Papers

We investigate the optimization of epsilon-security parameters in quantum key distribution (QKD), aiming to improve the achievable secure key rate under a fixed overall composable security level. For this purpose, we employ a continuous genetic algorithm (CGA) to optimize the epsilon-security components of two representative protocols: the homodyne protocol from the continuous-variable (CV) family and the BB84 protocol from the discrete-variable (DV) family. We detail the CGA configuration, summarize the derivation of the composable key rate, and emphasize the role of the epsilon-parameters in both protocols. We then compare key rates obtained with optimized epsilon-values against those derived from standard and randomized choices. Our results demonstrate substantial key rate improvements at high security levels, where the key rate typically vanishes, and uncover positive-rate regimes that are inaccessible without optimization.

We report on the design and demonstration of ultra-wide bandgap AlGaN polarization-graded field effect transistors with ultra-thin channels to enable excellent current density and high-frequency performance while significantly reducing thermal resistance. We use polarization-graded AlGaN layers and ultra-thin pseudomorphic AlGaN buffer layers to enable low thermal resistance and excellent structural quality. The polarization-graded field effect transistors (PolFETs) demonstrated here show Imax over 800mA/mm and current/power gain cutoff frequency (fT/fmax) of 26/28 GHz. Small signal modeling and analysis were used to determine parasitic/transit delays, and gate-resistance thermometry was implemented to thermally characterize AlGaN PolFET and benchmark against state-of-the-art AlGaN HEMTs. The ultra-thin AlGaN PolFET showed thermal resistance of 12 K.mm/W, representing a significant reduction from typical AlGaN transistors. These results show state-of-art combination of high current density, excellent fT-LG product for ultra-wide bandgap AlGaN transistors, and superior thermal performance, and highlight the promise of AlGaN transistors for future RF and mm-wave applications.

We reformulate the CHSH game in terms of indivisible stochastic processes. Using Barandes's stochastic-quantum correspondence and its associated definition of causal locality, we present a novel proof of the Tsirelson bound. In particular, we show that unlike the no-signaling principle alone, the postulates defining causally local, indivisible stochastic processes are precisely strong enough to allow for violations of the Bell inequality up to, but not beyond, the Tsirelson bound.

We report the design and demonstration of ultra-wide-bandgap (UWBG) AlGaN polarization-graded field-effect transistors (PolFETs) that achieve a current-gain cutoff frequency above 85 GHz and a current density exceeding 1.3 A/mm. Ultra-thin channel and buffer layers were grown epitaxially on AlN substrates, and a reverse-graded AlGaN contact layer was incorporated to reduce the contact resistance to below 1 ohm.mm. With aggressively scaled device dimensions, the AlGaN PolFETs exhibit state-of-the-art high-frequency performance for UWBG transistors. Small-signal modeling reveals both parasitic and transit delays, confirming the benefits of reduced access resistance and enhanced intrinsic transconductance. These results establish a new performance benchmark for UWBG AlGaN devices and demonstrate their strong potential for next-generation millimeter-wave electronics.

The MCPlas toolbox represents a collection of MATLAB functions for the automated generation of an equation-based fluid-Poisson model for non-thermal plasmas in the multiphysics simulation software COMSOL. Following the development of the new generation of the LXCat platform, all input data are prepared in a structured and interoperable JSON format and can be supplied and validated using existing JSON schemas. The toolbox includes fully transparent, editable MATLAB source code and offers an advanced description of electron transport in addition to commonly used approaches in the plasma modelling community. It supports one-dimensional and two-dimensional modelling geometries employing Cartesian, polar and cylindrical coordinate systems. MCPlas is tested on two reference cases: DC- and RF-driven low-pressure glow discharges in argon. Comparison of MCPlas results with results obtained by employing COMSOL's Plasma Module verifies the reliability of the plasma model implemented by MCPlas and demonstrates the significance of electron transport treatment and boundary conditions applied in the toolbox. Using the same examples, the easy handling of complex reaction kinetic models in MCPlas and the reusability of its JSON input data across different modelling platforms are illustrated. This demonstrates that MCPlas provides a transparent and reproducible workflow for the simulation of non-thermal plasmas using COMSOL.

