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Première mesure de la résonance pygmée par diffusion inélastique de neutrons auprès de l'installation NFS à GANIL-SPIRAL2
No full textThe pygmy dipole resonance (PDR) refers to a low-lying strength in the dipole response of nuclei, located around the neutron separation energy and associated with neutron excess in nuclei. As of today, the available experimental data do not provide an accurate picture of the fine structure of the PDR. These open questions on its structure convey a clear need for more experimental studies to pin down its nature and refine theoretical models. In experimental studies of the PDR via inelastic scattering, the so-called "multi-messenger investigation" shows a clear advantage to extract complementary information on its nature, depending on the probe used. The experiment presented in this manuscript fits into this context and offers, for the first time, a study of the PDR using a neutron probe. The experiment, dedicated to the study of the PDR in the ¹⁴⁰Ce via neutron inelastic scattering, took place in 2022 at the Neutrons For Science facility at GANIL-SPIRAL2. The 30.8 MeV quasi-monoenergetic neutron beams available at the facility, which are unique in terms of intensity, made this study possible. The PARIS and the MONSTER arrays were used for the γ ray and scattered neutron detection respectively. The detection setup offers very good timing characteristics and a high γ-ray efficiency in the PDR region. The analysis of the angular distributions of the scattered neutrons allowed for the extraction of the dipole strength, based on QRPA and DWBA predictions. The obtained results were then compared with other experimental studies with complementary probes and with microscopic predictions.La résonance pygmée est un ensemble d'états de basse énergie par rapport aux résonances géantes, qui émergent dans la réponse dipolaire électrique des noyaux riches en neutrons. Ces excitations sont situées autour des seuils d'émission de particules et épuisent un faible pourcentage de la réponse dipolaire électrique du noyau. Bien que plusieurs modèles théoriques prédisent l'existence de ces états, leur interprétation macroscopique d'une oscillation d'une peau de neutrons ainsi que leur structure fine restent débattus. L'étude de ce mode d'excitation exotique est donc cruciale pour mieux comprendre la structure nucléaire et tester les modèles. Cette thèse propose, pour la première fois, une étude de la résonance pygmée par diffusion inélastique de neutrons (n,n'), une sonde inédite avec une sensibilité complémentaire aux sondes précédemment utilisées pour ces études. L'expérience a été réalisée au Grand Accélérateur National d'Ions Lourds, auprès de l'installation Neutrons For Science de SPIRAL2. Un faisceau de neutrons quasi-monoénergétique de 30,8 MeV a été envoyé sur une cible de cérium naturel afin d'étudier la résonance pygmée dans le ¹⁴⁰Ce. Les neutrons diffusés ainsi que les rayonnements γ émis ont été détectés grâce à, respectivement, l'ensemble MONSTER et l'ensemble PARIS. L'analyse des distributions angulaires des neutrons diffusés à partir des prédictions QRPA et DWBA a permis l'extraction de la force dipolaire et sa comparaison aux données expérimentales existantes ainsi qu'aux prédictions microscopiques
Thermal Cycling Reliability of Hybrid Pixel Sensor Modules for The ATLAS High Granularity Timing Detector
No full textInternational audienceThe reliability of bump connection structures has become a critical aspect of future silicon detectors for particle physics. The High Granularity Timing Detector (HGTD) for the ATLAS experiment at the High-Luminosity Large Hadron Collider will require 8032 hybrid pixel sensor modules, composed of two Low Gain Avalanche Diode sensors bump-bonded to two readout ASICs and glued to a passive PCB. The detector will operate at low temperature (-30 degrees Celsius) to mitigate the impact of irradiation. The thermomechanical reliability of flip-chip bump connections in HGTD modules is a critical concern, particularly due to their characteristically lower bump density (pixel pitch dimensions of 1.3 mm by 1.3 mm). This paper elaborates on the challenges arising from this design characteristic. Finite element analysis and experimental testing were employed to investigate failure modes in the flip-chip bump structures under thermal cycling from -45 degrees Celsius to 40 degrees Celsius and to guide the module redesign. The optimized design demonstrates significantly enhanced robustness and is projected to fulfill the full lifetime requirements of the HGTD
Dynamical friction shear and rotation in Chaplygin cosmology
No full textInternational audienceIn this study, we build upon the findings of Del Popolo (2013) by further analyzing the influence of dynamical friction on the evolution of cosmological perturbations within the framework of the spherical collapse model (SCM) in a Universe dominated by generalized Chaplygin gas (GCG). Specifically, we investigate how dynamical friction alters the growth rate of density perturbations, the effective sound speed, the equation-of-state parameter , and the evolution of the cosmic expansion rate. Our results demonstrate that dynamical friction significantly delays the collapse process compared to the standard SCM. Accurate computation of these parameters is crucial for obtaining consistent results and reliable physical interpretations when employing the GCG model. Furthermore, our analysis confirms that the suppression of perturbation growth due to dynamical friction is considerably more pronounced than that caused by shear and rotation, as previously indicated by Del Popolo (2013). This enhanced suppression effectively addresses the instability issues, such as oscillations or exponential divergences in the dark-matter power spectrum, highlighted in linear perturbation studies, such as those by Sandvik (2004)
