Chemical Physics
qs$GW$ quasiparticle and $GW$-BSE excitation energies of 133,885 molecules
Dario Baum, Arno Förster and Lucas Visscher
Machine learning applications in the chemical sciences, especially when based on neural networks, critically depend on the availability of large quantities of high quality data. As they provide excellent accuracy for both charged and neutral excitations, a large
dataset containing quasiparticle self-consistent GW (qs$GW$) and Bethe-Salpeter equation (BSE) data would be highly desirable to model excited state energies and properties. In this work, we introduce a
dataset for qs$GW$-BSE excitation energies and qs$GW$ quasiparticle energies of unprecedented size. Our
dataset, denoted QM9GWBSE, supplies $GW$-BSE singlet-singlet and singlet-triplet excitation energies, corresponding transition dipole moments and oscillator strengths as well as qs$GW$ quasiparticle energies for all molecules from the popular QM9
dataset. We anticipate that QM9GWBSE will provide a solid foundation to train highly accurate machine learning models for the prediction of molecular excited state properties.
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Data Analysis and Statistics · Cross-listed from quant-ph
Estimating Detector Error Models on Google's Willow
Kregg Elliot Arms, Martin James McHugh, Joseph Edward Nyhan et al. (+2 more)
We consolidate recent theoretical advances in Detector Error Model (DEM) estimation and formalize several
algorithms to learn DEM parameters and structure from syndromes without using a decoder, demonstrating recovery of known DEMs from simulated syndromes with precision limited only by finite-sample effects. We then apply these
algorithms to estimate DEMs from Google's 72- and 105-
qubit chips. Using a likelihood function that is tractable for small DEMs, we show that DEMs estimated directly from syndromes agree more closely with unseen syndromes than DEMs trained to optimize logical performance, whereas the latter outperform the former as priors for decoders in logical
memory experiments. We used a time-series of estimated DEMs to track both global error and specific local errors over the course of a QEC experiment, suggesting applications in online characterization. We employ a sequence of DEM estimation techniques to discover and quantify long-range detector correlations spanning the width of the 105-
qubit chip, for which DEM analysis suggests correlated measurement errors rather than high-weight Pauli errors as the most likely explanation. Finally, we present two artifacts in repetition code syndromes that are not well-modeled by a DEM: correlated flipping of pairs of adjacent detectors in many consecutive rounds of QEC, and signatures consistent with radiation events occurring more frequently than previously reported.
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All authors Kregg Elliot Arms, Martin James McHugh, Joseph Edward Nyhan, William Frederick Reus, James Loudon Ulrich
Data Analysis and Statistics · Cross-listed from stat.ME
Identifiable factor analysis for mixed continuous and binary variables based on the Gaussian-Grassmann distribution
Takashi Arai
We develop a factor analysis for mixed continuous and binary observed variables. To this end, we utilized a recently developed multivariate probability distribution for mixed-type random variables, the Gaussian-Grassmann distribution. In the proposed factor analysis, marginalization over latent variables can be performed analytically, yielding an analytical expression for the distribution of the observed variables. This analytical tractability allows model parameters to be estimated using standard gradient-based optimization techniques. We also address improper solutions associated with maximum likelihood factor analysis. We propose a prescription to avoid improper solutions by imposing a constraint that row vectors of the factor loading
matrix have the same norm for all features. Then, we prove that the proposed factor analysis is identifiable under the norm constraint. We demonstrate the validity of this norm constraint prescription and numerically verified the model's identifiability using both real and synthetic
datasets. We also compare the proposed model with quantification method and found that the proposed model achieves better reproducibility of correlations than the quantification method.
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Fluid Dynamics
Data-driven Pressure Recovery in Diffusers
Juan Augusto Paredes Salazar, Ankit Goel, Rowen Costich et al. (+3 more)
This paper investigates the application of a data-driven technique based on retrospective cost optimization to optimize the frequency of mass injection into an S-shaped diffuser, with the objective of maximizing the pressure recovery. Experimental data indicated that there is an optimal injection frequency between 100 Hz and 300 Hz with a mass flow rate of 1 percent of the free stream. High-fidelity numerical simulations using compressible unsteady Reynolds-Averaged Navier-Stokes (URANS) are conducted to investigate the mean and temporal features resulting from mass injection into an S-shaped diffuser with differing injection speeds and pulse frequencies. The results are compared with experiments to confirm the accuracy of the numerical solution. Overall, 2-D simulations are relatively in good agreement with the experiment, with 3-D simulations currently under investigation to
benchmark the effect of spanwise instabilities. Simulation results with the proposed data-driven technique show improvements upon a baseline case by increasing pressure recovery and reducing the region of flow recirculation within the diffuser.
