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Nonlinear model calibration through bifurcation curves
No full textInternational audienceNonlinear systems exhibit a plethora of complex dynamic behaviours that are difficult to model and predict accurately. This difficulty often arises from a lack of knowledge of the physics that induces the nonlinear behaviours and the strong sensitivity of the nonlinear dynamics to parameter variation. We introduce in this paper a methodology to carry out nonlinear model updating based on bifurcations. The proposed approach involves minimising the distance between experimental and numerical bifurcation curves, which are key dynamic features that define stability boundaries and regions of multi-stability. For the model, bifurcation curves are computed via standard numerical bifurcation tracking analyses. In the experiment, we use control-based continuation to obtain the data. The approach is first demonstrated on a Duffing and a beam system using synthetic data, before being applied to experimental data collected on a base-excited energy harvester with magnetic nonlinearity
Community challenge towards consensus on characterization of biological tissue: C4Bio’s first findings
No full textInternational audienceThis study investigates methodological variability across various expert laboratories worldwide, with regards to characterizing the mechanical properties of biological tissues. Two testing rounds were conducted on the specific use case of uniaxial tensile testing of porcine aorta. In the first round, 24 labs were invited to apply their established methods to assess inter-laboratory variability. This revealed significant methodological diversity and associated variability in the stress–stretch results, underscoring the necessity for a standardized approach. In the second round, a consensus protocol was collaboratively developed and adopted by 19 labs in an attempt to minimize variability. This involved standardized sample preparation and uniformity in testing protocol, including the use of a common cutting and thickness measurement tool. Despite protocol harmonization, significant variability persisted across labs, which could not be solely attributed to inherent biological differences in tissue samples. These results illustrate the challenges in unifying testing methods across different research settings, underlining the necessity for further refinement of testing practices. Enhancing consistency in biomechanical experiments is pivotal when comparing results across studies, as well as when using the resulting material properties for in silico simulations in medical research
Measurement of asymmetry in decays with the LHCb Upgrade I detector
No full textInternational audienceA measurement of asymmetry in decays is reported, based on a data sample of proton-proton collisions collected with the LHCb Upgrade I detector in 2024 at a centre-of-mass energy of TeV, corresponding to an integrated luminosity of . The decay is used as calibration channel to cancel residual detection and production asymmetries. The time-integrated asymmetry for the mode is measured to be where the first uncertainty is statistical, and the second is systematic. This is the most precise determination of this quantity to date
Utilizing simulated event lists in IACT gamma-ray astronomy
No full textInternational audienceWe present a new approach for calculating the IACT detector response of high-level analyses with publicly available software based on dedicated simulations of the individual observations. For each of them, a corresponding event list is exported, paralleling the event list of the actual data. The information contained in these simulated lists can be utilized flexibly, without the need for but also being able to reduce it to the standard IRF scheme. We show that standard IRFs from simulated event lists yield consistent results, illustrating the validity of the concept. To improve on the standard response generation, we then demonstrate how to calculate the final IRFs for a given analysis geometry directly from the simulated event lists. The result is a more accurate description, where the change in IRF methodology leads to considerable differences. Finally, we introduce a new method of generating background models based on simulated event lists. By properly considering observation and detector conditions, the new approach provides an accurate description of the γ-like background, exhibiting good stability and a moderate systematics level
Quantum vorticity: a not so effective field theory
No full textInternational audienceWe provide a comprehensive picture for the formulation of the perfect fluid in the modern effective field theory formalism at both the classical and quantum level. Due to the necessity of decomposing the hydrodynamical variables into other internal degrees of freedom, the procedure is inherently not unique. We discuss and compare the different inequivalent formulations. These theories possess a peculiarity: the presence of an infinite dimensional symmetry implying a vanishing dispersion relation for the transverse modes. This sets the stage for UV-IR mixing in the quantum theory, which we study in the different formulations focussing on the incompressible limit. We observe that the dispersion relation gets modified by quantum effects to become , where the fundamental excitations can be viewed as vortex-anti-vortex pairs. The spectrum exhibits infinitely many types of degenerate quanta. The unusual sensitivity to UV quantum fluctuations renders the implementation of the defining infinite symmetry somewhat subtle. However we present a lattice regularization that preserves a deformed version of such symmetry
