Physics Techniques Research Articles

Channel-resolved wavefunctions of transverse magnetic focusing

Transverse magnetic focusing (TMF) is a staple technique in mesoscopic physics, used to study quasiparticles in a manner akin to mass spectrometry. However, the quantum nature of TMF has been difficult to appreciate due to several challenges in addressing the wavelike properties of the quasiparticles. Here, we report a numerical solution and experimental demonstration of the TMF wavefunction for the multichannel case, implemented using quantum point contacts in a two-dimensional electron gas. The wavefunctions could be understood as transverse modes of the emitter tracing a classical trajectory, and the geometric origins of multichannel effects were easily intuited from this simple picture. We believe our results may correspond to a near-field regime of TMF, in contrast to a far-field regime where the well-established semiclassical results are valid. Based on disorder analysis, we expect these results will apply to a wide range of realistic devices, suggesting that spatially coherent features even at the wavelength scale can be appreciated from TMF.

(Invited) Organic Coatings for Corrosion Protection: Highlighting the Polymer Molecular Mobility Contribution to the EIS Response

Organic coatings are the single most widely applied method for corrosion protection of metallic materials. The coatings have generally low permeability to water and oxygen [1,2] and high electrical resistivity (specifically ionic resistance) to impede electrochemical reactions [3,4]. Protective performance of organic coatings are commonly assessed by electrochemical impedance spectroscopy (EIS) [5]. Monitoring of impedance diagrams over immersion time in an electrolytic solution generally allows corrosion appearance in the impedance spectra to be detected, hence predicting the coating’s lifetime. Moreover, most failure modes leading to corrosion damage at the macroscopic scale do not occur without water uptake first taking place [6,7]. EIS can be used to monitor the water uptake [8-11] but rarely at elevated temperatures [12].In the field of polymer physics, molecular mobility is of prime importance as it governs the physical behavior of these macromolecular material. Broadband dielectric spectroscopy (BDS) is a widespread technique in the polymer physics community as it yields the dipole relaxation modes associated with molecular mobility in polymers [13].EIS studies of organic coatings hardly ever approach the barrier properties from a molecular mobility point of view. In recent papers [14-18], our group has studied industrial organic coatings with the objective of identifying the contribution of the molecular mobility on their electrochemical impedance responses. The coatings were first analyzed in the dry state by means of BDS, and then in the wet state by EIS. By performing EIS measurements at various constant temperatures and by applying the dielectric permittivity formalism to the impedance data, the signature of the molecular mobility (α-mode, dielectric manifestation of the glass transition) was shown in their impedance response.Through our recent studies, the presentation will highlight that temperature-controlled EIS (including ambient temperature) brings insights in the molecular mobility of protective organic coatings. Over long immersion times, such studies identified the influence of plasticization (linked to water uptake) or ageing of the polymer matrix (leaching of plasticizers, for example), which has a direct impact on the coatings performance against corrosion. The dielectric manifestation of the glass transition was evidenced from EIS measurements through the use of the dielectric permittivity formalism.

Pore to core plug scale characterization of porosity and permeability heterogeneities in a Cretaceous carbonate reservoir using laboratory measurements and digital rock physics, Abu Dhabi, United Arab Emirates

Laboratory and digital rock physics study of a Cretaceous limestone reservoir in Abu Dhabi, United Arab Emirates, provides insights into pore connectivity, pore-throat size, and permeability distributions. The procedure involves core analysis at different scales using micro-computed tomography, scanning electron microscopy, and nano-computed tomography; porosity quantification using segmentation techniques; numerical simulation of permeability using lattice Boltzmann method; and upscaling simulation results to core-plug scale using Darcy's Law. The greater proportion of connected versus poorly interconnected pores is attributed to (i) the formation of early diagenetic grain-rimming calcite cement, which reduced the degree of mechanical compaction and pore-throat size reduction, (ii) limited introduction of carbonate mud by bioturbation due to rapid sediment burial, and (iii) dissolution of allochems and partial dolomitization of micrite matrix. Microbial micritization of allochems at the seafloor led to the transformation of grain-supported limestones, dominated by unimodal macropores, into reservoirs with multimodal porosity. Conversely, the mud–supported limestones have depositional unimodal micropore distribution, which is reduced by mechanical compaction and cementation by calcite micro-overgrowths around micrite particles. Good agreement between laboratory and simulated values suggests that lateral facies and related textural variation across different depositional environments have limited implications for digital rock physics techniques in predicting the petrophysical properties of limestone reservoirs. Nevertheless, careful selection of representative elementary volume within a geologic context for a complex, anisotropic limestone reservoir is the key to achieving reliable results.