Interfaces play a crucial role in energy transport at the nanoscale. However, direct experimental observations of interfacial thermal conductance across molecular junctions have remained challenging due to the high spatiotemporal resolution required for probing. Here, we report dynamic energy transport processes across multi-component molecular junctions observed at the atomic level by employing reflection ultrafast electron diffraction. A clear temporal sequence of energy transfer is revealed at early times following photoexcitation of Au(111) surfaces chemically bonded with self-assembled monolayers (SAMs) of alkanethiols: from the gold surface layer (SL) to the head groups of a SAM and then to the CH2-CH2 methylene lattice. Remarkably, the structural dynamics of the gold SL differ significantly from those of clean gold. Furthermore, the methylene lattice dynamics exhibit chain-length insensitivity but with a length-dependent retention time to reach full thermalization. Quantitatively, we find an agreement in the increased out-of-plane atomic motions between the surface gold atoms and the SAM methylene units, signifying the nature of motion-based coupling for interfacial energy transport. High interfacial thermal conductance (~300 MW/m^2/K) and high thermal conductivity for the methylene lattice (~60 W/m/K) are obtained under the condition of impulsive heating, which provide strong support for previous room-temperature theoretical predictions and also the ballistic nature of intrachain heat transfer. This time- and spatially-resolved experimental approach will enable future quantitative assessment of interfacial energy transport across solid-molecule junctions.

Small and device-dependent valley splittings remain a key challenge for electron spin qubits in silicon (Si), directly limiting qubit fidelity, device uniformity, and the scalability of Si-based quantum processors. In silicon-germanium (SiGe) heterostructures, this problem can be addressed through engineering of the epitaxial layer stack. Several heuristic strategies have been proposed to enhance the energy gap between the two nearly degenerate valley states in strained Si/SiGe quantum wells (QWs), e.g., sharp Si/SiGe interfaces, Ge spikes, or oscillating Ge concentrations within the QW. Here, we develop a systematic variational optimization approach to compute optimal Ge concentration profiles that enhance selected properties of the intervalley coupling matrix element. Our free-shape optimization framework is augmented by realistic technological constraints to ensure feasibility of the resulting epitaxial profiles and is based on an effective-mass envelope-function theory accounting for strain and compositional alloy disorder. Previously proposed heterostructure designs are recovered as special cases of the constrained optimization problem. Our main result is a novel heterostructure design, which we refer to as the "modulated wiggle well", providing both a large deterministic enhancement of the valley splitting and a strong suppression of disorder-induced variability. In addition, this design enables wide electrical tunability of the valley splitting - from approximately $200\,\mu \text{eV}$ to above $1\,\text{meV}$ - offering new opportunities for engineering robust and switchable silicon qubits.

Achieving both high internal efficiency and fast reset times at elevated temperatures remains challenging due to limited understanding of how film properties govern SNSPD performance. We demonstrate that epitaxial NbN films on sapphire enable simultaneous high efficiency and rapid response. We fabricate and characterize SNSPDs based on these films deposited via DC magnetron sputtering on c-cut sapphire. High-quality epitaxial growth preserves a low electron diffusion coefficient and promotes strong electron-phonon coupling, yielding a high critical temperature and efficient hotspot formation in the dirty limit. X-ray diffraction and transmission electron microscopy confirm epitaxial alignment and lattice order. Nanowires of 20 nm width exhibit saturated internal efficiency at 1550 nm wavelength and short reset times at 4.2 K, enabled by lattice matching and high thermal conductance of the sapphire interface. Ab initio modeling reproduces photon count rates, linking device performance quantitatively to film properties such as diffusivity and electron-phonon coupling.

Edge fluid modeling of the first divertor-plasma detachment experiments in negative triangularity discharges on DIII-D is presented using the 2D multi-fluid code UEDGE, including cross-field particle drifts. Density scans are performed to reproduce the experimental roll-over of the outer-target ion saturation current and to investigate detachment physics for both forward and reverse toroidal magnetic field configurations. Consistent with experiments, the simulations show that approximately 40% higher density is required to reach detachment onset for forward BT compared to reverse BT, and that deep detachment is not achieved for reverse BT. Comparisons with positive triangularity Ohmic discharges further demonstrate that negative triangularity requires substantially higher densities, at or above the Greenwald limit, to access detachment. The modeling indicates that the increased difficulty of achieving detachment in negative triangularity arises from a shorter midplane-to-target connection length, a reduced outer divertor leg length, and lower cross-field transport compared to positive triangularity configurations.

