Search

Over the last five years, McGill University's Office of Science Education (OSE) has partnered with faculty members from the Department of Physics to form an education working group with the aim of charting the progression of students' conceptual understanding of uncertainties across their undergraduate degree. The research conducted by this group seeks to provide further insight into both the experimental skill set that students gain through undergraduate laboratory courses and how the department could address noticeable gaps in student understanding. In this paper, we evaluate the conceptual understanding of uncertainty using the Concise Data Processing Assessment (CDPA) instrument. First, we characterize the physics laboratory curriculum at McGill University by evaluating the evolution of CDPA scores across consecutive laboratory courses, and further propose the utilization of this tool for identifying gaps in student understanding. Following the analysis of student responses (N=2023), we specifically investigate data collected in second-year courses to better diagnose what student errors can tell us about common misconceptions in experimental physics. This more in-depth research focuses on data collected from students at the beginning and the end of their first full year of experimental laboratory courses, consisting of two consecutive laboratory courses that build on each other. By the end of the second course, students have engaged with all the material covered in the CDPA test. Interestingly, there have been no changes in CDPA total scores throughout the COVID-19 pandemic. We notice a marked upward shift in student understanding; however, the results indicate that a significant portion of students continue to struggle with uncertainties, basic data analysis, and curve fitting., Comment: 16 pages, 5 figures, 1 table

Direct observation of proton emission with very small emission energy is often unfeasible due to the long partial half-lives associated with tunneling through the Coulomb barrier. Therefore proton emitters with very small Q-values may require masses of both parent and daughter nuclei to establish them as proton unbound. Nuclear mass models have been used to predict the proton drip-line of the thulium (Tm) isotopic chain ($Z=69$), but up until now the proton separation energy has not been experimentally tested. Mass measurements were therefore performed using a Multiple Reflection Time-Of-Flight Mass Spectrometer (MR-TOF-MS) at TRIUMF's TITAN facility to definitively map the limit of proton-bound Tm. The masses of neutron-deficient, $^{149}$Tm and $^{150}$Tm, combined with measurements of $^{149m,g}$Er (which were found to deviate from literature by $\sim$150 keV), provide the first experimental confirmation that $^{149}$Tm is the first proton-unbound nuclide in the Tm chain. Our measurements also enable the strength of the $N=82$ neutron shell gap to be determined at the Tm proton drip-line, providing evidence supporting its continued existence.

Spontaneous neural activity observed in resting-state fMRI is characterized by complex spatio-temporal dynamics. Different measures related to local and global brain connectivity and fluctuations in low-frequency amplitudes can quantify individual aspects of these neural dynamics. Even though such measures are derived from the same functional signals, they are often evaluated separately, neglecting their interrelations and potentially reducing the analysis sensitivity. In our study, we present a fusion searchlight (FuSL) framework to combine the complementary information contained in different resting-state fMRI metrics and demonstrate how this can improve the decoding of brain states. Moreover, we show how explainable AI allows us to reconstruct the differential impact of each metric on the decoding, which additionally increases spatial specificity of searchlight analysis. In general, this framework can be adapted to combine information derived from different imaging modalities or experimental conditions, offering a versatile and interpretable tool for data fusion in neuroimaging.

This work aims at improving our understanding of the conditions enabling the development of an Internal transport barriers (ITB), using a more comprehensive physical model, including low-$\beta$ electromagnetic flux-driven simulations. Our key findings are that electron dynamics is crucial for ITB formation even in an ITG scenario and that having $q_{\text{min}}$ close to a lowest order rational value (2 in our simulations) to allow for eddies self-interaction is a necessary ingredient. Electron dynamics has two critical effects. First, it leads to a structure formation characterized by strong zonal flows shearing rate, reduction of turbulence and profile corrugation. Second, it leads to zonal current sheets that result in a broadening of the minimum-q region, qualitatively consistent with the flux-tube simulations of Vol\v{c}okas et al. [1]. Flux-driven simulations performed with $q_{\text{min}}=2$ reveal the development of the transport barrier in the ion channel, forming at inner and outer radial positions with respect to the $q_{\text{min}}$ position. The ITB formation in flux-driven setup is not recovered if $q_{\text{min}} = 2.03$. Additionally, a simulation at higher $\rho^*$ indicates that the extent of the flattened region of the q-profile due to turbulent self-interaction does not change proportionally to $\rho^*$ or to $\rho_i$, but somewhere in between. On the other hand, the input power required to achieve similar on-axis temperatures appears to exhibit almost GyroBohm scaling (for the two considered $\rho^*$ values). Furthermore, considering an initial q-profile with $q_{\text{min}} = 2.01$, flux-driven simulations show that partial self-interaction can evolve to complete self-interaction. This occurs due to turbulent-driven zonal currents that lower and flatten the q-profile down to $q_{\text{min}} = 2.0$, in line with what is reported in Vol\v{c}okas et al.[1].