Design of experiments for efficient and conform Bayesian learning of seismic fragility curves
No full textInternational audienceSeismic fragility curves quantify the probability of failure of mechanical structures as a function of a seismic intensity measure (IM) that is derived from the ground motion. Although based on a strong assumption, the probit-lognormal model is very popular among practitioners for estimating such curves. Since their estimates may be compromised when data is scarce, this paper presents a novel adaptive design of experiments (DoE) strategy, within a Bayesian framework, to address this issue. This strategy first takes the reference prior theory as a support, in order to minimize the incorporation of subjectivity in the method. It then proposes a sequential selection of seismic signals that maximizes their impact on the posterior distribution. An application of the method on a case study taken from the nuclear industry is proposed. The results demonstrate that our approach significantly improves the accuracy and robustness of fragility curves estimations, particularly in data-limited scenarios
Predicting and Clustering Machine Learning for DataCube Atomic Force Microscope (AFM) Electrical Modes in SSRM
No full textInternational audienceIn nanoscience, techniques based on the atomic force microscope (AFM) are a cornerstone for exploring local electrical, electrochemical, and magnetic properties at the nanoscale. As AFM's capabilities continue to evolve, the challenges of analyzing the data become more significant. With the goal of developing a prediction and clustering model for AFM electrical mode mappings based on machine learning, this work represents a step toward the analysis of big data recorded in the hyperspectral modes: AFM DataCube. To address the complexity of these multi-dimensional measurements and analysis, a self-developed tool is presented. This tool enables the analysis and processing of data, providing visualization options that include captured curves, scanned maps, animated maps in movie format, and a true 3D cube representation. In addition, the solution includes a machine learning algorithm to predict mappings from local features. In the refinement step, for prediction, the Random Forest Regressor model emerged with a root mean square error (RMSE) of 0.18, an R2 value of 0.90, with an execution time of a few minutes. The clustering results are also compared to three unsupervised machine learning methods. This article presents the machine learning prediction and clustering results of DataCube, developed for all AFM DataCube modes. In this study, Scanning Spreading Resistance Microscopy (DCUBE-SSRM) is utilized to analyze a silicon-integrated microelectronic device designed for RF applications
PathoGFAIR: a collection of FAIR and adaptable (meta)genomics workflows for (foodborne) pathogens detection and tracking
No full textInternational audienceAbstract Background Food contamination by pathogens poses a global health threat, affecting an estimated 600 million people annually. During a foodborne outbreak investigation, microbiological analysis of food vehicles detects responsible pathogens and traces contamination sources. Metagenomic approaches offer a comprehensive view of the genomic composition of microbial communities, facilitating the detection of potential pathogens in samples. Combined with sequencing techniques like Oxford Nanopore sequencing, such metagenomic approaches become faster and easier to apply. A key limitation of these approaches is the lack of accessible, easy-to-use, and openly available pipelines for pathogen identification and tracking from (meta)genomic data. Findings PathoGFAIR is a collection of Galaxy-based Findable, Accessible, Interoperable, and Reusable (FAIR) workflows employing state-of-the-art tools to detect and track pathogens from metagenomic Nanopore sequencing. Although initially developed to detect pathogens in food datasets, the workflows can be applied to other metagenomic Nanopore pathogenic data. PathoGFAIR incorporates visualizations and reports for comprehensive results. We tested PathoGFAIR on 130 samples containing different pathogens from multiple hosts under various experimental conditions. For all but 1 sample, workflows have successfully detected expected pathogens at least at the species rank. Further taxonomic ranks are detected for samples with sufficiently high colony-forming unit and low cycle threshold values. Conclusions PathoGFAIR detects the pathogens at species and subspecies taxonomic ranks in all but 1 tested sample, regardless of whether the pathogen is isolated or the sample is incubated before sequencing. Importantly, PathoGFAIR is easy to use and can be straightforwardly adapted and extended for other types of analysis and sequencing techniques, making it usable in various pathogen detection scenarios