Field line tracing is one of the fundamental computational tools used in the study of the magnetosphere, which helps in many areas including footprint mapping, connectivity analysis and real-time visualisation. This note describes an implementation approach to error-bounded adaptive integration of the field line differential equation (ODE), where an embedded Runge-Kutta pair is used in conjunction with event detection, allowing robust localisation of footprint locations on spacecraft. The method is validated against an analytic dipole field model as well as an international geomagnetic reference field (IGRF)-based geomagnetic configuration.

Why do physicists almost universally take the direction of positive rotation to be counterclockwise, and three-dimensional coordinates to be right-handed? This paper traces the historical development of these chiral conventions, with an emphasis on the physical quantity whose direction became the focal point of this discussion in the mid-1800s, the magnetic field. Though these standards are often reduced to mere mathematical, inconsequential choices, an analysis of the impact of Newton, Maxwell, the London Mathematical Society, and others toward the subject can enhance classroom discussion, not only as a contextual sidebar, but also by emphasizing the influence conventions in physics can have on pedagogy, communication, and scientific advancement.

The lack of long-range electrostatics is a key limitation of modern machine learning interatomic potentials (MLIPs), hindering reliable applications to interfaces, charge-transfer reactions, polar and ionic materials, and biomolecules. In this Perspective, we distill two design principles behind the Latent Ewald Summation (LES) framework, which can capture long-range interactions, charges, and electrical response just by learning from standard energy and force training data: (i) use a Coulomb functional form with environment-dependent charges to capture electrostatic interactions, and (ii) avoid explicit training on ambiguous density functional theory (DFT) partial charges. When both principles are satisfied, substantial flexibility remains: essentially any short-range MLIP can be augmented; charge equilibration schemes can be added when desired; dipoles and Born effective charges can be inferred or finetuned; and charge/spin-state embeddings or tensorial targets can be further incorporated. We also discuss current limitations and open challenges. Together, these minimal, physics-guided design rules suggest that incorporating long-range electrostatics into MLIPs is simpler and perhaps more broadly applicable than is commonly assumed.

We investigate dynamic crack propagation and fragmentation with the phase-field fracture approach. The method was chosen for its ability to yield crack paths that are independent of the underlying mesh, thanks to the damage regularization zone. In dynamics, we observe a progressive widening of this regularization zone and attribute it to an unphysical trapping of elastic waves. We show that the damage zones do not represent free boundaries accurately and that wave interactions induce additional damage. We reveal how mass erosion, by conserving the elastic wave speed in the damaged regions, can be used to efficiently reduce the spurious diffusion of damage. Furthermore, we provide numerical evidence that dynamically propagating cracks in the phase-field formulation, both with and without mass erosion, converge to the predictions of linear elastic fracture mechanics. For vanishing regularization length, the crack speed and energy release rate become independent of the phase-field regularization length, provided that this length scale is small enough and the mesh fine enough to resolve the process zone.

In the bounded confidence model the opinions of a set of agents evolve over discrete time steps. In each round an agent averages the opinion of all agents whose opinions are at most a certain threshold apart. Here we assume that the opinions of the agents are elements of the real line. The details of the dynamics are determined by the initial opinions of the agents, i.e. a starting configuration, and the mentioned threshold – both allowing uncountable infinite possibilities. Recently it was observed that for each starting configuration the set of thresholds can be partitioned into a finite number of intervals such that the evolution of opinions does not depend on the precise value of the threshold within one of the intervals. So, we may say that, given a starting configuration of initial opinions, there is only a finite number of equivalence classes of bounded confidence processes (and an algorithm to compute them). Here we systematically study different notions of equivalence. In our widest notion we can also get rid of the initial starting configuration and end up with a finite number of equivalent bounded confidence processes for each given (finite) number of agents. This allows to precisely study the occurring phenomena for small numbers of agents without the jeopardy of missing interesting cases by performing numerical experiments. We exemplarily study the freezing time, i.e. number of time steps needed until the process stabilizes, and the degree of fragmentation, i.e. the number of different opinions that survive once the process has reached its final state.