Intermediate mass-range particles from small scales: Nonperturbative techniques for cosmological collider physics from large-scale structure surveys

Intermediate mass-range particles from small scales: Nonperturbative techniques for cosmological collider physics from large-scale structure surveys

Secondary vertex reconstruction with MaskFormers

In high-energy particle collisions, the reconstruction of secondary vertices from heavy-flavour hadron decays is crucial for identifying and studying jets initiated by b- or c-quarks. Traditional methods, while effective, require extensive manual optimisation and struggle to perform consistently across wide regions of phase space. Meanwhile, recent advancements in machine learning have improved performance but are unable to fully reconstruct multiple vertices. In this work we propose a novel approach to secondary vertex reconstruction based on recent advancements in object detection and computer vision. Our method directly predicts the presence and properties of an arbitrary number of vertices in a single model. This approach overcomes the limitations of existing techniques. Applied to simulated proton-proton collision events, our approach demonstrates significant improvements in vertex finding efficiency, achieving a 10% improvement over an existing state-of-the-art method. Moreover, it enables vertex fitting, providing accurate estimates of key vertex properties such as transverse momentum, radial flight distance, and angular displacement from the jet axis. When integrated into a flavour tagging pipeline, our method yields a 50% improvement in light-jet rejection and a 15% improvement in c-jet rejection at a b-jet selection efficiency of 70%. These results demonstrate the potential of adapting advanced object detection techniques for particle physics, and pave the way for more powerful and flexible reconstruction tools in high-energy physics experiments.

Geometric Algebra Framework Applied to Single-Phase Linear Circuits with Nonsinusoidal Voltages and Currents

We apply a well known technique of theoretical physics, known as geometric algebra or Clifford algebra, to linear electrical circuits with nonsinusoidal voltages and currents. We rederive from the first principles the geometric algebra approach to the apparent power decomposition. The important new point consists of endowing the space of Fourier harmonics with a structure of a geometric algebra (it is enough to define the Clifford product of two periodic functions). We construct a set of commuting invariant imaginary units which are used to define impedance and admittance for any frequency.

Quasi-boson approximation yields accurate correlation energy in the 2D electron gas

We report the successful adaptation of the quasi-boson approximation, a technique traditionally employed in nuclear physics, to the analysis of the two-dimensional electron gas. We show that the correlation energy estimated from this approximation agrees closely with the results obtained from quantum Monte Carlo simulations. Our methodology comprehensively incorporates the exchange self-energy, direct scattering, and exchange scattering for a particle-hole pair excited out of the mean-field ground state within the equation-of-motion framework. The linearization of the equation of motion leads to a generalized random phase approximation (gRPA) eigenvalue equation whose spectrum indicates that the plasmon dispersion remains unaffected by exchange effects, while the particle-hole continuum experiences a marked upward shift due to the exchange self-energy. Using the gRPA excitation spectrum, we calculate the zero-point energy of the quasi-boson Hamiltonian, thereby approximating the correlation energy of the original Hamiltonian. This research highlights the potential and effectiveness of applying the quasi-boson approximation to the gRPA spectrum, a fundamental technique in nuclear physics, to extended condensed matter systems. Published by the American Physical Society 2024

