Instrumentation and Detectors
Development of LGAD Sensor Technology on 12 inch Wafers for 3D Integration with advanced CMOS processes
Ron Lipton, Julie Segal, Angela Kok et al. (+10 more)
We report on the design, simulation, and initial testing of Low Gain Avalanche Diode (LGAD) sensors fabricated in 65 nm CMOS technology on 12 inch wafers. These sensors were developed as part of a project to demonstrate full wafer to wafer hybrid bonding of sensors and 28 nm Readout Integrated
Circuits (ROICs). The sensor submission and test included standard (reach-through) LGADs, AC coupled LGADs, and Deep Junction LGADs as well as various test structures.
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All authors Ron Lipton, Julie Segal, Angela Kok, Troy England, Artur Apresyan, Davide Braga, Farah Fahim, Shuoxing Wu, Christopher Kenney, Bojan Markovic, Ariel Gustavo Schwartzman, Lorenzo Rota, Arthur Carpenter
Instrumentation and Detectors
WLCG Mini-Capability Challenge: Host Tuning to Im- prove WAN Data Transfers
Andrew Malone Melo, Carlos Fernando Gamboa, Diego Devila Foyo et al. (+8 more)
High-throughput data movement is a defining requirement of the WLCG computing model, yet end-to-end performance is often constrained less by the network backbone than by host-level configuration. We present the results of a WLCG mini-capability challenge focused on host optimization at ATLAS and CMS sites (FNAL, UCSD, UNL, BNL, AGLT2, MWT2, and Vanderbilt), using EL8/9 hosts with 25\,Gbps or higher NICs. NET2 participated in a subsequent trans-Atlantic test. The methodology combines ESnet Fasterdata guidance with the OSG-hosted fasterdata-tuning.sh framework, applying TCP, queue, offload, and ring-buffer tuning with save and restore support for safe baseline comparisons. Results show that tuning benefit depends critically on the local bottleneck. Where DTNs and storage were both capable (UNL), FTS throughput rose from 60 to 90\,Gbps. Where storage was the binding constraint (UCSD), iperf3 throughput improved from 20 to 90\,Gbps while FTS was unchanged. Where the NIC was the binding constraint (Vanderbilt, 25\,G NIC), the tuning profile actively reduced throughput, motivating a review of per-site applicability. The challenge also surfaced and resolved a dCache proxying misconfiguration at AGLT2, demonstrating that mini-challenges serve a diagnostic function beyond raw performance measurement.
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All authors Andrew Malone Melo, Carlos Fernando Gamboa, Diego Devila Foyo, Eduardo Bach, Eli Dart, Shawn McKee, Garhan Attebury, Philippe Laurens, Wendy Wu, Syed Asif Shaw, Hiro Ito
Classical Physics
The Myth of Hamel's Paradox
Robert Singer
Substituting a velocity constraint into the kinetic energy before forming Lagrange's equations yields, in general, incorrect equations of motion. Recent literature calls this failure "Hamel's paradox," a term introduced in 2010. We show that there is no paradox. The inadmissibility of the substitution was recognized in the 1890s; C. Neumann (1899) named the operation the illegitimate form of the kinetic energy, and Hadamard (1895), Chaplygin (1897), Appell (1899) and Hamel (1904) established its limits of validity. Hamel's textbook of 1949, the nominal source of the paradox, states the prohibition and uses Neumann's term; the result remained in print in the specialist literature and in
geometric mechanics. The modern debate cites none of this record; we collect it from the primary sources and measure the debate's claims against it. A
geometric analysis identifies the substituted function as the restriction of the mass metric to the constraint distribution. If the distribution is integrable, the restriction is the induced metric of an integral
manifold, and the substitution is legitimate. If not, the correctly embedded Boltzmann–Hamel equations differ from those of the restricted energy by a single residue, $\mathrm{Res}_\alpha=c^{s}_{\alpha\beta} \omega^\beta P_s$, relative to the complement of the constraint distribution that the shortcut selects. The residue vanishes exactly when the symmetric part of its coefficient vanishes. The two classical admissibility mechanisms, decoupling of the mass metric and vanishing bracket components, are sufficient for this and, for a single constraint, also necessary; for several constraints they are not, which corrects a necessity claim in Hamel's paper of 1904.
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Instrumentation and Detectors
Quantitative characterization of superconducting qubits as particle detectors
Francesco De Dominicis, Raja Yasir Mehmood Khan, Dounia L Helis et al. (+22 more)
Ionizing radiation is a major source of correlated errors in superconducting
quantum processors, yet the mechanisms responsible for the
qubit response to particle interactions remain only partially understood. In this work, we operate superconducting
qubits as particle detectors while simultaneously monitoring the deposited energy with an independent
semiconductor cryogenic sensor. This complementary measurement provides an absolute determination of the particle interaction rate in the chip, enabling the first direct measurement of the detection efficiency of superconducting
qubits exposed to environmental radiation. We measure individual
qubit detection efficiencies of about 30-40$\%$, increasing to approximately 50$\%$ when combining the response of two
qubits. Under the conservative assumption that the efficiency loss is entirely determined by a finite detection threshold, we derive an upper limit on the effective energy threshold of the detector of $70\pm20~\mathrm{(stat)}\pm30~\mathrm{(syst)}~\mathrm{keV}$. Measurements with radioactive sources producing different deposited-energy spectra further show that the detection efficiency decreases for lower-energy interactions. We further investigate the origin of the
qubit response by injecting controlled thermal pulses with a resistive heater. Although these pulses deposit energies comparable to those released by particle interactions, they do not reproduce the radiation-induced signatures, demonstrating that the
qubit response to radiation cannot be explained by a transient increase in substrate temperature alone. Our results establish an experimental framework for quantitatively connecting particle energy deposition to radiation-induced responses in superconducting
quantum circuits.