This paper serves as a practical guide for individuals and organisations seeking to design, implement, and evaluate astronomy-for-development initiatives, as well as those preparing proposals for the International Astronomical Union's Office of Astronomy for Development (IAU OAD) annual Call for Proposals. The paper aims to outline how systematic evidence collection can strengthen project design, enhance accountability, and increase the likelihood of measurable impact. It explains the distinction between monitoring and evaluation, provides guidance on when and how evaluation should be undertaken, and summarises key evaluation types - process, feasibility, impact, outcome, economic, and summative, relevant to astronomy-based interventions. In addition to conceptual discussion, the paper presents a set of practical steps, reflective questions, and examples to help project teams develop a clear theory of change, define appropriate indicators, and anticipate risks and unintended consequences. By contextualising M&E within the broader goals of the OAD and the Sustainable Development Goals (SDGs), this work aims to empower practitioners to create evidence-informed, community-driven, and sustainable astronomy-for-development projects that deliver both local and global benefit.

This paper presents a comprehensive numerical investigation of a NACA0012 undergoing Stall Flutter Limit Cycle Oscillations (LCO) across distinct fluid dynamics regimes. It accurately models Small Amplitude Oscillations (SAO) in the transitional Reynolds regime and Large Amplitude Oscillations (LAO) in the moderate regime, observed in different experimental campaigns. The SAO analysis serves as a verification of the computational framework against established numerical benchmarks. Crucially, the LAO simulations represent the first documented prediction across the full experimental velocity range correlated against available measured data, addressing a significant literature gap. The predictions fidelity relies on rigorous computational criteria defined through a detailed sensitivity analysis. This demonstrated numerical requirements significantly more demanding than those typically employed for computing static polars or simulating dynamic pitching motion of rigid airfoils, underscoring the severity of the aeroelastic problem. Quantitatively the simulation systematically over-predicts the critical onset velocity and under-predicts the LCO amplitudes.However, the results show strong qualitative agreement with experimental observations, successfully reproducing key dynamic stall mechanics and bifurcation phenomena.

For a century, quantum theory has posed a fundamental challenge to philosophical thinking. On its face, it repudiates many of the key features of the mechanical conception of physical reality. However, the challenge of developing a precise, coherent alternative to that conception has yet to be met. Here, I argue that a major hindrance to the project of quantum interpretation is its existing interpretative methodologies, which suffer from a lack of systematicity in their judgements about what aspects of the theory are interpretational relevant. In particular, I argue that current interpretations tend to marginalize the informal part of the theory in favour of its formal part, and place inappropriate emphasis on the natural language component of the formalism over its detailed mathematical structure. To counterbalance these biases, I propose that an interpretation-free zone be constructed around the theory, wherein an interpreter initially adopt a descriptive stance which considers all parts of the theory, and that the results of this deliberation~(and the judgements about what facts are interpretationally relevant) are reported as part of their interpretation. I argue that the interpretation of quantum theory poses special challenges and difficulties which necessitate this interpretation-free zone, and that existing interpretative methodologies are insufficient to address them. Further, I argue that a reconstructive interpretative methodology, which harnesses the recent results of the quantum reconstruction program, provides a powerful means to identify almost all facts that could be interpretationally relevant, and naturally meets these challenges and difficulties. Moreover, I argue that the quantum reconstruction program offers a powerful way to discover new physical principles, and offers a systematic pathway to build a rich, coherent conception of quantum reality.

One of the core strategies to reduce cartel violence is by directly targeting members with law enforcement. Whether targeting leaders, disrupting parts of the organisation, or incarcerating members, the purpose is to reduce the strength of cartels directly. Most security strategies result in increased incarceration rates. Yet its effectiveness in addressing organised crime remains unclear, particularly if it fails to prevent recidivism upon release from jail. Here, a model is constructed to quantify cartel participation across generations, where individuals are recruited, age over time, and exit cartels as victims of a homicide or due to incapacitation, or retirement. Incarcerating cartel members prevents less than 10% of cartel offences. Additionally, doubling penalties would reduce cartel members' potential by less than 5%, thereby challenging proposals for stricter rules. Yet, rehabilitation after prison, often neglected as an integral part of the security strategy, could be more effective in lowering cartel crimes.