Enhanced high harmonic efficiency through phonon-assisted photodoping effect

High-harmonic generation (HHG) has emerged as a central technique in attosecond science and strong-field physics, providing a tool for investigating ultrafast dynamics. However, the microscopic mechanism of HHG in solids is still under debate, and it is unclear how it is modified in the ubiquitous presence of phonons. Here we theoretically investigate the role of collectively coherent vibrations in HHG in a wide range of solids (e.g., hBN, graphite, 2H-MoS2, and diamond). We predict that phonon-assisted high harmonic yields can be significantly enhanced, compared to the phonon-free case – up to a factor of ~20 for a transverse optical phonon in bulk hBN. We also show that the emitted harmonics strongly depend on the character of the pumped vibrational modes. Through state-of-the-art ab initio calculations, we elucidate the physical origin of the HHG yield enhancement – phonon-assisted photoinduced carrier doping, which plays a paramount role in both intraband and interband electron dynamics. Our research illuminates a clear pathway toward comprehending phonon-mediated nonlinear optical processes within materials, offering a powerful tool to deliberately engineer and govern solid-state high harmonics.

Prediction of reservoir properties and fluids using rock physics techniques in the Rio Del Rey Basin, Cameroon

The application of rock physics modeling has been a breakthrough in oil and gas industry and has greatly managed exploration and interpretation risk. In the Rio del Rey Basin, the rock physics method was used to ascertain the reservoir's characteristics and relate them to the elastic characteristics. Using conventional techniques to connect to the elastic properties and assess various rock physics diagnostics, the estimated petrophysical parameters were water saturations, porosity, and shale volume. By connecting the saturated bulk modulus to its porosity, the bulk modulus of the porous frame, the bulk modulus of the mineral matrix, and the bulk modulus of the pore filling fluids, the Gassmann fluid replacement modeling was used to simulate various fluid scenarios. Finally, an evaluation was conducted on rock physics boundaries and cement models. The reservoirs are rated as extremely good based on the results. The rock physics model generated showed the friable sand model is a suitable model for the said basin and the rock physics bounds that were evaluated indicated that the sediments are deposited below the critical porosity and less compacted thereby depicting a soft sand model. Conventional petrophysical study would not be able to reveal the clustering of the gas sands from the brine sand and the shale zone in connection to the geologic trend as demonstrated by the rock physics template. The anticipated model will be useful to extrapolate the study to other sedimentary basins in Cameroon and estimate porosity from the impedance acquired from seismic data throughout the basin.

Hydrocarbon prospective study using seismic inversion and rock physics in an offshore field, Niger Delta

This study integrates seismic inversion and rock physics techniques to evaluate the hydrocarbon potential of an offshore field in the Niger Delta. Five wells revealed three reservoir sands with favourable reservoir properties, including gross thickness (49.2–81.4 m), porosity (0.18–0.2), permeability (565–1481 mD), and water saturation (0.16–0.54). A robust wavelet extraction process was implemented to guide seismic inversion, and a well log-centric approach was employed to validate the resulting acoustic impedance data. Rock physics analysis established correlations between acoustic impedance (Zp), porosity, fluid content, and lithology, enabling the identification of hydrocarbon-filled sands, brine-saturated sands, and shales. These relationships enabled the discrimination of hydrocarbon-filled sands [5000–8000 (m/s)(g/cc)], from brine-saturated sands [5600–8400 (m/s)(g/cc)], and shales [5000–9000 (m/s)(g/cc)] within the inverted seismic data. The inverted acoustic impedance section showed a general increase with depth, reflecting the typical compaction effects in the Niger Delta. Analysis of the impedance distribution across horizon time slices revealed prospective zones with low impedance values [below 6300 (m/s)(g/cc)], particularly in horizons 1 and 2. These newly identified zones exhibit the strongest potential for hydrocarbon accumulation and warrant further investigation. This study demonstrates the effectiveness of using well log and rock physics constrained seismic inversion for hydrocarbon exploration in an offshore field in the Niger Delta.