Observation of the decay
No full textInternational audienceThe first observation of the decay is reported using proton-proton collision data recorded with the LHCb detector corresponding to an integrated luminosity of . The decay mode is observed for the first time, with a significance of . Its branching fraction is measured relative to the decay mode \begin{align*} \frac{\cal{BF}(χ_{c1}(3872)\rightarrow J\mskip -3mu/\mskip -2muψμ^+μ^-)}{\cal{BF}(χ_{c1}(3872)\rightarrow J\mskip -3mu/\mskip -2muψπ^+π^-)} = \left(1.64\pm 0.32\pm 0.05\right)\times10^{-3}, \end{align*} where the first uncertainty includes both statistical contributions and systematic contributions which are uncorrelated between data-taking periods, and the second represents the systematic contributions that are correlated between data-taking periods
Full event interpretation with machine-learning-based particle-flow reconstruction in the CMS detector
No full textInternational audienceThe particle-flow (PF) algorithm constructs a global description of each particle collision by producing a comprehensive list of final-state particles, and is central to event reconstruction in the CMS experiment at the CERN LHC. The existing PF implementation relies on physics-motivated heuristics and assumptions that can be replaced by machine-learning (ML) models trained directly on simulated data and naturally suited to modern graphics processing units (GPUs). A state-of-the-art ML-based PF (MLPF) reconstruction algorithm, implemented within the CMS software framework, is presented. The MLPF algorithm performs a learnable full-event reconstruction on GPUs, generalizes across detector conditions and collision energies, and replaces multiple modular reconstruction steps with a single unified model. Physics performance comparable to standard PF reconstruction is achieved in both simulation and data, with improved jet energy resolution and inference time. In simulated top quark-antiquark events under LHC Run-3 (20232024) conditions, the jet energy resolution improves by 1020% for jets with transverse momentum between 30100 GeV. Inference time is evaluated using simulated multijet events, with a median of 20 ms per event on an Nvidia L4 GPU, compared to approximately 110 ms for the standard CMS PF reconstruction
A 3D-shell model of left atrial electromechanics
No full textThe thin-walled nature of the atrial myocardium can lead to artificial stiffening when full 3D electromechanical models are discretized using standard finite elements. In this work, we propose an electromechanical model of the left atrium based on a 3D-shell formulation that overcomes these limitations. The model incorporates both passive and active components of atrial tissue mechanics, while atrioventricular interaction is described by the coupling with a 0D electromechanical model of the left ventricle. The proposed approach is assessed under physiological and pathological conditions and systematically compared with the standard full 3D formulation. The results demonstrate the superior robustness and computational efficiency of the proposed 3D-shell electromechanical model
Entropic Mirror Monte Carlo
No full textImportance sampling is a Monte Carlo method which designs estimators of expectations under a target distribution using weighted samples from a proposal distribution. When the target distribution is complex, such as multimodal distributions in highdimensional spaces, the efficiency of importance sampling critically depends on the choice of the proposal distribution. In this paper, we propose a novel adaptive scheme for the construction of efficient proposal distributions. Our algorithm promotes efficient exploration of the target distribution by combining global sampling mechanisms with a delayed weighting procedure. The proposed weighting mechanism plays a key role by enabling rapid resampling in regions where the proposal distribution is poorly adapted to the target. Our sampling algorithm is shown to be geometrically convergent under mild assumptions and is illustrated through various numerical experiments
Beyond Log-Concavity and Score Regularity: Improved Convergence Bounds for Score-Based Generative Models in W2 -distance
No full textInternational audienceScore-based Generative Models (SGMs) aim to sample from a target distribution by learning score functions using samples perturbed by Gaussian noise. Existing convergence bounds for SGMs in the W2-distance rely on stringent assumptions about the data distribution. In this work, we present a novel framework for analyzing W2-convergence in SGMs, significantly relaxing traditional assumptions such as log-concavity and score regularity. Leveraging the regularization properties of the Ornstein-Uhlenbeck (OU) process, we show that weak log-concavity of the data distribution evolves into log-concavity over time. This transition is rigorously quantified through a PDE-based analysis of the Hamilton-Jacobi-Bellman equation governing the log-density of the forward process. Moreover, we establish that the drift of the time-reversed OU process alternates between contractive and noncontractive regimes, reflecting the dynamics of concavity. Our approach circumvents the need for stringent regularity conditions on the score function and its estimators, relying instead on milder, more practical assumptions. We demonstrate the wide applicability of this framework through explicit computations on Gaussian mixture models, illustrating its versatility and potential for broader classes of data distributions