Photonic integration of thick holograms in waveguiding structures could be considered the chimera of photonics; multi-faceted and hard to tame. It is the fundamental, and hence indispensable, concept behind compact and monolithically integrated linear optical transformation1. The true relevance of this becomes apparent in the high-dimensional context of unconventional optical computing, that is, in optical neural networks. Yet, integrating such holographic connections is very challenging. It demands high fabrication accuracy, and numerical design of the circuit is often non-tractable for large architectures. Both challenges are intrinsically linked to the usually large refractive index differences between sections of such holographic optical waveguides when using standard techniques of silicon photonics.

Over the past decades, multiple gyrokinetic codes have shown to be able to simulate turbulence and associated transport in the core of Tokamak devices. However, their application to the edge and scrape-off layer (SOL) region presents significant challenges. To date, only few codes and models have been adapted to SOL/edge conditions. To further study the SOL region in particular, with its steep temperature and density gradients as well as large fluctuation amplitudes, the full-f particle-in-cell code PICLS has been developed. PICLS is based on a full-f gyrokinetic model with linearised field equations, considers kinetic electrons and uses logical sheath boundary conditions. In the past, PICLS was verified by applying it to a well-studied 1D parallel transport problem during an edge-localized mode in the SOL under both collisionless and collisional conditions, for which a Lenard-Bernstein collision operator was implemented. PICLS recently was extended towards three spatial dimensions to study turbulence in open-field-line regions in slab and closed-field-line toroidal geometries. In this work, we will focus on the models and methods we used for extending the code towards three spatial dimensions, including validation efforts and comparisons with other existing codes in closed-field-line geometry

We construct defects describing the transition between different phases of gauged linear sigma models with higher rank abelian gauge groups, as well as defects embedding these phases into the GLSMs. Our construction refers entirely to the sector protected by B-type supersymmetry, decoupling the gauge sector. It relies on an abstract characterization of such transition defects and does not involve an actual perturbative analysis. It turns out that the choices that are required to characterize consistent transition defects match with the homotopy classes of paths between different phases. Our method applies to non-anomalous as well as anomalous GLSMs, and we illustrate both cases with examples. This includes the GLSM associated to the resolution of the $A_N$ singularity and one describing the entire parameter space of $N = 2$ minimal models, in particular, the relevant flows between them. Via fusion with boundary conditions, the defects we construct yield functors describing the transport of D-branes on parameter space. We find that our results match with known results on D-brane transport., Comment: 37 pages

We present experiments on reservoir computing (RC) using a network of vertical-cavity surface-emitting lasers (VCSELs) that we diffractively couple via an external cavity. Our optical reservoir computer consists of 24 physical VCSEL nodes. We evaluate the system's memory and solve the 2-bit XOR task and the 3-bit header recognition (HR) task with bit error ratios (BERs) below 1\,\% and the 2-bit digital-to-analog conversion (DAC) task with a root-mean-square error (RMSE) of 0.067.

In this paper, we investigate the effects of ion-scale turbulence-generated currents on the local safety factor profile under conditions of low magnetic shear and proximity to rational surfaces, relevant to Internal Transport Barrier (ITB) formation. Our results show that turbulent currents can generate stationary zonal magnetic potential corrugations, producing a stepped safety factor profile with extended regions of zero magnetic shear. This change significantly affects turbulence self-interaction, resulting in a substantial decrease in turbulent transport, indicating a potential triggering mechanism for transport barrier formation.

In recent years, the hardware implementation of neural networks, leveraging physical coupling and analog neurons has substantially increased in relevance. Such nonlinear and complex physical networks provide significant advantages in speed and energy efficiency, but are potentially susceptible to internal noise when compared to digital emulations of such networks. In this work, we consider how additive and multiplicative Gaussian white noise on the neuronal level can affect the accuracy of the network when applied for specific tasks and including a softmax function in the readout layer. We adapt several noise reduction techniques to the essential setting of classification tasks, which represent a large fraction of neural network computing. We find that these adjusted concepts are highly effective in mitigating the detrimental impact of noise.