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All authors Francesco De Dominicis, Raja Yasir Mehmood Khan, Dounia L Helis, Ambra Mariani, Letizia Tirabasso, Alberto Ressa, Mustafa Bal, Fabio Bellini, Camilla Bonomo, Nicola Casali, Gianluigi Catelani, Ivan Colantoni, Francesco Crisa, Angelo Cruciani, Sabrina Garattoni, Luca Gironi, Andrea Melchiorre, Lorenzo Pagnanini, Valerio Pettinacci, Stefano Pirro, Andrei Puiu, Tanay Roy, Shaojiang Zhu, Anna Grassellino, Laura Cardani
Applied Physics
A reduced-order analysis for multi-reflection-driven laser absorptance in vaporization-induced cavities
Haolin Liu, Haoran Shi, Xuzhe Zeng et al. (+4 more)
Multiple internal reflections enhance
laser absorption in vaporization-induced cavities, but connecting ray histories, cavity geometry, and absorptance typically requires full ray tracing. We develop a reduced-order framework based on Beer-Lambert-type attenuation governed by an effective per-reflection optical depth (EPROD). A power-weighted formulation defines an absorptance-equivalent interaction count under constant effective single-interaction absorptance, while a cumulant expansion separates the EPROD-independent mean interaction count from ray-path heterogeneity corrections. Across nine simulation groups spanning three
alloys and three spot diameters, the mean interaction count follows a unit-intercept linear depth relation with R-squared values of 0.7824-0.9843. Its slope varies by only 3.4%-6.3% across
alloys at fixed spot diameter and decreases with increasing spot diameter. Fixing this slope from ray statistics yields an absorptance-depth model with one fitted parameter and a median prediction error (MAPE) of 2.61%. An independent synchronized Ti-64 experiment yields model parameters within approximately 2.4% of corresponding simulation values. This framework separates
geometric interaction accumulation from optical attenuation, enabling efficient energy-coupling prediction and absorptance-based keyhole characterization.
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All authors Haolin Liu, Haoran Shi, Xuzhe Zeng, Brian J. Simonds, Wenda Tan, Anthony D. Rollett, Levent Burak Kara
Chemical Physics
Reinforcement learning amortizes transition-state physics into one-step flow models
Yunyang Li, Zechang Sun, Kuang Yu et al. (+3 more)
Transition-state searches remain a major bottleneck in reaction discovery, as identifying valid saddle-point structures requires numerous expensive
quantum-chemical calculations. Generative models can reduce this burden by proposing candidates from reactant and product geometries, but supervised training on geometries alone does not enforce the physical conditions required of a transition state, while enforcing them during sampling is costly. We introduce Trèfle, a one-step flow model that amortizes physics-guided generation into training: a mean-flow objective replaces multi-step sampling, a low-cost surrogate replaces density functional theory (DFT) reward evaluations, and reinforcement-learning post-training with rewards that enforce those conditions replaces inference-time guidance. Trèfle generates accurate transition states at $30\times$ the throughput of multi-step baselines. It transfers few-shot to ten unseen transition metals and generalizes zero-shot to molecules far larger than any in training. With DFT refinement and reaction-path validation, Trèfle-seeded searches recover all 13 unseen $\gamma$-ketohydroperoxide channels, while post-training raises per-attempt recovery from 28% to 50% across 85 unseen bimolecular channels. Trèfle thus provides fast, reliable seeds for automated reaction discovery.
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All authors Yunyang Li, Zechang Sun, Kuang Yu, Wen Yan, Mark Gerstein, Hung Q. Pham
Plasma Physics
Controlling $\gamma$-photon production with additively manufactured microstructured targets
Joshua Luoma, Andreas Kemp, Thomas G. White et al. (+3 more)
Nonlinear inverse Compton scattering (NICS) in high-density targets offers a promising route for generating high flux, broadband sources of MeV $\gamma$-
photons which are important for applications like radiography, photonuclear physics, and pair production. Microstructured targets enable control over the NICS process to help shape the
photon energy, angular distribution, and total yield. This work presents particle-in-
cell (PIC) simulations exploring target microstructures compatible with two-
photon polymerization (TPP) additive manufacturing, which is a promising technique that allows high-volume and reproducible printing of sub-wavelength features as small as 200 nm. We investigate how microstructures influence NICS radiation and merge simulation analysis with simple analytical models that guide target design. These results establish pathways to optimize targets for low-divergence, high-energy
photon beams with 2.0$\cdot$10$^{8}$
photons/J exceeding 100 MeV and targets for high yields delivering upwards of 2.0$\cdot$10$^{11}$
photons/J above 1 MeV in the multi-PW regime.