We propose physics-informed neural networks (PINNs) as a numerical solver for relativistic spin hydrodynamics and demonstrate that the total angular momentum, i.e., the sum of orbital and spin angular momentum, is accurately conserved throughout the fluid evolution by imposing the conservation law directly in the loss function as a training target. This enables controlled numerical studies of the mutual conversion between spin and orbital angular momentum, a central feature of relativistic spin hydrodynamics driven by the rotational viscous effect. We present two physical scenarios with a rotating fluid confined in a cylindrical container: one case in which initial orbital angular momentum is converted into spin angular momentum in analogy with the Barnett effect, and the opposite case in which initial spin angular momentum is converted into orbital angular momentum in analogy with the Einstein-de Haas effect. We investigate these conversion processes governed by the rotational viscous effect by analyzing the spacetime profiles of thermal vorticity and spin potential. Our PINNs-based framework provides the first numerical evidence for spin-orbit angular momentum conversion with fully nonlinear computational relativistic spin hydrodynamics.

This paper analyzes the performance of spectro-temporal unitary transforms for coherent optical modulation. Unlike conventional IQ modulation, such transforms are based on a cascade of phase modulators and dispersive elements, so are theoretically lossless and not limited by the bandwidth of the constituent modulators. We analyse the performance limits and design trade-offs of this scheme: estimating how the number of stages, amount of dispersion, modulator bandwidth, symbol block length and electrical signal power impacts the achievable signal-to-distortion ratio (SDR). Importantly, we show that high (>30 dB) SDRs suitable for modern >200 GBd class coherent optical communications are achievable with a low (<6) number of stages and reasonable parameters for driver power, modulator bandwidth and on-chip dispersion. Finally we address the SDR penalties associated with potential phase, amplitude, or dispersion errors, and limited DAC resolution.

Realizing and controlling the unconventional pairing featured by topological superconductors remains a central challenge. We introduce a cavity QED quantum simulator that engineers competing chiral $p_x+ip_y$ and $d_{x^2-y^2}+id_{xy}$ orders by tailoring cavity-mediated couplings between atomic pseudospins that emulate momentum-dependent pairing channels. The desired spatially inhomogeneous cavity-mediated couplings can be engineered in a 2D optical lattice using incommensurate cavity-lattice wavelengths naturally occurring in cavity QED systems. This minimal and fully tunable platform enables controlled state preparation and continuous measurement of superconducting order parameters. To probe this competition, the platform enables selective excitation and direct time-domain detection of Bardasis-Schrieffer modes—collective excitations of the subdominant pairing channel. The same competing channel can have an even stronger impact out of equilibrium, where a sudden quench can drive its growth and abruptly reshape the dominant superconducting order, despite the stability of the dominant order in equilibrium. Together, these results establish cavity QED as a controlled setting for revealing hidden competing orders and exploring nonlinear dynamics of topological superconductivity that remain elusive in solid-state and ultracold-atom systems.

New free-swimming experiments and simulations are conducted on a pair of three-dimensional, bio-robotic swimmers composed of a body and tail section based on Yellowfin tuna, Thunnus albacares. It is discovered that the pair converges spontaneously to a side-by-side schooling formation that is stable to perturbations in the swimming direction at a fixed lateral spacing. We reveal that for close lateral spacings of 43% of the body length and thick, tuna-like bodies with a 22% thickness-to-length ratio, the flow between the swimmers is accelerated in a "channeling effect" due to flow constriction. Consequently, this creates a low-pressure zone that is the primary mechanism generating a fluid-mediated restorative force, thereby making the side-by-side formation hydrodynamically stable. This quasi-steady mechanism makes the stability of the formation insensitive to the phase synchronization between the bio-robots in contrast to previous results for schooling foils. Moreover, in the side-by-side formation tunalike swimmers are seen to have only a small reduction in their swimming speed and a concurrent small rise in their cost of transport. By leveraging this channeling effect, bio-robotic schools may be able to maintain a schooling formation with little or no control. This flow mechanism may also be present in biological schools of tuna-like fish where it may sculpt the formations observed in nature.