KM3NeT/ORCA is an underwater neutrino telescope under construction in the Mediterranean Sea. Its primary scientific goal is to measure the atmospheric neutrino oscillation parameters and to determine the neutrino mass ordering. ORCA can constrain the oscillation parameters $\Delta m^{2}_{31}$ and $\theta_{23}$ by reconstructing the arrival direction and energy of multi-GeV neutrinos crossing the Earth. Searches for deviations from the Standard Model of particle physics in the forward scattering of neutrinos inside Earth matter, produced by Non-Standard Interactions, can be conducted by investigating distortions of the standard oscillation pattern of neutrinos of all flavours. This work reports on the results of the search for non-standard neutrino interactions using the first six detection units of ORCA and 433 kton-years of exposure. No significant deviation from standard interactions was found in a sample of 5828 events reconstructed in the 1 GeV$-$1 TeV energy range. The flavour structure of the non-standard coupling was constrained at 90\% confidence level to be $|\varepsilon_{\mu\tau} | \leq 5.4 \times 10^{-3}$, $|\varepsilon_{e\tau} | \leq 7.4 \times 10^{-2}$, $|\varepsilon_{e\mu} | \leq 5.6 \times 10^{-2}$ and $-0.015 \leq \varepsilon_{\tau\tau} - \varepsilon_{\mu\mu} \leq 0.017$. The results are comparable to the current most stringent limits placed on the parameters by other experiments.

Thermodynamic computing has emerged as a promising paradigm for accelerating computation by harnessing the thermalization properties of physical systems. This work introduces a novel approach to solving quadratic programming problems using thermodynamic hardware. By incorporating a thermodynamic subroutine for solving linear systems into the interior-point method, we present a hybrid digital-analog algorithm that outperforms traditional digital algorithms in terms of speed. Notably, we achieve a polynomial asymptotic speedup compared to conventional digital approaches. Additionally, we simulate the algorithm for a support vector machine and predict substantial practical speedups with only minimal degradation in solution quality. Finally, we detail how our method can be applied to portfolio optimization and the simulation of nonlinear resistive networks., Comment: 13 pages, 4 figures

Indirect dark matter detection methods are used to observe the products of dark matter annihilations or decays originating from astrophysical objects where large amounts of dark matter are thought to accumulate. With neutrino telescopes, an excess of neutrinos is searched for in nearby dark matter reservoirs, such as the Sun and the Galactic Centre, which could potentially produce a sizeable flux of Standard Model particles. The KM3NeT infrastructure, currently under construction, comprises the ARCA and ORCA undersea \v{C}erenkov neutrino detectors located at two different sites in the Mediterranean Sea, offshore of Italy and France, respectively. The two detector configurations are optimised for the detection of neutrinos of different energies, enabling the search for dark matter particles with masses ranging from a few GeV/c$^2$ to hundreds of TeV/c$^2$. In this work, searches for dark matter annihilations in the Galactic Centre and the Sun with data samples taken with the first configurations of both detectors are presented. No significant excess over the expected background was found in either of the two analyses. Limits on the velocity-averaged self-annihilation cross section of dark matter particles are computed for five different primary annihilation channels in the Galactic Centre. For the Sun, limits on the spin-dependent and spin-independent scattering cross sections of dark matter with nucleons are given for three annihilation channels.

In recent years, the hardware implementation of neural networks, leveraging physical coupling and analog neurons has substantially increased in relevance. Such nonlinear and complex physical networks provide significant advantages in speed and energy efficiency, but are potentially susceptible to internal noise when compared to digital emulations of such networks. In this work, we consider how additive and multiplicative Gaussian white noise on the neuronal level can affect the accuracy of the network when applied for specific tasks and including a softmax function in the readout layer. We adapt several noise reduction techniques to the essential setting of classification tasks, which represent a large fraction of neural network computing. We find that these adjusted concepts are highly effective in mitigating the detrimental impact of noise.

The KM3NeT Collaboration has tackled a common challenge faced by the astroparticle physics community, namely adapting the experiment-specific simulation software to work with the CORSIKA air shower simulation output. The proposed solution is an extension of the open-source code gSeaGen, allowing for the transport of muons generated by CORSIKA to a detector of any size at an arbitrary depth. The gSeaGen code was not only extended in terms of functionalities but also underwent a thorough redesign of the muon propagation routine, resulting in a more accurate and efficient simulation. This paper presents the capabilities of the new gSeaGen code as well as prospects for further developments., Comment: 27 pages, 13 figures, submitted to Computer Physics Communications

Neutrinoless double beta decay ($0 \nu \beta \beta$) provides a way to probe physics beyond the Standard Model of particle physics. The upcoming nEXO experiment will search for $0\nu\beta\beta$ decay in $^{136}$Xe with a projected half-life sensitivity exceeding $10^{28}$ years at the 90\% confidence level using a liquid xenon (LXe) Time Projection Chamber (TPC) filled with 5 tonnes of Xe enriched to $\sim$90\% in the $\beta \beta$-decaying isotope $^{136}$Xe. In parallel, a potential future upgrade to nEXO is being investigated with the aim to further suppress radioactive backgrounds, and to confirm $\beta \beta$-decay events. This technique, known as Ba-tagging, comprises of extracting and identifying the $\beta \beta$-decay daughter $^{136}$Ba ion. One tagging approach being pursued involves extracting a small volume of LXe in the vicinity of a potential $\beta \beta$-decay using a capillary tube and facilitating a liquid to gas phase transition by heating the capillary exit. The Ba ion is then separated from the accompanying Xe gas using a radio-frequency (RF) carpet and RF funnel, conclusively identifying the ion as $^{136}$Ba via laser-fluorescence spectroscopy and mass spectrometry. Simultaneously, an accelerator-driven Ba ion source is being developed to validate and optimize this technique. The motivation for the project, the development of the different aspects along with current status and results are discussed here.