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All authors Joshua Luoma, Andreas Kemp, Thomas G. White, Dean Rusby, Hui Chen, Gennady Shvets
Chemical Physics
Bridging Molecular Scales with Implicit Score Matching for Bottom-Up Coarse Graining
Patrick G. Sahrmann, Nicholas Lubbers, Benjamin T. Nebgen et al. (+4 more)
Molecular dynamics simulations provide a computational microscope for atomic scale processes but remain restricted to relatively small spatial and temporal scales. Coarse-grained models extend their reach by representing groups of atoms as effective interaction sites. The number of particles is systematically reduced by
embedding the collective behavior of groups of atoms into coarse-grained sites governed by effective potentials. However, constructing accurate, highly coarsened potentials, even with the assistance of modern machine-learning methods, is a grand challenge because coarse grained forces depend on a statistical distribution of composite atomic configurations making direct calculation computationally prohibitive. Here, we introduce an alternative framework based on implicit score matching that directly avoids the need for explicit coarse-grained forces. This approach both substantially reduces the training data required relative to conventional force-matching methods and enables high-fidelity models in which each coarse-grained site represents hundreds to thousands of atoms. We demonstrate the method's versatility and effectiveness across a diverse range of chemical systems and phenomena, developing bottom-up, machine-learned models. These enable efficient modeling at micrometer and millimeter length scales, while retaining significant amounts of the atomic scale fidelity. These results establish implicit score matching as a practical route towards chemically accurate simulations that bridge molecular and mesoscopic scales.
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All authors Patrick G. Sahrmann, Nicholas Lubbers, Benjamin T. Nebgen, Emily Shinkle, Sergei Tretiak, Kipton Barros, Brenden W. Hamilton
Data Analysis and Statistics · Cross-listed from gr-qc
Identifiability Limits of Gravitational Wave Phase Deviations: Multiclass Classification with a Multihead Neural Network
Lavinia Heisenberg and Shayan Hemmatyar
We study how well gravitational-wave phase deviations can be identified using a neural network with classification and regression heads. The classification head distinguishes general relativity (GR) from six parametrized post-Einsteinian phase families with exponents $b\in\{-7,-5,-3,-1,+1,+2\}$; the regression head predicts the logarithm of the coupling magnitude, $\log_{10}|\beta|$. The network input is a response function quantifying the sensitivity of waveform mismatch to waveform deformations. We use stationary Gaussian noise with the Advanced LIGO design spectrum for 262 sources and a measured Hanford spectrum from the third observing run (O3) for 132 sources, and also test injections into recorded Hanford strain. On the two Gaussian
datasets, the network identifies the exponent of deviations clearly above the noise with accuracies of $0.427$ and $0.329$. To interpret these results, we compare the network with an approximate classifier built from predicted waveform residuals for the six phase families and same sources. The two classifiers often confuse the same families. Examining residuals left by different phase corrections, we link these errors to similarities between the underlying deviations. On selected recorded Hanford strain, networks trained on simulated or recorded noise achieve overall classification accuracies close to those on simulated Hanford noise. Of thirteen neural-network variants tested on the Advanced LIGO design
dataset, the recurrent network has the highest mean classification accuracy for loud deviations, although all variants remain below the approximate classifier. The network nearly matches that classifier's accuracy, indicating that identification is limited by similarities between neighboring phase families rather than by the network, for known intrinsic source parameters.
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Classical Physics · Cross-listed from cs.LG
Lagrangian and Hamiltonian Neural Networks With a Dissipative System
V. Rayamajhi and J. Singal
We investigate the applicability of Lagrangian and
Hamiltonian Neural Network models to a dissipative system that has explicit time dependence in its Lagrangian,
Hamiltonian, and total energy. To do so we consider these neural network models for simulated systems of a harmonic one-dimensional, one-component oscillator with damping, as well as without damping for comparison. We find that both the Lagrangian and
Hamiltonian approaches are able to predict the empirical physical behavior of the damped oscillator systems and to effectively “learn” to varying degrees the underlying Lagrangians and
Hamiltonians, as has previously been shown to be the case with undamped oscillator systems. These investigations elucidate important properties of Lagrangian and
Hamiltonian mechanics, including properties that are not manifest when considering systems without explicit time dependence.
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Computational Physics · Cross-listed from cs.LO
Developing a Numerical Algorithm with CIVL Model Checking in the Loop
Youngjun Lee, Anshu Dubey and Jan Hückelheim
Verifying a numerical
algorithm in a large scientific simulation framework is challenging: the framework is too big to model-check, and the unit tests exercise only sampled inputs. We report a case study in which we developed a new cloud-in-
cell (CIC) deposition
algorithm for Flash-X, a large-scale multiphysics simulation framework, keeping the CIVL model checker in the development loop. Rather than verify the
algorithm within Flash-X's hefty infrastructure, we extract only the interfaces that the
algorithm needs into a small, self-contained C model, which abstracts away implementation details of the Flash-X infrastructure that are unrelated to the new
algorithm. The new
algorithm is then built and checked within this C model. The CIVL model checker enables verification of the required physical properties of the CIC deposition
algorithm using symbolic values for the particle positions. It proves two physical properties—mass conservation and the deposition location—for the continuum of admissible positions in the simulation domain. CIVL also verifies the
algorithm's
memory safety and freedom from MPI deadlocks and data race conditions over all rank distributions within specified bounds. Writing the verifying properties first and continuously checking them at each stage of the bottom-up prototyping workflow turned CIVL into a design guardrail that greatly increased confidence in the extended
algorithm. During our case study, CIVL surfaced a concurrency defect in the
algorithm that our random-seed-based tests failed to exercise. This paper shows our workflow, with the goal of helping readers understand its benefits, cost, and tradeoffs compared with testing.