Effect of contact line evaporation during nucleate boiling studied by multiscale numerical simulation
No full textInternational audienceThe present paper studies the bubble growth in saturated nucleate boiling considering its multiscale physical behaviour for the isothermal heater case. The effect of microscale evaporation occurring in the close vicinity of contact line has been included through a subgrid microregion model describing the partial wetting case. The microregion model is analysed and compared to other existing approaches. Numerical simulations have been performed using the open-source code TRUST/TrioCFD coupled with the microregion model. The coupled solver calculates the phase change taking into account the evaporation from both the microregion and the macroregion. The interface position is determined with a combined front tracking and volume-of-fluid algorithm. The microregion heat flux is applied locally, without smearing it over the microregion. First, the default phase change model in TrioCFD was validated using benchmark tests based on the Stefan and Scriven problems. Next, the multiscale coupling approach was validated against existing experimental data on nucleate boiling. Our simulations show that microregion evaporation can contribute between 15% and 26% of total bubble growth, with this contribution becoming increasingly significant as superheating intensifies. Neglecting microregion effect leads to an underestimation of the heat fluxes, dry spot sizes and bubble growth rates, thus causing longer departure times; ultimately it can lead to a failure in capturing the bubble dynamics observed in experimental studies
Trainability and Expressivity of Hamming-Weight Preserving Quantum Circuits for Machine Learning
No full textInternational audienceQuantum machine learning (QML) has become a promising area for real world applications of quantum computers, but near-term methods and their scalability are still important research topics. In this context, we analyze the trainability and controllability of specific Hamming weight preserving variational quantum circuits (VQCs). These circuits use qubit gates that preserve subspaces of the Hilbert space, spanned by basis states with fixed Hamming weight k . In this work, we first design and prove the feasibility of new heuristic data loaders, performing quantum amplitude encoding of ( n k ) -dimensional vectors by training an n -qubit quantum circuit. These data loaders are obtained using controllability arguments, by checking the Quantum Fisher Information Matrix (QFIM)'s rank. Second, we provide a theoretical justification for the fact that the rank of the QFIM of any VQC state is almost-everywhere constant, which is of separate interest. Lastly, we analyze the trainability of Hamming weight preserving circuits, and show that the variance of the l 2 cost function gradient is bounded according to the dimension ( n k ) of the subspace. This proves conditions of existence/lack of Barren Plateaus for these circuits, and highlights a setting where a recent conjecture on the link between controllability and trainability of variational quantum circuits does not apply
Sensitivity analysis of a flow redistribution model for a multidimensional and multifidelity simulation of fuel assembly bow in a pressurized water reactor
No full textInternational audienceIn the core of nuclear reactors, fluid–structure interaction and intense irradiation lead to progressive deformation of fuel assemblies. When this deformation is significant, it can lead to additional costs and longer fuel unloading and reloading operations. Therefore, it is preferable to adopt a fuel management that avoids excessive deformation and interactions between fuel assemblies. However, the prediction of deformation and interactions between fuel assemblies is uncertain. Uncertainties affect neutronics, thermohydraulics and thermomechanics parameters. Indeed, the initial uncertainties are propagated over several successive power cycles of twelve months each through the coupling of non-linear, nested and multidimensional thermal–hydraulic and thermomechanical simulations. In this article, we set out to study the hydraulic contribution and quantify the associated uncertainty. To achieve this objective, we develop a multi-stage approach to carry out an initial sensitivity analysis, highlighting the most influential parameters in the hydraulic model. By optimally adjusting these parameters, we aim to obtain a more accurate description of the flow redistribution phenomenon in the reactor core. The aim of the sensitivity analysis presented in this article is to construct an accurate and suitable surrogate model that represents the in-core lateral hydraulic forces in a given state. This surrogate model could then be coupled with a thermomechanical model to quantify the final uncertainty in the simulation of fuel assembly bow within a pressurized water reactor. This approach will provide a better understanding of the interactions between hydraulic and thermomechanical phenomena, thereby improving the reliability and accuracy of the simulation results