over a finite set of microstates. For the first time, we formulate conditions needed to obey the particle transport- and strong-locality for indistinguishable particles. Models which obey transport-locality and lead to equilibrium-state are considered. We show that it is possible to construct models obeying and violating strong-locality both for indistinguishable particles and for distinguishable ones. However, we find that only for distinguishable particles strongly-local evolution to equilibrium is possible without breaking the microstate-symmetry. This is the strongest symmetry one can impose and leads to the shortest equilibration time. We hope that the results presented here may provide a new perspective on a violation of strong-locality, and the developed framework will help in future studies. Specifically they may help to interpret results on high-energy nuclear collisions indicating a fast equilibration of indistinguishable particles.

Earthquake forecasting and prediction have long and in some cases sordid histories but recent work has rekindled interest based on advances in early warning, hazard assessment for induced seismicity and successful prediction of laboratory earthquakes. In the lab, frictional stick-slip events provide an analog for earthquakes and the seismic cycle. Labquakes are ideal targets for machine learning (ML) because they can be produced in long sequences under controlled conditions. Recent works show that ML can predict several aspects of labquakes using fault zone acoustic emissions. Here, we generalize these results and explore deep learning (DL) methods for labquake prediction and autoregressive (AR) forecasting. DL improves existing ML methods of labquake prediction. AR methods allow forecasting at future horizons via iterative predictions. We demonstrate that DL models based on Long-Short Term Memory (LSTM) and Convolution Neural Networks predict labquakes under several conditions, and that fault zone stress can be predicted with fidelity, confirming that acoustic energy is a fingerprint of fault zone stress. We predict also time to start of failure (TTsF) and time to the end of Failure (TTeF) for labquakes. Interestingly, TTeF is successfully predicted in all seismic cycles, while the TTsF prediction varies with the amount of preseismic fault creep. We report AR methods to forecast the evolution of fault stress using three sequence modeling frameworks: LSTM, Temporal Convolution Network and Transformer Network. AR forecasting is distinct from existing predictive models, which predict only a target variable at a specific time. The results for forecasting beyond a single seismic cycle are limited but encouraging. Our ML/DL models outperform the state-of-the-art and our autoregressive model represents a novel framework that could enhance current methods of earthquake forecasting., Comment: Published in https://www.sciencedirect.com/science/article/pii/S0012821X22004617