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All authors Juan Augusto Paredes Salazar, Ankit Goel, Rowen Costich, Meliksah Koca, Ozgur Tumuklu, Michael Amitay
Optics
Ultrahigh-Q chiral resonances empowered by multi-head attention deep learning
Cong Zhang, Jiaju Wu, Huazheng Wu et al. (+9 more)
High quality (Q) factor optical
chiral resonators are indispensable for many
chiral photonic devices. Designing ultrahigh Q-factors in
chiral metasurfaces traditionally relies on extensive parameter scanning, which is time-consuming and inefficient. While deep learning now provides a rapid design alternative, conventional models still face challenges in accurately predicting ultrahigh Q-factor spectral characteristics. In this study, we introduce a multi-head
attention network (MuHAN) to accelerate the design of ultrahigh Q-factor optical
chiral resonators in
bilayer metasurfaces. MuHAN achieves forward spectral predictions in approximately 10ms, thousands of times faster than finite-difference time-domain simulations, boasting 99.85% and 99.9% accuracy for forward and inverse predictions, respectively. By transferring the learned physical principles, we perform inverse design of nanoscale structures with ultrahigh Q-factors (up to 2.9910E5) based on
chiral quasi-bound states in the continuum (quasi-BICs) at minimal computational cost. Our rapid design tool, based on MuHAN, enables high-performance encryption imaging, bridging deep learning with high-Q
chiral metasurfaces for advanced sensing,
laser, and detection applications.
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All authors Cong Zhang, Jiaju Wu, Huazheng Wu, Yufei Liu, Xu Yang, Na Liu, Chaoyang Wang, Peipei Chen, Chenggang Yan, Seng Yang, Xingguang Liu, Shaowei Jiang
Instrumentation and Detectors · Cross-listed from hep-ex
Modeling Light Signals Using Data from the First Pulsed Neutron Source Program at the DUNE Vertical Drift ColdBox Test Facility at CERN Neutrino Platform
A. Paudel, W. Shi, P. Sala et al. (+36 more)
In this paper, we present a first quantitative test of detected light signals produced in a pulsed
neutron source run in a small vertical drift LArTPC at the CERN
neutrino platform ColdBox test facility. The ColdBox cryostat, detectors,
neutron sources, and particle interactions are modeled and simulated using Fluka. A good agreement is found in the detected number of photoelectrons, with values below 650 photoelectrons in both data and simulation, for all four X-ARAPUCA photodetectors on the cathode in the LArTPC. A time constant is also fitted from the
neutron-beam-off light signal spectrum and found consistent between data and MC. Several important systematic effects are discussed and serve as guides for future runs at larger LArTPCs.
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All authors A. Paudel, W. Shi, P. Sala, F. Cavanna, W. Johnson, J. Wang, W. Ketchum, F. Resnati, A. Heindel, A. Ashkenazi, E. Bertholet, E. Bertolini, D. A. Martinez Caicedo, E. Calvo, A. Canto, S. Manthey Corchado, C. Cuesta, Z. Djurcic, M. Fani, A. Feld, S. Fogarty, F. Galizzi, S. Gollapinni, Y. Kermaïdic, A. Kish, F. Marinho, D. Torres Muñoz, A. Verdugo de Osa, L. Paulucci, W. Pellico, V. Popov, J. Rodriguez Rondon, D. Leon Silverio, S. Sacerdoti, H. Souza, R. C Svoboda, D. Totani, V. Trabattoni, L. Zambelli
Fluid Dynamics
The dynamics of thermalisation in the Galerkin-truncated, three-dimensional Euler equation
Rajarshi, Mohammad Saif Khan, Prateek Anand et al. (+1 more)
The inviscid, partial differential equations of hydrodynamics when projected via a Galerkin-truncation on a finite-dimensional subspace spanning wavenumbers $-{\bf K}_{\rm G} \le {\bf k} \le {\bf K}_{\rm G}$, and hence retaining a finite number of modes $N_{\rm G}$, lead to absolute
equilibrium states. We review how the Galerkin-truncated, three-dimensional, incompressible Euler equation thermalises and its connection to questions in
turbulence. We also discuss an emergent pseudo-dissipation range in the energy spectrum and the time-scales associated with thermalisation.
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All authors Rajarshi, Mohammad Saif Khan, Prateek Anand, Samriddhi Sankar Ray
Physics Education
Developing an LLM-Based Feedback System Grounded in Evidence-Centered Design to Support Physics Problem Solving
Holger Maus, Fabian Kieser, Stefan Petersen et al. (+2 more)
Generative AI offers new opportunities for individualized and adaptive learning, e.g., through large
language model (LLM)-based feedback systems. While LLMs can produce factually correct feedback for relatively straightforward conceptual tasks, delivering high-quality feedback for tasks that require advanced domain expertise, such as physics problem solving, remains a substantial challenge. This study presents the design and implementation of an LLM-based feedback system for physics problem solving grounded in evidence-centered design and reports a first evaluation within the German Physics Olympiad. Participants rated the usefulness and correctness of the generated feedback for each implemented problem. The collected ratings indicate that the feedback was generally perceived as useful and highly correct. However, an in-depth analysis revealed that the feedback contained errors in 20% of cases; errors that often went unnoticed by the students. We discuss the risks associated with uncritical reliance on LLM-based feedback and outline potential directions for generating more adaptive and reliable LLM-based feedback in the future.