Ranking species in complex ecosystems through nestedness maximization

Identifying the rank of species in a complex ecosystem is a difficult task, since the rank of each species invariably depends on the interactions stipulated with other species through the adjacency matrix of the network. A common ranking method in economic and ecological networks is to sort the nodes such that the layout of the reordered adjacency matrix looks maximally nested with all nonzero entries packed in the upper left corner, called Nestedness Maximization Problem (NMP). Here we solve this problem by defining a suitable cost-energy function for the NMP which reveals the equivalence between the NMP and the Quadratic Assignment Problem, one of the most important combinatorial optimization problems, and use statistical physics techniques to derive a set of self-consistent equations whose fixed point represents the optimal nodes’ rankings in an arbitrary bipartite mutualistic network. Concurrently, we present an efficient algorithm to solve the NMP that outperforms state-of-the-art network-based metrics and genetic algorithms. Eventually, our theoretical framework may be easily generalized to study the relationship between ranking and network structure beyond pairwise interactions, e.g. in higher-order networks.

Deep quantum graph dreaming: deciphering neural network insights into quantum experiments

Despite their promise to facilitate new scientific discoveries, the opaqueness of neural networks presents a challenge in interpreting the logic behind their findings. Here, we use a eXplainable-AI technique called inception or deep dreaming, which has been invented in machine learning for computer vision. We use this technique to explore what neural networks learn about quantum optics experiments. Our story begins by training deep neural networks on the properties of quantum systems. Once trained, we ‘invert’ the neural network—effectively asking how it imagines a quantum system with a specific property, and how it would continuously modify the quantum system to change a property. We find that the network can shift the initial distribution of properties of the quantum system, and we can conceptualize the learned strategies of the neural network. Interestingly, we find that, in the first layers, the neural network identifies simple properties, while in the deeper ones, it can identify complex quantum structures and even quantum entanglement. This is in reminiscence of long-understood properties known in computer vision, which we now identify in a complex natural science task. Our approach could be useful in a more interpretable way to develop new advanced AI-based scientific discovery techniques in quantum physics.

The decimation scheme for symmetric matrix factorization

Matrix factorization is an inference problem that has acquired importance due to its vast range of applications that go from dictionary learning to recommendation systems and machine learning with deep networks. The study of its fundamental statistical limits represents a true challenge, and despite a decade-long history of efforts in the community, there is still no closed formula able to describe its optimal performances in the case where the rank of the matrix scales linearly with its size. In the present paper, we study this extensive rank problem, extending the alternative ‘decimation’ procedure that was recently introduced by Camilli and Mézard, and carry out a thorough study of its performance. Decimation aims at recovering one column/line of the factors at a time, by mapping the problem into a sequence of neural network models of associative memory at a tunable temperature. The main advantage of decimation is that its theoretical performance can be studied using statistical physics techniques. In this paper we provide the general analysis applying to a large class of compactly supported priors and we show that the replica symmetric free entropy of the neural network models takes a universal form in the low temperature limit. We then study the decimation phase diagrams in two concrete cases, the sparse Ising prior and the uniform prior, for which we show that matrix factorization is theoretically possible when the ration P/N of rank to variable is below a certain threshold. This suggest that a possible route to solving algorithmically the matrix factorization problem could be to find efficient decimation algorithms; in this respect, we show that simple simulated annealing, though being effective for limited signal sizes, is not scalable.

Medical Physics.

Medical physics is a branch of applied physics that is dedicated to the application of physics principles and techniques to the field of medicine. It plays a crucial role in ensuring the safety and effectiveness of medical procedures and contributes to advancements in the diagnosis and treatment of various medical conditions. Medical physics has its roots in the early discoveries of X-rays by Wilhelm Roentgen and the radioactive properties of certain elements, notably those discovered by Marie Curie.

Development of High Energy Physics and Prospects forDark Matter Searches at the HL-LHC

This document outlines the fundamental theory of the Standard Model and the aims and anticipated upgrades of current experimental techniques in high-energy physics. Motivations and requirements for the search for new physics, specifically on searching for dark matter, at the HL-LHC are analyzed and presented. Methodology, constraints, and predicted sensitivity of yields for searching dark matter, based on analyses of simplified models with j+MET, DM+tt,DM+Wt, DM+bb in their final states, are presented.