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Optics
Discrete quality-factor control in a side-coupled photonic crystal microcavity: evanescent Bloch tunnelling and the finite-cell correction
Hasan Oguz
A point-defect cavity side-coupled to a line-defect waveguide in a two-dimensional photonic
crystal of silicon rods in water is studied with plane-wave expansion and finite-difference time-domain computations. The defect-rod radius tunes the resonance continuously within the band gap, as first-order perturbation theory predicts, whereas the quality factor changes in discrete steps set by the number of
lattice rows between cavity and guide; displacing the defect rod by up to a tenth of a period changes it by less than 7 percent. Each added row multiplies the quality factor by a factor that follows, without an adjustable parameter, from the decay of the evanescent Bloch channel of the bulk
crystal that is phase-matched to the guided mode; the channel at zero wavevector along the guide predicts little more than half that factor. Two biases of a finite computational domain, leakage across the cladding and a standing wave set up by reflections at the ends of the truncated guide, lower the per-row factor and change the quality factor by up to a factor of two if left uncorrected. Scaled to 1550 nm the design gives a sensitivity of 634 nm per refractive index unit, which perturbation theory reproduces, and the linewidth agrees with that of a transmission spectrum normalized to a cavity-free reference. Absorption by water caps the quality factor near 9200 and limits the useful number of barrier rows. The guided mode lies inside the light cone, so a slab of finite height needs a different guide.
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Fluid Dynamics · Cross-listed from astro-ph.SR
Transport of Magnetic Fields by Thermohaline Convection in Crystallizing White Dwarfs
Nicolas Frazao Fernandes, J. R. Fuentes and Jim Fuller
The origins of strong
magnetic fields observed in many white dwarfs are uncertain. Recent observations show that such fields are more common for white dwarfs with crystallized cores. Crystallization creates a destabilizing composition gradient that drives thermohaline convection in the
star's fluid envelope, which may play an important role in
magnetic field emergence. We perform 3D magnetohydrodynamical simulations of thermohaline convection with
magnetic fields of various strengths and orientations, using the Boussinesq approximation in periodic Cartesian boxes. Our simulations suggest that thermohaline convection can generate
magnetic fields when the
magnetic Prandtl number is large (i.e., low
magnetic diffusivity), but they are likely too weak to account for those observed in
magnetic white dwarfs. However, the simulations indicate that strong
magnetic fields can be advected outwards by the thermohaline convection without being destroyed, depending on the net
magnetic flux. Hence, crystallization-induced thermohaline mixing may transport
magnetic fields initially trapped in white dwarf interiors to their outer layers, allowing them to be observed. However, additional simulations using more realistic geometry and
magnetic field configurations will be needed to confirm this possibility.
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Plasma Physics
Joint Amplitude-Phase Optimization of Broadband Lasers Raises Absolute Two-Plasmon-Decay Thresholds Above Coherence-Time Predictions
A. S. Joglekar, R. K. Follett, D. H. Froula et al. (+1 more)
Broadband
lasers suppress the absolute two-plasmon-decay instability that preheats inertial-fusion targets. The usual scaling relates this suppression to the drive's coherence time, a power-spectrum statistic insensitive to spectral phase. By gradient descent through a differentiable enveloped wave solver, we show that at fixed power spectrum phase optimization raises the threshold from the random-phase median of $2.8\,I_\mathrm{mono}$ to $4.1\,I_\mathrm{mono}$, but produces a transform-limited (TL) pulse train with peak-to-average ratio $R=32$. Joint amplitude–phase optimization reaches $5.6\,I_\mathrm{mono}$ at $R=3.6$, above the best tested TL line-count result of $4.5\,I_\mathrm{mono}$ at $R=16$; relaxing the peak constraint raises the joint threshold to $6.4\,I_\mathrm{mono}$. An exact growth-rate budget evaluated inside the simulation separates the two mechanisms. TL suppresses coupling mainly by concentrating the pump field into short bursts, thereby lowering its time-averaged magnitude at fixed average intensity. The joint optima retain near-random values of this amplitude measure while suppressing both the available coupling and the fraction realized through pump–daughter
alignment. The advantage of joint optimization persists from $1\%$ to $4\%$ bandwidth and from OMEGA-scale to ignition-scale conditions.
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All authors A. S. Joglekar, R. K. Follett, D. H. Froula, J. P. Palastro
Physics Education
Artificial Neural Network Assisted Modelling of Tangent Galvanometer Measurements for the Determination of Horizontal Component of Earth's Magnetic Field
Saralasrita Mohanty, Sudakshina Prusty, Anshuman Pal et al. (+1 more)
The Tangent Galvanometer (TG) is a standard undergraduate laboratory experiment for estimating the horizontal component of Earth's
magnetic field (BH) by measuring the angle of deflection of a
magnetic needle corresponding to the current flowing through a circular coil. In this study, an artificial neural network (ANN) is used as a complementary data-driven model to predict the value of BH. A
dataset comprising 225 observations obtained using 50-turn and 500-turn coils was used for developing the ANN model. After quality control, 223 observations were retained and divided into training (70%), validation (15%), and testing (15%) subsets. The model was optimized using a feed-forward neural network with Tanh activation. Three different models (Models A, B, and C) with different input variables were compared for optimum performance. Model C used five input variables: current, deflection angle, tan(theta),
magnetic field produced by the coil, and number of turns. The addition of tan(theta) produced a substantial improvement in prediction performance, which was further improved by including the
magnetic field produced by the coil. Model C gave the best test performance, with R2 = 0.99053, RMSE = 0.54076 microT, and MAE = 0.32940 microT. The experimental and ANN-predicted values of BH were also compared with an adopted local geomagnetic reference value of 39.0 microT. The mean experimental and ANN-predicted values were 37.38898 microT and 37.32161 microT, respectively. The results demonstrate the usefulness of ANN as a complementary tool for analyzing experimental variability and nonlinear relationships in an undergraduate physics laboratory experiment.