The ability to accurately model and predict neutral transport in the boundary plasma is important for tokamak operation. Nevertheless, validation of neutral transport models can be challenging due to the difficulty in measuring neutral particle distributions. Taking advantage of the localized neutral gas puff associated with the Gas Puff Imaging (GPI) diagnostic on the Alcator C-Mod, a validation study of the neutral transport code DEGAS 2 has been performed for helium and deuterium neutrals. Absolutely calibrated measurements of helium and hydrogen line emission are compared with simulated emission from DEGAS 2, accounting for the measured gas flow rates and employing a realistic geometry. Good agreement in peak brightness and profile shape is found for a deuterium puff case. However, helium line emission measurements are found to be lower by a factor of three than that predicted in the steady state DEGAS 2 simulations for a wide range in plasma density. Discrepancies in the light emission profile shape are evident as well. As possible causes for this discrepancy, two effects are discussed. First is the effect of local cooling due to gas puff. Second is the role of time-dependent turbulence effects on neutral penetration.

Optical fibers constitute an attractive platform for the realization of nonlinear and quantum optics processes. Here we show, through theoretical considerations, how polarization effects of both third-order parametric down-conversion and four-wave-mixing in optical fibers may be exploited to enhance detection schemes. We apply our general framework specifically to the case of tapered fibers for photon triplet generation, a long-standing goal within quantum optics, and obtain explicit expectation values for its efficiency. A quantitative investigation of four-wave-mixing in a microstructured solid-core fiber provides significant consequences for the role of polarization in experimental design., Comment: 14 pages, 7 pages SI, 9 figures

The quantum anomalous Hall effect holds promise as a disruptive innovation in condensed matter physics and metrology, as it gives access to Hall resistance quantization in terms of the von-Klitzing constant RK = h/e2 at zero external magnetic field. In this work, we study the accuracy of Hall resistance quantization in a device based on the magnetic topological insulator material (V,Bi,Sb)2Te3. We show that the relative deviation of the Hall resistance from RK at zero external magnetic field is (4.4 +/- 8.7) nohm/ohm when extrapolated to zero measurement current, and (8.6 +/- 6.7) nohm/ohm when extrapolated to zero longitudinal resistivity (each with combined standard uncertainty, k = 1), which sets a new benchmark for the quantization accuracy in topological matter. This precision and accuracy at the nohm/ohm level (or 10-9 of relative uncertainty) achieve the thresholds for relevant metrological applications and establish a zero external magnetic field quantum standard of resistance - an important step towards the integration of quantum-based voltage and resistance standards into a single universal quantum electrical reference., Comment: 11 pages (incl. 7 pages main text and 4 pages supplementary information), with 5 figures and 2 tables. Nat Electron (2024)

Based on the analysis of a large number of high-fidelity nonlinear gyrokinetic simulations, we propose a novel strategy to improve confinement in tokamak plasmas by combining up-down asymmetric flux surface shaping with the Low Momentum Diffusivity (LMD) regime. We show that the intrinsic momentum flux driven by up-down asymmetry creates strong flow shear in the LMD regime that can significantly reduce energy transport, increasing the critical gradient by up to $25\%$. In contrast to traditional methods for generating flow shear, such as neutral beam injection, this approach requires no external momentum source and is expected to scale well to large fusion devices. The experimental applicability of this strategy in spherical tokamaks is addressed via simulations by considering actual equilibria from MAST and a preliminary equilibrium from SMART., Comment: 7 pages, 5 figures

Neutrinos described as an open quantum system may interact with the environment which introduces stochastic perturbations to their quantum phase. This mechanism leads to a loss of coherence along the propagation of the neutrino $-$ a phenomenon commonly referred to as decoherence $-$ and ultimately, to a modification of the oscillation probabilities. Fluctuations in space-time, as envisaged by various theories of quantum gravity, are a potential candidate for a decoherence-inducing environment. Consequently, the search for decoherence provides a rare opportunity to investigate quantum gravitational effects which are usually beyond the reach of current experiments. In this work, quantum decoherence effects are searched for in neutrino data collected by the KM3NeT/ORCA detector from January 2020 to November 2021. The analysis focuses on atmospheric neutrinos within the energy range of a few GeV to $100\,\mathrm{GeV}$. Adopting the open quantum system framework, decoherence is described in a phenomenological manner with the strength of the effect given by the parameters $\Gamma_{21}$ and $\Gamma_{31}$. Following previous studies, a dependence of the type $\Gamma_{ij} \propto (E/E_0)^n$ on the neutrino energy is assumed and the cases $n = -2,-1$ are explored. No significant deviation with respect to the standard oscillation hypothesis is observed. Therefore, $90\,\%$ CL upper limits are estimated as $\Gamma_{21} < 4.6\cdot 10^{-21}\,$GeV and $\Gamma_{31} < 8.4\cdot 10^{-21}\,$GeV for $n = -2$, and $\Gamma_{21} < 1.9\cdot 10^{-22}\,$GeV and $\Gamma_{31} < 2.7\cdot 10^{-22}\,$GeV for $n = -1$, respectively., Comment: 17 pages, 5 figures