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All authors Holger Maus, Fabian Kieser, Stefan Petersen, Peter Wulff, Paul Tschisgale
Physics Education
Opportunities and Challenges in Harnessing Digital Technology for Effective Teaching and Learning
Zhongzhou Chen and Chandralekha Singh
Most of today's educators are in no shortage of digital and online learning technologies available at their fingertips, ranging from Learning Management Systems such as Canvas, Blackboard, or Moodle, online meeting tools, online homework, and tutoring systems, exam proctoring platforms, computer simulations, and even virtual reality/augmented reality technologies. Furthermore, with the rapid development and wide availability of generative artificial intelligence (GenAI) services such as ChatGPT, we are just at the beginning of harnessing their potential to transform higher education. Yet, facing the large number of available options provided by cutting-edge technology, an imminent question on the mind of most educators is the following: how should I choose the technologies and integrate them into my teaching process so that they would best support student learning? We contemplate over these types of important and timely questions and share our reflections on evidence-based approaches to harnessing digital learning tools using a Self-regulated Engaged Learning Framework we have employed in our research in physics education that can be valuable for educators in other disciplines.
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History and Philosophy of Physics
Deflating the Spacetime-Matter Dichotomy
Antonio Ferreiro, Alex Fleuren and Niels C.M. Martens
In this paper we analyse scalar-tensor theories-specific instances of which include mainstream inflation and dark energy models-in light of the spacetime-matter dichotomy. We argue that it is difficult to categorise the scalar fields as either a pure aspect of the spacetime structure or a pure form of matter, by focusing on the Jordan vs Einstein frames of these theories. We present and evaluate various interpretational options available, concluding that the spacetime-matter dichotomy becomes untenable in this context. At the same time, the ontological and conceptual category of spacetime can be decoupled from that of
gravity, with the latter remaining viable in the context of scalar-tensor theories.
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Applied Physics
Theoretical and experimental development of a high-conversion-efficiency rectifier at X-band
Feifei Tan and Changjun Liu
Voltage doubler rectifiers are usually applied to systems with high voltage and low current requirement. An X band voltage doubler rectifier has been developed with 72% conversion efficiency. To the best of our knowledge, the obtained rectifying efficiency is the maximum reported to date at X band with Schottky diodes. The working characteristics of the diodes in the voltage doubler rectifier are analyzed in detail. Closed-form equations of diode input impedance and rectifying efficiency are presented and validated using Advanced Design System simulations. The matching network design of the proposed rectifier is based on the closed-form equations. The preliminary rectifying efficiency is predicted by the closed-form equations as well. Measured and simulated results are in good agreement.
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Instrumentation and Detectors
Accurate laboratory testing of low-frequency triaxial vibration sensors under various environmental conditions
Tomofumi Shimoda, Wataru Kokuyama and Hideaki Nozato
Triaxial vibration sensor are widely used used in various application. Recently, low-cost sensors based on micro electro mechanical system (MEMS) technology are also becoming more widely adopted. However, their measurement accuracy can be affected by environmental factors such as temperature. In this study, we developed an environmental testing system integrated with a triaxial vibration exciter. The system can reproduce long-stroke, low-frequency triaxial vibrations – such as those caused by huge earthquakes – under temperatures ranging from $-30~^\circ\mathrm{C}$ to $+80~^\circ\mathrm{C}$. Using this system, the measurement accuracy of vibration sensors can be evaluated under different environmental conditions. The system provides highly accurate reference measurements using a
laser interferometer and reference accelerometers that are primarily calibrated within the system. The overall accuracy of the reference vibration measurement is estimated to be approximately 0.23~%. Based on these reference measurements, we investigated the accuracy of earthquake observations using a MEMS accelerometer as a demonstration. The system configuration and testing procedures are presented in this paper.