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All authors Saralasrita Mohanty, Sudakshina Prusty, Anshuman Pal, Pradipta Kumar Mishra
Fluid Dynamics
Reversibility and Multiplicity in Liquid-Vapor Phase Change
Luiz Eduardo Czelusniak, Alexander J. Wagner and Luben Cabezas Gómez
Using diffuse-interface theory, we uncover unexpected phase-change phenomena that are absent from widely used models of evaporation and condensation. In the inviscid limit, the same adjacent thermodynamic states admit stationary evaporation and condensation with equal and opposite mass fluxes, even when the vapor pressure lies below saturation. Viscosity breaks this reversibility but gives rise to multiple stationary condensation rates for the same vapor state.
Lattice Boltzmann simulations dynamically recover these states, showing that these phenomena are dynamically realized by the diffuse-interface equations rather than arising solely as mathematical solutions of the reduced interfacial relations. Our results reveal previously unexplored behavior in a well-established theory of
liquid–vapor interfaces.
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Accelerator Physics
Incorporating Heightened Scrutiny into a Large HEP Software Project
Philippe Canal, Chris Green, Kyle Knoepfel et al. (+5 more)
In 2023, DUNE began re-evaluating the requirements of its data-processing framework, which led to commissioning a new design that would better fit
neutrino physics than the existing reconstruction frameworks designed for
collider physics. Due to the radical changes expected, significant multi-institutional effort has been directed toward the creation of the Phlex framework. In addition, the tight timelines in which to implement such a framework have invited scrutiny from various parties, including DUNE itself, the host laboratory Fermilab, and the US Department of Energy. After briefly discussing the unique needs of
neutrino physics, we will recount how the Phlex development team has approached the design and development of a framework in the context of this heightened scrutiny. This process began with a systems-engineering approach to formally manage the requirements DUNE has of its framework. What followed was, for the first time in the HEP community, a review of the framework's conceptual design by a panel of external framework experts before developers proceeded to the implementation. The implementation efforts have led to a series of prototypes that are being used to provide rapid feedback from Phlex users to framework developers. We will discuss how each of these steps has resulted in a strong design that has the backing of the DUNE experiment, Fermilab, and the US Department of Energy.
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All authors Philippe Canal, Chris Green, Kyle Knoepfel, Wim Lavrijsen, Adam Lyon, Marc Paterno, Saba Sehrish, Beojan Stanislaus
Plasma Physics · Cross-listed from astro-ph.SR
Negative and Positive Cascade Rates of Slow Alfvénic Turbulence in Switchback and Non-switchback Intervals: Wind Observations
Sofiane Bourouaine
We estimate the turbulent cascade rate in imbalanced Alfvénic
turbulence in the slow solar wind using a Politano–Pouquet-based approach that does not require an isotropic assumption. We analyze selected 8-hour intervals provided by Wind spacecraft. These intervals are characterized by near-homogeneity, weak velocity shear, and imbalanced Alfvénic fluctuations, and are classified as switchback or non-switchback depending on the angular spread of the
magnetic field relative to the mean field. Switchback intervals are identified by large
magnetic-field rotations, whereas non-switchback intervals exhibit only small angular deviations. Our results show that non-switchback intervals exhibit a positive total cascade rate and power spectra consistent with a $-3/2$ scaling, indicative of a forward energy cascade. In contrast, switchback intervals are characterized by a negative total cascade rate (inverse cascade), and by steeper power spectra with a spectral index of approximately $-1.6$. This indicates that large
magnetic-field rotations can substantially modify the scale-to-scale energy transfer in Alfvénic slow solar wind. The results suggest that switchbacks may be associated with additional physical processes, such as local instabilities, or reconnection-related structures, that alter the direction and magnitude of the turbulent cascade. These findings may help explain previous reports of negative cascade rates in imbalanced Alfvénic solar-wind
turbulence.
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Chemical Physics
Heteroatom Position Controls Ultrafast Photodynamics of Oxazole and Isoxazole
Briony Downes-Ward, Paul Javed, Huynh V. S. Lam et al. (+27 more)
The ultrafast photochemistry of heterocyclic compounds is central to photobiology and materials chemistry, yet direct experimental observation of their structural dynamics remains rare. Here we present the first real-time structural characterization of photoinduced ring opening and subsequent fragmentation in the isomeric pair oxazole and isoxazole using MeV ultrafast electron diffraction (UED), complemented by non-adiabatic molecular dynamics simulations. Upon photoexcitation at 200 nm, both isomers undergo ring opening followed by fragmentation into various products, but with strikingly different dynamics governed by heteroatom positioning. Trajectory Surface Hopping simulations reproduce the experimental diffraction signatures, which are surprisingly similar for both isomers, and reveal distinct mechanistic pathways for the two isomers. For isoxazole, all trajectories exclusively undergo N-O bond cleavage within 40 fs, followed by sequential fragmentation into HCN + ketene and HCO + vinyl nitrene channels on the hundreds-of-femtoseconds timescale. Oxazole, by contrast, shows significantly slower ring opening (290 fs) with only 85% efficiency, proceeding primarily through O-C cleavage and accessing a richer landscape of intermediates including nitrile ylide and O-pyramidalized structures. Simulated diffraction patterns derived from trajectory ensembles, convolved with the experimental instrument response function, are in agreement with the branching ratios observed by UED. This synergy between UED and trajectory surface hopping provides an atomistic picture of how the simple interchange of heteroatom connectivity in structural isomers fundamentally reshapes excited-state potential surfaces, conical intersection accessibility, and photochemical outcome.