This paper makes contributions to "pure" sheaf model theory, the part of model theory in which the models are sheaves over a complete Heyting algebra. We start by outlining the theory in a way we hope is readable for the non-specialist. We then give a careful treatment of the interpretation of terms and formulae. This allows us to prove various preservation results, including strengthenings of the results of Brunner and Miraglia. We give refinements of Miraglia's work on directed colimits and an analogue of Tarski's theorem on the preservation of $\forall_2$-sentences under unions of chains. We next show various categories whose objects are (pairs of) presheaves and sheaves with various notions of morphism are accessible in the category theoretic sense. Together these ingredients allow us ultimately to prove that these categories are encompassed in the AECats framework for independence relations developed by Kamsma.

We consider industrial federated learning, a collaboration between a small number of powerful, potentially competing industrial players, mediated by a third party aspiring to improve the service it provides to its customers. We argue that this configuration harbours covert privacy risks that do not arise in e.g. cross-device settings. Companies are very protective of their intellectual property and production processes. Information about changes to their production and the timing of which is to be kept private. We study a scenario in which one of the collaborators infers changes to their competitors' production by detecting potentially subtle temporal data distribution shifts. In this framing, a data distribution shift is always problematic, even if it has no negative effect on training convergence. Thus, our goal is to find means that allow the detection of distributional shifts better than customary evaluation metrics. Based on the assumption that even minor shifts translate into the collaboratively learned machine learning model, the attacker tracks the shared models' internal state with a selection of metrics from literature in order to pick up on relevant changes. In an empirical study on benchmark datasets, we show an honest-but-curious attacker to be capable of detecting subtle distributional shifts on other clients, in some cases long before they become obvious in evaluation.

Many estimators of the variance of the well-known unbiased and uniform most powerful estimator $\htheta$ of the Mann-Whitney effect, $\theta = P(X < Y) + \nfrac12 P(X=Y)$, are considered in the literature. Some of these estimators are only valid in case of no ties or are biased in case of small sample sizes where the amount of the bias is not discussed. Here we derive an unbiased estimator that is based on different rankings, the so-called 'placements' (Orban and Wolfe, 1980), and is therefore easy to compute. This estimator does not require the assumption of continuous \dfs\ and is also valid in the case of ties. Moreover, it is shown that this estimator is non-negative and has a sharp upper bound which may be considered an empirical version of the well-known Birnbaum-Klose inequality. The derivation of this estimator provides an option to compute the biases of some commonly used estimators in the literature. Simulations demonstrate that, for small sample sizes, the biases of these estimators depend on the underlying \dfs\ and thus are not under control. This means that in the case of a biased estimator, simulation results for the type-I error of a test or the coverage probability of a \ci\ do not only depend on the quality of the approximation of $\htheta$ by a normal \db\ but also an additional unknown bias caused by the variance estimator. Finally, it is shown that this estimator is $L_2$-consistent.

Quantum Communications is a field that promises advances in cryptography, quantum computing and clock synchronisation, among other potential applications. However, communication based on quantum phenomena requires an extreme level of isolation from external disturbances, making the transmission of quantum signals together with classical ones difficult. A range of techniques has been tested to introduce quantum communications in already deployed optical networks which also carry legacy traffic. This comes with challenges, not only at the physical layer but also at the operations and management layer. To achieve a broad acceptance among network operators, the joint management and operation of quantum and classical resources, compliance with standards, and quality and legal assurance need to be addressed. This article presents a detailed account of solutions to the above issues, deployed and evaluated in the MadQCI (Madrid Quantum Communication Infrastructure) testbed. This network is designed to integrate quantum communications in the telecommunications ecosystem by installing quantum-key-distribution modules from multiple providers in production nodes of two different operators. The modules were connected through an optical-switched network with more than 130 km of deployed optical fibre. The tests were done in compliance with strict service level agreements that protected the legacy traffic of the pre-existing classical network. The goal was to achieve full quantum-classical compatibility at all levels, while limiting the modifications of optical transport and encryption and complying with as many standards as possible. This effort was intended to serve as a blueprint, which can be used as the foundation of large-scale quantum network deployments. To demonstrate the capabilities of MadQCI, end-to-end encryption services were deployed and a variety of use-cases were showcased., Comment: 16 pages