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Computational Physics · Cross-listed from cond-mat.str-el
Phase structure of the one-dimensional $\mathbb{Z}_2$ lattice gauge theory with second nearest-neighbor interactions
Yeimer Zambrano, Aleksey Alekseev, Konrad J. Kapcia et al. (+2 more)
We investigate the ground-state phase diagram of a one-dimensional $\mathbb{Z}_2$
lattice gauge theory (LGT) model with hard-core
bosons at half-filling, extending previous studies by including second nearest-neighbor (2NN) interactions. Using
matrix product state techniques within the density
matrix renormalization group, we compute charge gap, static structure factor, pair-pair correlation functions, and
entanglement entropy for various interaction strengths and field parameters. We analyze two representative neatest-neighbor interaction strengths ($V_1$) that correspond to the Luttinger
liquid (LL) and Mott
insulator (MI) phases in the absence of the 2NN interactions. We introduce the 2NN coupling $V_2$ and investigate its impact on the system. Our results reveal very rich behavior. As the 2NN repulsion increases, in the case of small $V_1$, we observe a direct transition from the LL phase to a charge-ordered
insulator (COI) phase with four-site ordering pattern, whereas for large $V_1$, we observe a transition from the MI phase with two-site ordering pattern (previously found with only $V_1$ included), going through an intermediate LL region, and finally reaching the COI regime. Additionally, the inclusion of 2NN interactions enhances charge order and suppresses pair coherence, evidenced by sharp peaks in the structure factor and rapid decay in pair-pair correlators. Our work extends the well-studied phase structure of 1D $\mathbb{Z}_2$ LGT models and demonstrates the interplay between gauge fields, confinement, and extended interactions.
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All authors Yeimer Zambrano, Aleksey Alekseev, Konrad J. Kapcia, Krzysztof Cichy, Agnieszka Cichy
Fluid Dynamics
Mixing by offshore wind infrastructure: Resolving the density stratified wakes past vertical cylinders
Charlie J. Lloyd and Robert M. Dorrell
Offshore wind is rapidly expanding to meet clean and secure energy needs. New developments are now increasingly constrained to deep seasonally stratified waters. Here, flows past offshore wind infrastructure will increase water column mixing, although such processes and their extent are poorly understood. Studies have so far been limited to: field-scale simulations, which make sweeping assumptions regarding flow-structure interactions and fine-scale stratified
turbulence; and field observations, which are limited by the sparsity of measurement campaigns and data captured. To isolate and quantify the key processes governing water column mixing by infrastructure, we present the first structure-resolved direct numerical simulations of two-layer stratified flow past a vertical cylinder. We identify two wake regimes dependent on the flow Reynolds and Richardson numbers: i) A weakly stratified regime, characterised by a narrow but highly energetic wake dominated by horizontal shear, and ii) A strongly stratified wake, characterised by a thermocline-spanning recirculation
cell attached to the cylinder. Here, strong vertical motions develop which are responsible for the formation of large-scale stationary internal waves. These waves account for up to 10% of the total energy budget, and provide a new mechanism for far field energy propagation. The weakly stratified wake regime is characteristic of existing offshore wind sites where temperature gradients are relatively weak; the newly identified strongly stratified regime describes the dynamics to be expected in new and future deep water offshore wind sites. This difference between the two regimes explains previously enigmatic field observations regarding wake persistence and detectability. These simulations provides a critical
benchmark for validating future models and narrowing the gap between idealized simulations and field-scale flows.
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Medical Physics
PMB-NN: Physiology-Centred Hybrid AI for Personalized Hemodynamic Monitoring from Photoplethysmography
Yaowen Zhang, Libera Fresiello, Peter H. Veltink et al. (+2 more)
Continuous monitoring of blood pressure (BP) and hemodynamic parameters such as peripheral resistance (R) and arterial compliance (C) are critical for early vascular dysfunction detection. While photoplethysmography (PPG) wearables has gained popularity, existing data-driven methods for BP estimation lack interpretability. We advanced our previously proposed physiology-centered hybrid AI method-Physiological Model-Based Neural Network (PMB-NN)-in blood pressure estimation, that unifies deep learning with a 2-element Windkessel based model parameterized by R and C acting as physics constraints. The PMB-NN model was trained in a subject-specific manner using PPG-derived timing features, while demographic information was used to infer an intermediate variable: cardiac output. We validated the model on 10 healthy adults performing static and cycling activities across two days for model's day-to-day robustness, benchmarked against deep learning (DL) models (FCNN, CNN-LSTM,
Transformer) and standalone Windkessel based physiological model (PM). Validation was conducted on three perspectives: accuracy, interpretability and plausibility. PMB-NN achieved systolic BP accuracy (MAE: 7.2 mmHg) comparable to DL
benchmarks, diastolic performance (MAE: 3.9 mmHg) lower than DL models. However, PMB-NN exhibited higher physiological plausibility than both DL baselines and PM, suggesting that the hybrid architecture unifies and enhances the respective merits of physiological principles and data-driven techniques. Beyond BP, PMB-NN identified R (ME: 0.15 mmHg$\cdot$s/ml) and C (ME: -0.35 ml/mmHg) during training with accuracy similar to PM, demonstrating that the embedded physiological constraints confer interpretability to the hybrid AI framework. These results position PMB-NN as a balanced, physiologically grounded alternative to purely data-driven approaches for daily hemodynamic monitoring.