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All authors Briony Downes-Ward, Paul Javed, Huynh V. S. Lam, Sajib K. Saha, Surjendu Bhattacharyya, Martin Centurion, Xinxin Cheng, R. Joel England, Casey Foley, Smita Ganguly, Patrick L. Kramer, Jinxin Lang, Randy Lemons, Ming-Fu Lin, Yusong Liu, Tu T. Nguyen, Joao Pedro Figueira Nunes, Chatura Perera, Alexander H. Reid, Ethan Ross, Artem Rudenko, Xiaozhe Shen, John Searles, Anbu Selvam Venkatachalam, Enliang Wang, Stephen P. Weathersby, Vinod Kumarappan, Arthur G. Suits, Christine Aikens, Daniel Rolles
Atomic Physics
Systematic errors from scalar slowing-down models in transient SERF magnetometry
Marek Kopciuch, Harini Raghavan and Morgan W. Mitchell
Reduced Bloch models of
spin-exchange-relaxation-free (SERF) magnetometers commonly describe the coupled electron–nuclear
spin dynamics using a single nuclear slowing-down factor. We show that this scalar description becomes inaccurate when the polarization magnitude changes appreciably. We derive an effective Bloch equation with separate longitudinal and transverse slowing-down factors, $q_\parallel(P)$ and $q_\perp(P)$, governing changes in polarization magnitude and direction. For $^{87}\mathrm{Rb}$ under representative SERF conditions and undergoing free-induction decay (FID), comparison with master-equation simulations shows that the dynamic scalar model produces a
magnetic-field estimation bias of up to $4.2\%$ at high initial polarization, while fixing the slowing-down factor at a reference value increases the error to $10.1\%$. Lowering the polarization suppresses this bias but also reduces the
magnetic-field information contained in the FID signal. We further show that the anisotropic response modifies noncollinear optical pumping and introduces an additional nonlinear contribution to the harmonic response under periodic driving. The resulting model retains the simplicity of a three-component Bloch description while improving the quantitative accuracy of transient and driven SERF magnetometry.
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Data Analysis and Statistics · Cross-listed from hep-ph
Prompting Particle Physics: Tokenized Multi-modal Foundation Models for Combinatorially Many Tasks
Nilotpal Kakati, Daniel Murnane, Baran Hashemi et al. (+5 more)
Reconstruction and simulation at a
collider experiment are long chains of specialised
algorithms, each tuned to a single step. We explore how one model can serve many of those steps at once, while still producing the intermediate objects (tracks, calorimeter
cells, clusters, particles and jets) that make the chain interpretable. To do so, we represent every object in a jet in a single shared
token vocabulary and train one model to map any subset of these modalities to any other. A task is then only a choice of which modalities to provide and which to request: particle flow, detector simulation and charged energy subtraction are all directions through the same set of weights. We train over all modality combinations, with a decoder emitting
tokens either autoregressively or in parallel. With tokenisation, both architectures train stably with little tuning. In evaluations, both models produce realistic reconstruction and simulation objects, with the autoregressive model particularly faithful to output from Geant4. The autoregressive model also outperforms a state-of-the-art particle flow
algorithm on many typical jet reconstruction metrics.
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All authors Nilotpal Kakati, Daniel Murnane, Baran Hashemi, Samuel Klein, Jeffrey Krupa, Eilam Gross, Lukas Heinrich, Michael Kagan
Atomic Physics
Microwave Power-to-Frequency Transduction in a Magnetically Initiated Rydberg Dissipative Time Crystal
Darmindra Arumugam
Microwave power-to-frequency transduction is demonstrated using the emergent oscillation frequency of a magnetically initiated Rydberg dissipative time
crystal (DTC). In a room-temperature 87Rb vapor, a ~14 G
magnetic field establishes a self-sustained DTC near 21 kHz, while a near-resonant 8.325 GHz field coupling the 63D3/2 to 64P3/2
manifold continuously shifts the autonomous oscillation frequency. The resulting field-to-frequency transfer function is strongly nonlinear, with a total fundamental-frequency excursion of ~6.7 kHz and peak responsivity approaching 1 kHz/(mV/cm). The nonlinear transition is substantially steeper than a simple quadratic saturation response and is captured by a self-consistent mean-field picture in which microwave-driven Rydberg-population redistribution modifies the collective resonance condition. Higher DTC harmonics preserve the same normalized transfer function while exhibiting approximately linear growth of absolute responsivity with harmonic order. These results establish an intrinsic many-body atomic route to RF amplitude-to-frequency conversion, enabling continuous frequency-domain sensing through an emergent atomic frequency without an externally engineered RF feedback oscillator, repeated optical spectral scans, or resolved Autler Townes splitting.