Artificial neural networks (ANNs) represent a fundamentally connectionnist and distributed approach to computing, and as such they differ from classical computers that utilize the von Neumann architecture. This has revived research interest in new unconventional hardware to enable more efficient implementations of ANNs rather than emulating them on traditional machines. In order to fully leverage the capabilities of this new generation of ANNs, optimization algorithms that take into account hardware limitations and imperfections are necessary. Photonics represents a particularly promising platform, offering scalability, high speed, energy efficiency, and the capability for parallel information processing. Yet, fully fledged implementations of autonomous optical neural networks (ONNs) with in-situ learning remain scarce. In this work, we propose a ternary weight architecture high-dimensional semiconductor laser-based ONN. We introduce a simple method for achieving ternary weights with Boolean hardware, significantly increasing the ONN's information processing capabilities. Furthermore, we design a novel in-situ optimization algorithm that is compatible with, both, Boolean and ternary weights, and provide a detailed hyperparameter study of said algorithm for two different tasks. Our novel algorithm results in benefits, both in terms of convergence speed and performance. Finally, we experimentally characterize the long-term inference stability of our ONN and find that it is extremely stable with a consistency above 99\% over a period of more than 10 hours, addressing one of the main concerns in the field. Our work is of particular relevance in the context of in-situ learning under restricted hardware resources, especially since minimizing the power consumption of auxiliary hardware is crucial to preserving efficiency gains achieved by non-von Neumann ANN implementations.

The use of high-dimensional systems for quantum communication opens interesting perspectives, such as increased information capacity and noise resilience. In this context, it is crucial to certify that a given quantum channel can reliably transmit high-dimensional quantum information. Here we develop efficient methods for the characterization of high-dimensional quantum channels. We first present a notion of dimensionality of quantum channels, and develop efficient certification methods for this quantity. We consider a simple prepare-and-measure setup, and provide witnesses for both a fully and a partially trusted scenario. In turn we apply these methods to a photonic experiment and certify dimensionalities up to 59 for a commercial graded-index multi-mode optical fiber. Moreover, we present extensive numerical simulations of the experiment, providing an accurate noise model for the fiber and exploring the potential of more sophisticated witnesses. Our work demonstrates the efficient characterization of high-dimensional quantum channels, a key ingredient for future quantum communication technologies.

Entanglement is a fundamental aspect of quantum physics, both conceptually and for its many applications. Classifying an arbitrary multipartite state as entangled or separable -- a task referred to as the separability problem -- poses a significant challenge, since a state can be entangled with respect to many different of its partitions. We develop a certification pipeline that feeds the statistics of random local measurements into a non-linear dimensionality reduction algorithm, to determine with respect to which partitions a given quantum state is entangled. After training a model on randomly generated quantum states, entangled in different partitions and of varying purity, we verify the accuracy of its predictions on simulated test data, and finally apply it to states prepared on IBM quantum computing hardware., Comment: 15 pages, 5 figures

Strong $E\times B$ plasma flow shear is beneficial for reducing turbulent transport. However, traditional methods of driving flow shear do not scale well to large devices such as future fusion power plants. In this paper, we use a large number of nonlinear gyrokinetic simulations to study a novel approach to increase flow shear: decreasing the momentum diffusivity to make the plasma ``easier to push''. We first use an idealized circular geometry and find that one can obtain low momentum diffusivity at tight aspect ratio, low safety factor, high magnetic shear and low temperature gradient. This is the so-called Low Momentum Diffusivity (LMD) regime. To drive intrinsic momentum flux, we then tilt the flux surface, making it up-down asymmetric. In the LMD regime, this intrinsic momentum flux drives strong flow shear that can significantly reduce the heat flux and increase the critical temperature gradient. We also consider the actual experimental geometry of the MAST tokamak to illustrate that this strategy can be practical and create experimentally significant flow shear. Lastly, a preliminary prediction for the SMART tokamak is made., Comment: 43 pages, 23 figures

KM3NeT/ORCA is a water Cherenkov neutrino detector under construction and anchored at the bottom of the Mediterranean Sea. The detector is designed to study oscillations of atmospheric neutrinos and determine the neutrino mass ordering. This paper focuses on an initial configuration of ORCA, referred to as ORCA6, which comprises six out of the foreseen 115 detection units of photo-sensors. A high-purity neutrino sample was extracted, corresponding to an exposure of 433 kton-years. The sample of 5828 neutrino candidates is analysed following a binned log-likelihood method in the reconstructed energy and cosine of the zenith angle. The atmospheric oscillation parameters are measured to be $\sin^2\theta_{23}= 0.51^{+0.04}_{-0.05}$, and $ \Delta m^2_{31} = 2.18^{+0.25}_{-0.35}\times 10^{-3}~\mathrm{eV^2} \cup \{-2.25,-1.76\}\times 10^{-3}~\mathrm{eV^2}$ at 68\% CL. The inverted neutrino mass ordering hypothesis is disfavoured with a p-value of 0.25., Comment: 29 pages, 12 figures