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All authors Yaowen Zhang, Libera Fresiello, Peter H. Veltink, Dirk W. Donker, Ying Wang
Plasma Physics
Optimized matching conditions for self-guided laser wakefield accelerators
P. Valenta, K. G. Miller, B. K. Russell et al. (+4 more)
We revisit the matching conditions for self-guided
laser pulse propagation in
plasma and refine their formulation to maximize the energy of electrons produced via
laser wakefield acceleration.
Bayesian optimization, combined with particle-in-
cell simulations carried out in a quasi-three-dimensional geometry and a Lorentz-boosted frame, is employed. The optimization identifies the maximum electron energy that a self-guided
laser wakefield accelerator, driven by a
laser of a given energy, can produce, together with the corresponding acceleration distance. Our results further demonstrate that electrons with energies close to the maximum value can be obtained across a relatively wide range of input parameters and without the need for their precise tuning. This provides substantial flexibility for experimental implementation and significantly relaxes the operational constraints associated with self-guided
laser wakefield accelerators.
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All authors P. Valenta, K. G. Miller, B. K. Russell, M. Lamač, M. Jech, G. M. Grittani, S. V. Bulanov
Biological Physics
Motor shot noise explains active fluctuations in a single cilium
Maximilian Kotz, Veikko F. Geyer and Benjamin M. Friedrich
Mesoscopic fluctuations reveal
stochastic dynamics of molecules in both inanimate and living matter. We investigate how small-number fluctuations shape the collective dynamics of molecular motors using motile cilia as model system. We theoretically show that fluctuations in the number of bound motors are sufficient to explain experimentally observed fluctuations in the cilia beat, including a quality factor $Q$ that measures oscillation precision and phase defects of intra-cilium synchronization. Our findings constrain theories of motor control and establish a link between microscopic motor noise and mesoscopic non-
equilibrium dynamics.
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Atmospheric and Oceanic Physics
Evaluation of preCICE (version 3.3.0) in an Earth System Model Regridding Benchmark
Alex Hocks and Benjamin Uekermann
In Earth System Modeling (ESM), meshes of different models usually do not match, requiring data mapping
algorithms implemented in coupling software. Valcke et al. recently introduced a
benchmark to evaluate such
algorithms and compared implementations in four specialized ESM couplers. In this paper, we assess preCICE, a general-purpose coupling library not limited to ESM, using this
benchmark and compare our results to the original study. The generality of preCICE with its larger community offers potential benefits to ESM applications, but the software naturally lacks ESM-specific solutions. We describe necessary pre- and postprocessing steps to make the
benchmark tangible for preCICE. Overall, preCICE achieves comparable results; using its radial basis function mapping yields significantly lower errors.
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Classical Physics
Influence of the basins of attractions in the register jumps of the clarinet
Nathan Szwarcberg, Tom Colinot, Christophe Vergez et al. (+1 more)
When playing the clarinet, opening the register hole allows for a transition from the first to the second register, producing a twelfth interval. On an artificial mouth, the blowing pressure range where the second register remains stable can be determined by gradually varying the blowing pressure while keeping the register hole open. However, when the register hole is opened while the instrument is already producing the first register, the range of blowing pressures that lead to a stable second register is narrower than the full stability zone of the second register. This phenomenon is investigated numerically by performing multiple hole openings at different times for each blowing pressure value. The
evolution of the probability of reaching the second register is computed, and its relationship with the structure of the basin of attraction of the second register is analyzed.
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All authors Nathan Szwarcberg, Tom Colinot, Christophe Vergez, Michaël Jousserand
Optics · Cross-listed from quant-ph
Scalable Optical Links for Controlling Bosonic Quantum Processors
Chuanlong Ma, Jia-Qi Wang, Linze Li et al. (+13 more)
Superconducting
quantum computing has the potential to revolutionize computational capabilities. However, scaling up large
quantum processors is limited by the cumbersome and heat-conductive electronic cables that connect room-temperature control electronics to
quantum processors, leading to significant signal attenuation. Optical fibers provide a promising solution, but their use has been restricted to controlling simple two-level
quantum systems over short distances. Here, we demonstrate optical control of a bosonic
quantum processor, achieving universal operations on the joint
Hilbert space of a transmon
qubit and a storage cavity. Using an array of cryogenic fiber-integrated uni-traveling-carrier photodiodes, we prepare Fock states containing up to ten
photons. Additionally, remote control of bosonic modes over a transmission distance of 15 km has been achieved, with fidelities exceeding 95%. The combination of high-dimensional
quantum control, multi-channel operation, and long-distance transmission addresses the key requirements for scaling superconducting
quantum computers and enables architectures for distributed
quantum data centers.