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Physics and Society
Vertices that belong to every minimum dominating set of a graph and their connection with transportation sharing systems with study cases in Campo de Gibraltar area
Mohammad Farhan, Dorota Kuziak, Iztok Peterin et al. (+1 more)
This study addresses a theoretical model regarding equity and accessibility challenges in designing shared transportation systems (such as micro-mobility networks) by applying graph-vertex domination setting. The work focuses on identifying dominating forced vertices, that represent nodes belonging to every dominating set of a graph of the smallest possible cardinality, and which correspond to critical, non-negotiable station locations essential for maintaining system efficiency and coverage. From a theoretical perspective, in the paper it is first demonstrated that determining whether a given vertex is a dominating forced vertex is co-NP-hard, establishing the computational infeasibility of exact identification in large networks. To analyze graph structures, sharp theoretical bounds on the maximum number of dominating forced vertices are established, proving that their count is bounded above by one-third of the order of the graph, and provide complete structural characterizations for graphs achieving this bound, as well as, trees with no dominating forced vertices. To overcome computational limits in practical urban settings, the study uses an iterated greedy metaheuristic framework to generate minimal dominating sets and approximate critical forced nodes based on their appearance frequency across iterations. The methodology is validated on strong grid graphs and applied to real-world road network models of Algeciras and La Línea de la Concepción, two cities in the area of Campo de Gibraltar, Spain, which successfully pinpoints candidate location points for scooter-sharing stations across both cities.
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All authors Mohammad Farhan, Dorota Kuziak, Iztok Peterin, Ismael G. Yero
History and Philosophy of Physics
Does a Second-Class Primary Constraint Generate a Gauge Transformation? Electromagnetisms and Gravities, Massless and Massive
J. Brian Pitts
In constrained
Hamiltonian dynamics there are two views regarding how first-class constraints generate gauge transformations: individually or only in a certain combination, the Rosenfeld-Anderson-Bergmann-Castellani gauge generator. This gauge generator $G$ preserves Hamilton's equations and changes the canonical action at most by a boundary term;
Hamiltonian's equations are the Euler-Lagrange equations for the canonical action. Hence the canonical formalism is equivalent to the Lagrangian formalism, and indeed subsumed within it (in important examples) with many canonical momenta serving as auxiliary fields. $G$ generates transformations basically equivalent to the usual 4-dimensional Lagrangian expressions, such as a 4-gradient in electromagnetism or a space-time coordinate transformation (on-shell) in General Relativity. It has been shown recently that separate first-class constraints lead to inequivalent observables between Proca non-gauge and Stueckelberg gauge massive electromagnetism. There is, however, widespread agreement that second-class constraints do not generate gauge transformations. Here it is shown that in such a sense as the first-class primary constraint in Maxwell's theory generates a gauge transformation, the second-class primary constraint in Proca's massive electromagnetism also generates a gauge transformation. Likewise the second-class primary constraints in various massive
spin $2$ relatives of General Relativity generate as much of a gauge transformation as do the corresponding first-class primary constraints in GR. Hence the view that first-class constraints typically generate gauge transformations _individually_ faces a puzzle not faced by the gauge generator view.
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Space Physics
Electric Potential Patterns Forecasting in the Southern Hemisphere with Deep Learning Techniques
Francesco Pio Ramunno, Simone Mestici, Igino Coco et al. (+5 more)
Space weather disturbances driven by the solar wind can degrade satellite navigation, disrupt
radio communications, and threaten power infrastructure, making accurate forecasting of the high-latitude ionosphere's response a critical operational need. Existing approaches, empirical climatological models and physics-based magnetohydrodynamic simulations, either smooth out the ionosphere's time-dependent, non-linear response or are too computationally expensive for real-time use, while prior machine learning efforts have mostly targeted scalar indices rather than the full spatial structure of ionospheric convection. Here we train and compare three deep learning architectures, a probabilistic
diffusion model conditioned on multi-variate solar wind and interplanetary
magnetic field measurements at the L1 Lagrange point, an unconditioned
diffusion ablation, and a deterministic U-Net baseline, to forecast Southern Hemisphere high-latitude electric potential maps derived from SuperDARN radar observations. Using five years (2020–2025) of SuperDARN data synchronised with DSCOVR L1 measurements, we evaluate the models under a single-pass regime, an extended autoregressive rollout of up to 350 frames, and an out-of-distribution case study on the intense March 2015 St.\ Patrick's Day storm, unseen during training. We find that the relative advantage of the deterministic and probabilistic approaches is not fixed: the deterministic model is competitive over short horizons and calm conditions, while the
diffusion model's advantage grows and eventually dominates as the forecast horizon lengthens and the event becomes more dynamic, evidence that probabilistic, sample-based generative models are the more promising direction for operational, long-horizon space weather forecasting.
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All authors Francesco Pio Ramunno, Simone Mestici, Igino Coco, Maria Walach, Stefano Massetti, Maria Federica Marcucci, Brandon Panos, André Csillaghy
Optics · Cross-listed from quant-ph
Exact series formulas for the capacities of the amplitude damping channel
Stefano Pirandola
We derive explicit, absolutely convergent series formulas for the
quantum, unassisted classical, and
entanglement-assisted classical capacities of the
qubit amplitude damping channel, eliminating residual optimizations and implicit roots. We also obtain analytical upper and lower bounds on two-way assisted
quantum and private communication capacities by evaluating a balanced-squashing bound and optimizing the reverse coherent information. Our results characterize the optimal input populations, establish convergence and truncation properties, and provide analytical
benchmarks for
quantum and classical communication over dissipative channels.