Two-dimensional (2D) organic-inorganic hybrid perovskites emerged as a versatile platform for light-emitting and photovol-taic applications due to their unique structural design and chemical flexibility. Their properties depend heavily on both the choice of the inorganic lead halide framework and the surrounding organic layers. Recently, the introduction of chiral cations into 2D perovskites has attracted major interest due to their potential for introducing chirality and tuning the chiro-optical response. Importantly, the optical properties in these materials are dominated by tightly bound excitons that also serve as primary carriers for the energy transport. The mobility of photoinjected excitons is thus important from the perspectives of fundamental material properties and optoelectronic applications, yet remains an open question. Here, we demonstrate exciton propagation in a 2D chiral perovskite methylbenzylammonium lead iodide (MBA2PbI4) using transient photoluminescence microscopy and reveal density-dependent transport over more than 100 nanometers at room temperature with diffusion coeffi-cients as high as 2 cm2/s. We observe two distinct regimes of initially rapid diffusive propagation and subsequent localiza-tion. Moreover, perovskites with enantiomer pure cations are found to exhibit faster exciton diffusion compared to the race-mic mixture, correlated with the impact of the material composition on disorder. Altogether, the observations of efficient exciton diffusion at room temperature highlight the potential of 2D chiral perovskites to merge chiro-optical properties with strong light-matter interaction and efficient energy transport.

We develop a statistical framework, based on a manifold learning embedding, to extract relevant features of multipartite entanglement structures of mixed quantum states from the measurable correlation data of a quantum computer. We show that the statistics of the measured correlators contains sufficient information to characterise the entanglement, and to quantify the mixedness of the state of the computer's register. The transition to the maximally mixed regime, in the embedding space, displays a sharp boundary between entangled and separable states. Away from this boundary, the multipartite entanglement structure is robust to finite noise.

Enabling bistatic radar sensing within the context of integrated sensing and communication (ISAC) for future sixth generation mobile networks demands strict synchronization accuracy, which is particularly challenging to be achieved with over-the-air synchronization. Existing algorithms handle time and frequency offsets adequately, but provide insufficiently accurate sampling frequency offset (SFO) estimates that result in degradation of obtained radar images in the form of signal-to-noise ratio loss and migration of range and Doppler shift. This article introduces an SFO estimation algorithm named tilt inference of time offset (TITO) for orthogonal frequency-division multiplexing (OFDM)-based ISAC. Using available pilot subcarriers, TITO obtains channel impulse response estimates and extracts information on the SFO-induced delay migration to a dominant reference path with constant range, Doppler shift, and angle between transmit and receive ISAC nodes. TITO then adaptively selects the delay estimates that are only negligibly impaired by SFO-induced intersymbol interference, ultimately employing them to estimate the SFO. Assuming a scenario without a direct line-of-sight (LoS) between the aforementioned transmitting and receiving ISAC nodes, a system concept with a relay reflective intelligent surface (RIS) is used to create the aforementioned reference path is proposed. Besides a mathematical derivation of accuracy bounds, simulation and measurements at 26.2 GHz are presented to demonstrate TITO's superiority over existing methods in terms of SFO estimation accuracy and robustness., Comment: This work has been submitted to the IEEE for possible publication

High-significance evidences of the existence of a high-energy diffuse flux of cosmic neutrinos have emerged in the last decade from several observations by the IceCube Collaboration. The ANTARES neutrino telescope took data for 15 years in the Mediterranean Sea, from 2007 to 2022, and collected a high-purity all-flavour neutrino sample. The search for a diffuse cosmic neutrino signal using this dataset is presented in this article. This final analysis did not provide a statistically significant observation of the cosmic diffuse flux. However, this is converted into limits on the properties of the cosmic neutrino spectrum. In particular, given the sensitivity of the ANTARES neutrino telescope between 1 and 50 TeV, constraints on single-power-law hypotheses are derived for the cosmic diffuse flux below 20 TeV, especially for power-law fits of the IceCube data with spectral index softer than 2.8.