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All authors Chuanlong Ma, Jia-Qi Wang, Linze Li, Jiajun Chen, Xiaoxuan Pan, Zheng-Hui Tian, Zheng-Xu Zhu, Jia-Hua Zou, Dingran Gu, Luyu Wang, Qiushi Chen, Weiting Wang, Xin-Biao Xu, Chang-Ling Zou, Baile Chen, Luyan Sun
Computational Physics
MULE – A Co-Generation Fission Power Plant Concept to Support Lunar In-Situ Resource Utilisation
Julius Mercz, Philipp Reiss and Christian Reiter
For a sustained human presence on the Moon, robust in-situ resource utilisation supply chains to provide consumables and propellant are necessary. A promising process is molten salt electrolysis, which typically requires temperatures in excess of 900{\deg}C. Fission reactors do not depend on
solar irradiance and are thus well suited for power generation on the Moon, especially during the 14-day lunar night. As of now, fission reactors have only been considered for electric power generation, but the reactor coolant could also be used directly to heat those processes to their required temperatures. In this work, a concept for a co-generation fission power plant on the Moon that can directly heat a MSE plant to the required temperatures and provide a surplus of electrical energy for the lunar base is presented. The
neutron transport code Serpent 2 is used to model a ceramic core, gas-cooled very-high-temperature microreactor design and estimate its lifetime with a burnup simulation in hot conditions with an integrated step-wise criticality search. Calculations show a neutronically feasible operation time of at least 10 years at 100kW thermal power. The obtained power distributions lay a basis for further thermal-hydraulic studies on the technical feasibility of the reactor design and the power plant.
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Physics and Society
The Physics of Sustainability: Material and Power Constraints for the Long Term
José Halloy, Petros Chatzimpiros, François Graner et al. (+1 more)
Much of today's sustainability discourse emphasizes efficiency, clean technologies, and smart systems, but largely underestimates fundamental physical constraints relating to energy-matter interactions. These constraints stem from the fact that Earth is a materially closed yet energetically open system, driven by the sustained but low power-density flux of
solar radiation. This Perspective reframes sustainability within these axiomatic limits, integrating relevant timescales and orders of magnitude. We argue that fossil-fueled industrial metabolism is inherently incompatible with long-term viability, while post-fossil systems are surface-, materials-, and power-intensive. Long-term sustainability must therefore be defined not only by how much energy or material is used, but also by how it is used: favoring organic, carbon-based chemistry with limited reliance on purified metals, operating at low power density, and maintaining low throughput rates. Achieving this requires radical technological shifts toward life-compatible systems and biogeochemical circular processes, and, likely as a consequence, a paradigm change toward degrowth to a steady-state. These two shifts are mutually reinforcing and together provide the necessary foundation for any viable future.
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All authors José Halloy, Petros Chatzimpiros, François Graner, Thomas Gregor
Optics
Spatial-spectral mapping for long-duration broadband terahertz pulse generation in on-chip waveguide arrays
Yibo Huang, Yao Lu, Haoyu Duan et al. (+5 more)
Conventional approaches to terahertz (THz) pulse generation are restricted by the
Fourier-transform limit, which hinders the creation of sources that combine long duration with broad bandwidth–a capability crucial for many spectroscopic and sensing applications. In this work, we overcome this challenge in the terahertz domain using an on-chip gradient waveguide array. The key is to spectrally disperse the pulse into spatially separated channels within a lithium niobate chip, effectively decoupling the design of temporal and spectral properties. We validate the source by distinguishing amino acid mixtures, demonstrating its tailored biosensing potential. This work establishes a novel mechanism for integrated THz generation, offering considerable promise for broadband spectroscopy and on-chip photonics.
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All authors Yibo Huang, Yao Lu, Haoyu Duan, Chao Wang, Xitan Xu, Jiwei Qi, Qiang Wu, Jingjun Xu
Atomic Physics
Bound-free electron-positron pair production in combined Coulomb and constant crossed electromagnetic fields: a Schwinger-like process with intrinsic assistance
S. Remme, A. Eckey, S. Villalba-Chávez et al. (+2 more)
The bound-free channel of electron-positron pair production by a highly charged bare ion in the presence of a strong constant crossed electromagnetic field is studied. To calculate the pair production rate, two different methods are applied and compared with each other: (i) a quasiclassical tunneling theory and (ii) a strong-field approximation, both equipped with appropriate Coulomb correction factors. The resulting rate, which depends nonperturbatively on both the Coulomb field of the ion and the constant crossed field, is calculated in a broad range of applied field strengths and
nuclear charge numbers. Its functional form resembles the rate for a dynamically assisted Schwinger-like process, with the assistance being provided by the atomic binding energy of the created electron.