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Optics · Cross-listed from quant-ph
Scalable Hybrid Device Architecture on Thin-Film Lithium Tantalate for Long Distance Quantum Network Nodes with Atomic Frequency Comb Quantum Memories
Ayed Sayem and Towsif Taher
Scalable photonic
circuits are essential for realizing practical
quantum networks beyond proof-of-principle demonstrations. Although diamond-based integrated photonics has been extensively investigated over the past two decades for
quantum networking applications, its integration into scalable photonic platforms remains challenging.
Quantum memories based on rare-earth-ion atomic frequency combs (AFCs) offer an attractive alternative; however, most demonstrations to date have relied on bulk free-space
crystals, limiting their scalability for practical implementations. In this work, we systematically investigate the potential for hybrid integration of AFC-based
quantum memories with a scalable integrated photonic platform. We propose a device architecture combining thin-film lithium tantalate (TFLT) nanophotonic
circuits with ytterbium-doped yttrium orthosilicate (Yb:YSO)
crystals for long-distance
quantum communication. Our analysis shows that the hybrid TFLT-YSO platform offers a promising and scalable pathway toward integrated
quantum memories and, ultimately, large-scale
quantum networks.
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Instrumentation and Detectors
Transition Radiation Detector Upgrade for the GlueX-III Charmonium Program
Lauren Kasper, Alexander Austregesilo, Cody Dickover et al. (+9 more)
The GlueX-III experiment at Jefferson Lab, planned to run in 2027-2029, will enhance the laboratory's capability to explore charmonium production near threshold and related aspects of QCD in the non-perturbative regime. A central upgrade for GlueX-III is a large-scale triple-GEM Transition Radiation Detector (TRD) optimized for electron identification and pion suppression in the
hadron-rich environment of fixed-target photoproduction. This document describes the motivation for and efforts related to such an upgrade, including the design, construction, and performance of a 720x528mm$^{2}$ triple-GEM-TRD prototype that was tested in the existing GlueX-II experimental acceptance during the 2025 run. Results validate the prototype's integration, timing performance, and electron identification capability. Its projected impact in the GlueX-III setting is discussed, with background reduction in a $J/\psi$ candidate sample demonstrated. The development and use of a modern TRD technology in a high-background environment at Jefferson Lab may inform detector design choices as a future upgrade path for the EIC.
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All authors Lauren Kasper, Alexander Austregesilo, Cody Dickover, Sean Dobbs, Sergey Furletov, Yulia Furletova, Ilya Larin, Lubomir Pentchev, Saheli Rakshit, Nizar Septian, Simon Taylor, Julia Velkovska
Medical Physics
Numerical Simulation of Electrical Properties in Cortical and Trabecular Bone: A Simplified Model
María José Cervantes, Catalina A. Cely-Ortíz, C. Manuel Carlevaro et al. (+1 more)
The electrical properties of biological tissues depend on their composition and microstructure and determine their response to applied electric fields. In bone tissue, these properties are closely related to its composition, porosity, and microstructural organization. However, simplified models that link bone microstructure (trabecular and cortical) to its effective electrical properties while accounting for the electrical anisotropy of cortical bone remain scarce. In this work, a multiscale approach was developed, combining finite element simulations with Bruggeman-based homogenization models. Trabecular bone was modeled using microCT derived geometries, and its effective conductivity was analyzed as a function of BV/TV and free water content. Cortical bone was represented by a simplified three-dimensional network of Haversian canals (axial = z) and Volkmann canals (transverse), incorporating their directional organization through an anisotropic Bruggeman formulation. The models captured the conductivity range reported experimentally for trabecular bone (approximately 25-200 mS/m) and reproduced the anisotropic behavior observed in the cortical FEM simulations, with higher conductivity in the axial direction. In the generated cortical geometries, 65.5% of the total porosity was associated with axial Haversian canals. At the macroscale, the tibia model with lower trabecular and cortical BV/TV and reduced cortical thickness showed an approximately 38-39% reduction in impedance relative to the reference model. These results indicate that microstructural variations in bone tissue are reflected in its effective electrical properties and in the macroscopic impedance of the model, providing a physical and computational basis for future investigations into the sensitivity of electrical measurements to changes in bone microstructure.
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All authors María José Cervantes, Catalina A. Cely-Ortíz, C. Manuel Carlevaro, Ramiro M. Irastorza
Optics
A unified sensitivity kernel for coherent wave tomography in radar, seismic, ultrasound, and optical imaging
Alexey Yamilov
Imaging an inhomogeneous medium with waves rests on the sensitivity of a boundary measurement to a change inside. Radar, seismology, medical ultrasound, and diffuse optics cast this sensitivity as a kernel of area-specific form. Here we show that all of them are built from two classes. The classes stem from the complex material parameter of the underlying wave equation, its real part determining the wave speed and its imaginary part the loss. Every kernel is the product of a forward and an adjoint field, and a real measurement retains one quadrature of that product. These quadratures are the two classes, and all kernels in ballistic and diffusive transport are combinations of them. We place the areas on one diagram of scattering strength and transport depth, exposing a shared computational limit set by wavefield storage, and propose a broadband on-shell compression that can relax that limit.
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