Neutrinoless double beta decay is one of the most sensitive probes for new physics beyond the Standard Model of particle physics. One of the isotopes under investigation is $^{136}$Xe, which would double beta decay into $^{136}$Ba. Detecting the single $^{136}$Ba daughter provides a sort of ultimate tool in the discrimination against backgrounds. Previous work demonstrated the ability to perform single atom imaging of Ba atoms in a single-vacancy site of a solid xenon matrix. In this paper, the effort to identify signal from individual barium atoms is extended to Ba atoms in a hexa-vacancy site in the matrix and is achieved despite increased photobleaching in this site. Abrupt fluorescence turn-off of a single Ba atom is also observed. Significant recovery of fluorescence signal lost through photobleaching is demonstrated upon annealing of Ba deposits in the Xe ice. Following annealing, it is observed that Ba atoms in the hexa-vacancy site exhibit antibleaching while Ba atoms in the tetra-vacancy site exhibit bleaching. This may be evidence for a matrix site transfer upon laser excitation. Our findings offer a path of continued research toward tagging of Ba daughters in all significant sites in solid xenon., Comment: 9 pages, 8 figures

We show that the dual graph of the triangulation of the flow polytope of the zigzag graph adorned with the length-reverse-length framing is a subgraph of a grid graph. Through M\'esz\'aros, Morales, and Striker's bijection between simplices of the triangulation, integer flows of a different, supplemental flow polytope, we provide a simple numerical characterization of the adjacency between the triangulation's simplices in terms of their corresponding integer flows. The proofs result from the development of Postnikov and Stanley's sequences of noncrossing bipartite trees as combinatorial objects we call groves. We propose two new statistics derived from this construction that we conjecture recover the $h^*$-polynomial of the flow polytope of the zigzag graph., Comment: 20 pages, 12 figures, 1 table

In the context of global gyrokinetic simulations of turbulence using a Particle-In-Cell framework, verifying the delta-f assumption with a fixed background distribution becomes challenging when determining quasi-steady state profiles corresponding to given sources over long time scales, where plasma profiles can evolve significantly. The advantage of low relative sampling noise afforded by the delta-f scheme is shown to be retained by considering the background as a time-evolving Maxwellian with time-dependent density and temperature profiles. Implementation of this adaptive scheme to simulate electrostatic collisionless flux-driven turbulence in tokamak plasmas show small and non-increasing sampling noise levels, which would otherwise increase indefinitely with a stationary background scheme. The adaptive scheme furthermore allows one to reach numerically converged results of quasi-steady state with much lower marker numbers.

Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to general-purpose reasoning. While current benchmarks have shown that LLMs can solve tasks using programs like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks or standalone function calls. Solving challenging and practical requires the capability of utilizing diverse function calls as tools to efficiently implement functionalities like data analysis and web development. In addition, using multiple tools to solve a task needs compositional reasoning by accurately understanding complex instructions. Fulfilling both of these characteristics can pose a great challenge for LLMs.To assess how well LLMs can solve challenging and practical tasks via programs, we introduce BigCodeBench, a benchmark that challenges LLMs to invoke multiple function calls as tools from 139 libraries and 7 domains for 1,140 fine-grained tasks. To evaluate LLMs rigorously, each task encompasses 5.6 test cases with an average branch coverage of 99%. In addition, we propose a natural-language-oriented variant of BigCodeBench, BigCodeBench-Instruct, that automatically transforms the original docstrings into short instructions only with essential information. Our extensive evaluation of 60 LLMs shows that LLMs are not yet capable of following complex instructions to use function calls precisely, with scores up to 60%, significantly lower than the human performance of 97%. The results underscore the need for further advancements in this area., Comment: 44 pages, 14 figures, 7 tables, built with love by the BigCode community :)

In this work, we present the first experimental evidence of the presence of zonal flow (ZF) structures in the optimized stellarator Wendelstein 7-X. Using an assortment of diagnostics, flux surface-uniform, electrostatic flow oscillations have been measured, showing a radial scale in the range of tens of ion gyroradii. Such measurements show remarkable agreement with the ZF predicted by local and global non-linear gyrokinetic simulations. These results represent the first direct measurement of ZF in a large stellarator, suitable for the validation of models in reactor relevant conditions., Comment: Submitted to Physical Review Letters

We discuss quantum network Bell nonlocality in a setting where the network structure is not fully known. More concretely, an honest user may trust their local network topology, but not the structure of the rest of the network, involving distant (and potentially dishonest) parties. We demonstrate that quantum network nonlocality can still be demonstrated in such a setting, hence exhibiting topological robustness. Specifically, we present quantum distributions obtained from a simple network that cannot be reproduced by classical models, even when the latter are based on more powerful networks. In particular, we show that in a large ring network, the knowledge of only a small part of the network structure (involving only 2 or 3 neighbouring parties) is enough to guarantee nonlocality over the entire network. This shows that quantum network nonlocality can be extremely robust to changes in the network topology. Moreover, we demonstrate that applications of quantum nonlocality, such as the black-box certification of randomness and entanglement, are also possible in such a setting.

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern AI. Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "yes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained - primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models., Comment: 29 pages, 4 figures