Berry: A code for the differentiation of Bloch wavefunctions from DFT calculations

Density functional calculation of electronic structures of materials is one of the most used techniques in theoretical solid state physics. These calculations retrieve single electron wavefunctions and their eigenenergies.The berry suite of programs amplifies the usefulness of DFT by ordering the eigenstates in analytic bands, allowing the differentiation of the wavefunctions in reciprocal space. It can then calculate Berry connections and curvatures and the second harmonic generation conductivity.The berry software is implemented for two dimensional materials and was tested in hBN and InSe. In the near future, more properties and functionalities are expected to be added. Program summaryProgram Title: berryCPC Library link to program files:https://doi.org/10.17632/mpbbksz2t7.1Developer's repository link:https://github.com/ricardoribeiro-2020/berryLicensing provisions: MITProgramming language: Python3Nature of problem: Differentiation of Bloch wavefunctions in reciprocal space, numerically obtained from a DFT software, applied to two dimensional materials. This enables the numeric calculation of material's properties such as Berry geometries and Second Harmonic conductivity.Solution method: Extracts Kohn-Sham functions from a DFT calculation, orders them by analytic bands using graph and AI methods and calculates the gradient of the wavefunctions along an electronic band.Additional comments including restrictions and unusual features: Applies only to two dimensional materials, and only imports Kohn-Sham functions from Quantum Espresso package.

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems

Autoregressive Neural Networks (ARNNs) have shown exceptional results in generation tasks across image, language, and scientific domains. Despite their success, ARNN architectures often operate as black boxes without a clear connection to underlying physics or statistical models. This research derives an exact mapping of the Boltzmann distribution of binary pairwise interacting systems in autoregressive form. The parameters of the ARNN are directly related to the Hamiltonian’s couplings and external fields, and commonly used structures like residual connections and recurrent architecture emerge from the derivation. This explicit formulation leverages statistical physics techniques to derive ARNNs for specific systems. Using the Curie–Weiss and Sherrington–Kirkpatrick models as examples, the proposed architectures show superior performance in replicating the associated Boltzmann distributions compared to commonly used designs. The findings foster a deeper connection between physical systems and neural network design, paving the way for tailored architectures and providing a physical lens to interpret existing ones.

Direct Measurement of the Internal Pressure of Ultrafine Bubbles Using Radioactive Nuclei

Direct Measurement of the Internal Pressure of Ultrafine Bubbles Using Radioactive Nuclei

Reservoir Characterization: Enhancing Accuracy through Advanced Rock Physics Techniques

Reservoir Characterization: Enhancing Accuracy through Advanced Rock Physics Techniques

A probabilistic model for the efficiency of cosmic-ray radio arrays

Digital radio detection of cosmic-ray air showers has emerged as an alternative technique in high-energy astroparticle physics. Estimation of the detection efficiency of cosmic-ray radio arrays is one of the few remaining challenges regarding this technique. To address this problem, we developed a new approach to model the efficiency based on the explicit probabilistic treatment of key elements of the radio technique for air showers: the footprint of the radio signal on ground, the detection of the signal in an individual antenna, and the detection criterion on the level of the entire array. The model allows for estimation of sky regions of full efficiency and can be used to compute the aperture of the array, which is essential to measure the absolute flux of cosmic rays. We also present a semi-analytical method that we apply to the generic model, to calculate the efficiency and aperture with high accuracy and reasonable calculation time. The model in this paper is applied to the Tunka-Rex array as example instrument and validated against Monte Carlo simulations. The validation shows that the model performs well, in particular, in the prediction of regions with full efficiency. It can thus be applied to other antenna arrays to facilitate the measurement of absolute cosmic-ray fluxes and to minimize a selection bias in cosmic-ray studies.