Digital lasers control the laser beam by dynamically updating the phase patterns of the spatial light modulator (SLM) within the laser cavity. Due to the presence of nonlinear effects, such as mode competition and gain saturation in digital laser systems, it is often necessary to rely on specifically manually tailored approach or iteration processes to find suitable loaded phases in Digital lasers. This study proposes a model based on Conditional Generative Adversarial Networks (cGAN) and a modified U-Net architecture, with designed loss functions to inverse design the loaded phases. In this work, we employ deep neural networks to learn the nonlinear effects in simulated L-shape digital lasers, enabling the prediction of SLM-loaded phases for both analytical and non-analytical arbitrary structured light fields. The results demonstrate superior performance on non-analytical light fields compared to the current methods in L-shape Digital lasers. Furthermore, a transfer learning strategy is introduced, allowing knowledge obtained from one class of structured beams to be effectively reused for another, thereby enhancing generalization and improving performance under limited training data. Importantly, this method, the first proposed learning framework for digital lasers, is not limited to the L-shaped digital lasers discussed in this study, providing an efficient alternative for generating structured light in other digital laser systems.

Terrain-following coordinates in atmospheric models often imprint their grid structure onto the solution, particularly over steep topography, where distorted coordinate layers can generate spurious horizontal and vertical motion. Standard formulations, such as hybrid or SLEVE coordinates, mitigate these errors by using analytic decay functions controlled by heuristic scale parameters that are typically tuned by hand and fixed a priori. In this work, we propose a framework to define a parametric vertical coordinate system as a learnable component within a differentiable dynamical core. We develop an end-to-end differentiable numerical solver for the two-dimensional non-hydrostatic Euler equations on an Arakawa C-grid, and introduce a NEUral Vertical Enhancement (NEUVE) terrain-following coordinate based on an integral transformed neural network that guarantees monotonicity. A key feature of our approach is the use of automatic differentiation to compute exact geometric metric terms, thereby eliminating truncation errors associated with finite-difference coordinate derivatives. By coupling simulation errors through the time integration to the parameterization, our formulation finds a grid structure optimized for both the underlying physics and numerics. Using several standard tests, we demonstrate that these learned coordinates reduce the mean squared error by a factor of 1.4 to 2 in non-linear statistical benchmarks, and eliminate spurious vertical velocity striations over steep topography.

Understanding the fundamental properties that dictate photoexcited polarons in materials is critical to tuning their properties. Theoretical models of polarons have only recently been extended to the excited state. Experimental measurements of polaron formation and transport have been widely undertaken across a range of materials, from photocatalysts and superconductors to soft conducting polymers. Here, we map thermalized excited state experimental measurements of quantities such as polaron strength onto phase diagrams of the Holstein, Hubbard-Holstein, and t-J-Holstein models. This work demonstrates that tuning electron-phonon coupling strength, electron localization, and spin exchange can be leveraged to suppress or control polarons in transition metal oxides. We find that the t-J-Holstein model provides the clearest qualitative description of electron polarons in the measured iron oxides and could be generally applied to a wide range of systems that exhibit polaron formation in the excited state. This work combines experimental data with ground state models to provide a qualitative parameter space for informing photoexcited electron polaron design, under which excited state polaronic behavior can be classified within ground-state calculable models.

This paper demonstrates a novel method to extract photomultiplier tube (PMT) calibration timing constants in large liquid scintillation detectors from physics data using the machinery of unsupervised deep learning. The approach uses a simplified physical model of optical photon transport in the loss function, with PMT calibration constants treated as free parameters, and the simple assumption that individual events represent point-like emission. The problem is, thus, effectively reduced to that of regression on a very large scale, made tractable by deep learning architectures and automatic differentiation frameworks. Using data from the 9,300 PMTs in the SNO+ detector, the method has been shown to reliably extract 3 calibration constants for each of the over 7,500 online PMTs using radioactive background events. We believe that this basic approach can be straightforwardly generalised for a wide range of applications.