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All authors S. Remme, A. Eckey, S. Villalba-Chávez, A. B. Voitkiv, C. Müller
Optics · Cross-listed from quant-ph
All-photonic entanglement swapping with remote quantum dots
Mattia Beccaceci, Giuseppe Ronco, Fabrizio Cienzo et al. (+16 more)
Entanglement swapping is a protocol that details how to create
entanglement between previously uncorrelated particles. Its all-photonic version - mediated by the interference of
photon pairs generated by separate
quantum systems-finds disparate applications in
quantum networks. So far, all-photonic
entanglement swapping between remote systems has been implemented only using sources that operate probabilistically. However, the scaling up of
quantum networks requires deterministic
quantum emitters that do not suffer from a trade-off between degree of
entanglement and photonpair generation rate. Here, we demonstrate all-photonic
entanglement swapping using
photon-pairs generated by two separate GaAs
quantum dots. The emitters are deterministically embedded in hybrid
semiconductor-piezoelectric devices that make the entangled-
photons from two dissimilar
quantum dots nearly identical.
Entanglement swapping is demonstrated with a fidelity as high as 0.71(2), more than 10 standard deviations above the classical limit. The experimental data are quantitatively explained by a theoretical model that also suggests how to boost the protocol performances. Our work opens the path to the exploitation of
quantum dot entangled-
photon sources in
quantum repeater networks.
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All authors Mattia Beccaceci, Giuseppe Ronco, Fabrizio Cienzo, Pierpaolo Bassetti, Alessandro Laneve, Francesco Basso Basset, Tobias M. Krieger, Qurin Buchinger, Francesco Salusti, Barbara Souza Damasceno, Silke Kuhn, Saimon F. Covre da Silva, Sandra Stroj, Klaus D. Jöns, Sven Höfling, Tobias Huber-Loyola, Armando Rastelli, Michele B. Rota, Rinaldo Trotta
Applied Physics
Field Reconstruction for High-Frequency Electromagnetic Exposure Assessment Based on Deep Learning
Miao Cao, Zicheng Liu, Bazargul Matkerim et al. (+4 more)
Fifth-generation (5G) communication systems, operating in higher frequency bands from 3 to 300 GHz, provide unprecedented bandwidth to enable ultra-high data rates and low-latency services. However, the use of millimeter-wave frequencies raises public health concerns regarding prolonged electromagnetic radiation (EMR) exposure. Above 6 GHz, the incident power density (IPD) is used instead of the specific absorption rate (SAR) for exposure assessment, owing to the shallow penetration depth of millimeter waves. This paper proposes a hybrid field reconstruction framework that integrates classical electromagnetic
algorithms with deep learning to evaluate the IPD of wireless communication devices operating at 30 GHz, thereby determining compliance with established RF exposure limits. An initial estimate of the electric field on the evaluation plane is obtained using a classical reconstruction
algorithm, followed by refinement through a neural network model that learns the mapping between the initial and accurate values. A multi-
antenna dataset, generated via full-wave simulation, is used for training and testing. The impacts of training strategy, initial-value
algorithm, reconstruction distance, and measurement sampling density on model performance are analyzed. Results show that the proposed method significantly improves reconstruction accuracy, achieving an average relative error of 4.57% for electric field reconstruction and 2.97% for IPD estimation on the test
dataset. Additionally, the effects of practical uncertainty factors, including probe misalignment, inter-probe coupling, and measurement noise, are quantitatively assessed.
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All authors Miao Cao, Zicheng Liu, Bazargul Matkerim, Tongning Wu, Changyou Li, Yali Zong, Bo Qi
Optics
Deep Photonic Reservoir Computing with On-chip Nonlinearity
Jinlong Xiang, Youlve Chen, Yuchen Yin et al. (+6 more)
Reservoir computing, renowned for its low training cost, has emerged as a promising lightweight paradigm for efficient spatiotemporal processing,it remains challenging to realize deep photonic reservoir computing (DPRC) systems, due to the lack of scalable on-chip nonlinearity. Here, we introduce a versatile time delayed DPRC framework that natively supports deep and concurrent spatiotemporal processing entirely in the optical domain. At its core, the system leverages free carrier dynamics in silicon microring resonators to provide the fundamental nonlinearity and short term
memory, and these nonlinear nodes are interconnected through true time delay lines that establish shared long-term
memory. Benefiting from intrinsic physical nonlinearity and multi-timescale fading
memory, this simple yet effective architecture demonstrates remarkable high dimensional representation capabilities. On the NTU RGB D
benchmark, the parameter efficient DPRC system achieves superior action recognition accuracies compared to mainstream deep learning models, while requiring only a single shot regression training procedure. We further verify a prototype DPRC chip that excels across diverse
dataset classification and time series prediction tasks. It enables a streamlined all optical pipeline between hierarchical layers, delivering a consistent computational density of 334.25 TOPs/mm2, independent of the reservoir depth and three orders of magnitude higher than conventional approaches. Moreover, its performance scales with near-zero hardware overhead by utilizing additional wavelength channels. This DPRC network is highly scalable on a silicon photonic platform, with flexible extension to hundreds of deep reservoir layers and parallel channels, paving the way toward intelligent optoelectronic systems for advanced real time processing and parallel decision making.