We introduce the two-point propagation field (TPPF), a real-valued, phase-sensitive quantity defined as the functional derivative of the single-photon detection probability with respect to an infinitesimal opaque perturbation placed between the source and detection slits. The TPPF is analytically derived and shown to exhibit a stable, high-frequency sinusoidal structure with periods of 4~7 nm near the X-ray detection slit. This structure enables shot-noise-limited displacement detection with $\sim200 pm$ precision for 6 keV X-rays, using total photon counts on the order of $1\times10^{7}$ and detector photon counting as low as 287. Beyond displacement detection, the TPPF physically performs a Fourier-Radon transformation of the projection data, providing a pathway to non-iterative frequency-domain tomography. Two conceptual strategies, a central blocker and off-axis multi-slit arrays, are estimated to lower the required incident photon budget by more than one order of magnitude each, yielding combined reductions of two to three orders of magnitude with near-term detector development. The TPPF concept, originally developed in a perturbative study of single-particle propagation, bridges quantum measurement questions with practical high-resolution X-ray physics. This work provides the foundational physics required for future discrete sampling and 3D numerical reconstruction algorithms.

We introduce an entanglement witness that identifies off-diagonal long-range order (ODLRO) – a distinctive form of entanglement – in systems containing both fermionic and bosonic particles. By analyzing the particle-hole reduced density matrices of each subsystem, the approach detects ODLRO independently in both fermionic and bosonic sectors and identifies when long-range order develops across the entire mixed-particle system. The witness also quantifies the magnitude of ODLRO within each particle type, revealing how fermionic and bosonic correlations combine to form the total entanglement of the system, including a bosonic condensation of particle-hole pairs driven by many-body correlations rather than particle statistics. Using the Lipkin-Meshkov-Glick spin model, we show how the transition from ODLRO localized to one particle type to ODLRO shared by both particle types captures the onset of collective entanglement in a mixed-particle environment, providing new insight into systems where fermionic and bosonic correlations coexist.

We extend the active learning emulators for two-body scattering in coordinate space with error estimation, recently developed by Maldonado et al. [Phys. Rev. C 112, 024002], to coupled-channel scattering in momentum space. Our full-order model (FOM) solver is based on the Lippmann-Schwinger integral equation for the scattering $t$-matrix as opposed to the radial Schrödinger equation. We use (Petrov-)Galerkin projections and high-fidelity calculations at a few snapshots across the parameter space of the interaction to construct efficient reduced-order models (ROMs), trained by a greedy algorithm for locally optimal snapshot selection. Both the FOM solver and the corresponding ROMs are implemented efficiently in Python using Google's JAX library. We present results for emulating scattering phase shifts in coupled and uncoupled channels and cross sections, and assess the accuracy of the developed ROMs and their computational speedup factors. We also develop emulator error estimation for both the $t$-matrix and the total cross section. The software framework for reproducing and extending our results is publicly available. Together with our recent advances in developing active-learning emulators for three-body scattering, these emulator frameworks set the stage for full Bayesian calibrations of chiral nuclear interactions and optical models against scattering data with quantified emulator errors.

We show that the photoelectron angular distributions produced by elliptical and cross-polarized two-color laser fields interacting with randomly oriented chiral molecules decompose into four irreducible representations of the $D_{2h}$ point group. One of these ($A_u$) corresponds to a non-dichroic enantiosensitive (NoDES) contribution. This NoDES contribution has opposite sign for opposite enantiomers but remains invariant under reversal of the field ellipticity, enabling chirality detection that is robust against variations of the relative phase between orthogonal field components. We propose a protocol to isolate this component using only two velocity-map imaging projections and validate it through numerical simulations. Our calculations, performed in the two-photon resonantly-enhanced ionization, multi-photon, and strong-field ionization regimes with cross-polarized two-color fields show that the NoDES signal reaches about 1% of the energy-resolved ionization yield, comparable to photoelectron circular dichroism and much larger than standard magnetic-dipole chiroptical effects. NoDES spectroscopy thus provides a symmetry-protected and phase-robust route to probe molecular chirality on the ultrafast time scale. The experimental confirmation of our theory is presented in the companion paper [L. Fede et al., arXiv:2512.19062 (2025)].