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All authors Jinlong Xiang, Youlve Chen, Yuchen Yin, Zhenyu Zhao, Chaojun Xu, An He, Xintong Lv, Yikai Su, Xuhan Guo
Fluid Dynamics
Generating wall-bounded turbulent inflows at high Reynolds numbers
Ronith Stanly, Timofey Mukha, Martin Karp et al. (+2 more)
One of the main challenges in simulating high Reynolds number ($Re$) turbulent boundary layers (TBLs) is the long streamwise distance required for large-scale outer-layer structures to develop, making such simulations prohibitively expensive. We propose an inflow generation method for high $Re$ wall
turbulence that leverages the known structure and scaling laws of TBLs, enabling shorter development lengths by providing rich input information. As observed from the inner-scaled pre-multiplied spectra of streamwise velocity, with an increase in $Re$ the outer region grows and occupies more of the spanwise wavenumber space in proportion to the increase in $Re$; while the inner region remains approximately the same. Exploiting this behavior, we generate high-$Re$ inflow conditions for a $\textit{target}$ $Re$ by starting from cross-stream velocity slices at a lower $\textit{base}$ $Re$. In spectral space, we identify the inner and outer region wavenumbers, and shift the outer-region components proportionally to the desired $Re$ increase. We closely examine the capability of this method by scaling a set of velocity slices at $Re_\theta=2240$ and $4430$ to $Re_\theta=8000$, and using them as inflow conditions for direct numerical simulations (DNS) of spatially developing TBLs growing from $Re_\theta=8000-9000$. The skin friction coefficient and shape factor predicted by the new method, regardless of the $\textit{base}$ $Re$ tested, is within $\pm3.5\%$ and $\pm0.5\%$, respectively, of that of a precursor simulation right from the inlet. Reynolds stresses match very well after approximately $8~\delta_{99_0}$. This gives an order of magnitude reduction in development length compared to other methods proposed in the literature.
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All authors Ronith Stanly, Timofey Mukha, Martin Karp, Stefano Markidis, Philipp Schlatter
Physics and Society
Coupling opinion dynamics and epidemiology
Thomas Goetz, Tyll Krueger, Karol Niedzielewski et al. (+2 more)
This research investigates the coupled dynamics of behavior and infectious disease using a mathematical model. We integrate a two-state q-voter opinion process with SIS-type infection dynamics, where transmission rates are influenced by the opinion and an infection-induced switching mechanism represents individuals reassessing their behavior upon infection. Analytically, we derive conditions for the stability of endemic and disease-free
equilibria. Numerical simulations reveal complex dynamics: above a certain infectivity threshold, the system can exhibit alternative basins of attraction leading to a balanced endemic fixed point or stable limit cycles. Notably, the dominant asymptotic opinion and resulting epidemiological outcomes show non-monotonic relationships with infectivity, highlighting the potential for adaptive behavior to induce complex system dynamics. These findings underscore the critical role of social interventions; shifts in behavioral norms and trust can permanently alter epidemic outcomes, suggesting that such interventions are as crucial as biomedical controls
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All authors Thomas Goetz, Tyll Krueger, Karol Niedzielewski, Jan Schneider, Barbara Pabjan
Atmospheric and Oceanic Physics · Cross-listed from astro-ph.EP
An analytical framework for atmospheric tides on rocky planets. I. Formulation
Pierre Auclair-Desrotour, Mohammad Farhat, Gwenaël Boué et al. (+1 more)
Atmospheric thermal tides arise from the diurnal contrast in
stellar irradiation. They exert a significant influence on the long-term rotational
evolution of rocky
planets because they can accelerate the planetary
spin, thereby counteracting the decelerating effect of classical gravitational tides. Consequently,
equilibrium tide-locked states may emerge, as exemplified by Venus and hypothesised for Precambrian Earth. Quantifying the atmospheric thermal torque and elucidating its dependence on
tidal frequency – both in the low- and high-frequency regimes – is therefore essential. In particular, we focus here on the resonance that affected early Earth, which is associated with a forced Lamb wave. Within the framework of linear theory, we develop a new analytical model of the atmospheric response to both gravitational an thermal
tidal forcings for two representative vertical temperature profiles that bracket the atmospheres of rocky
planets: (i) an isothermal profile (uniform temperature) and (ii) an isentropic profile (uniform potential temperature). Dissipative processes are incorporated via Newtonian cooling. We demonstrate that the isothermal and isentropic cases are governed by the same general closed-form solution, and we derive explicit expressions for the three-dimensional
tidal fields (pressure, temperature, density and wind velocities) throughout the spherical atmospheric shell. These results constitute the foundation for two forthcoming papers, in which analytical formulae for the thermotidal torque will be presented and compared with numerical solutions obtained from General Circulation Models (GCMs).
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All authors Pierre Auclair-Desrotour, Mohammad Farhat, Gwenaël Boué, Jacques Laskar