Instrumentation and Detectors
An ASME-Compliant Helium-4 Evaporation Refrigerator for the SpinQuest Experiment
Jordan D. Roberts, Vibodha Bandara, Kenichi Nakano et al. (+1 more)
This paper presents the design, safety basis, and commissioning results of a 1 K
liquid helium-4 (4He) evaporation refrigerator developed for the Fermilab SpinQuest Experiment (E1039). The system represents the first high power helium evaporation refrigerator operated in a fixed target scattering experiment at Fermilab and was engineered to comply with the Fermilab ES&H Manual (FESHM) requirements governing pressure vessels, piping, cryogenic systems, and
vacuum vessels. The design is mapped to ASME B31.3 (Process Piping) and the ASME Boiler and Pressure Vessel Code (BPVC) for pressure boundary integrity and overpressure protection, with documented compliance to FESHM Chapters 5031 (Pressure Vessels), 5031.1 (Piping Systems), and 5033 (
Vacuum Vessels). This work documents the methodology used to reach compliance and approval for the 4He evaporation refrigerator at Fermilab which the field lacks. Design considerations specific to the high radiation target-cave environment including remotely located instrumentation approximately 20 m from the cryostat are summarized, together with the relief-system sizing methodology used to accommodate transient heat loads from dynamic
nuclear polarization microwaves and the high-intensity
proton beam. Commissioning data from July 2024 confirms that the system satisfies all thermal performance and safety objectives.
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All authors Jordan D. Roberts, Vibodha Bandara, Kenichi Nakano, Dustin Keller
Fluid Dynamics
Dynamic subgrid-scale LES model for turbulent non-Newtonian flows: A priori and a posteriori analyses of Burgers turbulence
E. Amani, A. Ahmadpour and M.J. Aghajari
Large Eddy Simulation (LES) of turbulent non-Newtonian flows involves two additional closures, namely the Non-Newtonian SubGrid-Scale (NNSGS) stress tensor and filtered viscosity. Here, dynamic closures are proposed for NNSGS, eliminating the need for model calibration. In addition, for the primary evaluation of LES closures, two canonical case studies are designed by the extension of Newtonian forced Burgers
turbulence to include power-law viscosity rheology. The characteristics of the proposed non-Newtonian
turbulence are studied carefully using direct numerical simulation. For instance, it is revealed that the shear-thinning effect intensifies the small-scale motions and elevates the energy spectrum function at high wave-numbers. The subsequent a priori and a posteriori studies manifest that the NNSGS modeling is important for the present test cases. The omission of this term results in the under-prediction of kinetic energy due to the substantial backscatter effect of NNSGS. The proposed dynamic Smagorinsky based closure is recommended, based on the correlation coefficient measure in the a priori tests and the energy spectrum function in the a posteriori tests, for LES of
turbulence at high shear-thinning rheology.
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Geophysics
Lithological Controls on the Permeability of Geologic Faults: Surrogate Modeling and Sensitivity Analysis
Hannah Lu, Llu{\i}s Salo-Salgado and Ruben Juanes
Fault zones exhibit complex and heterogeneous permeability structures influenced by stratigraphic, compositional, and structural factors, making them critical yet uncertain components in subsurface flow modeling. In this study, we investigate how lithological controls influence fault permeability using the PREDICT framework: a probabilistic workflow that couples
stochastic fault geometry generation, physically constrained material placement, and flow-based upscaling. The flow-based upscaling step, however, is a very computationally expensive component of the workflow and presents a major bottleneck that makes global sensitivity analysis (GSA) intractable, as it requires millions of model evaluations. To overcome this challenge, we develop a neural network surrogate to emulate the flow-based upscaling step. This surrogate model dramatically reduces the computational cost while maintaining high accuracy, thereby making GSA feasible. The surrogate-model-enabled GSA reveals new insights into the effects of lithological controls on fault permeability. In addition to identifying dominant parameters and negligible ones, the analysis uncovers significant nonlinear interactions between parameters that cannot be captured by traditional local sensitivity methods.
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Optics
Bayesian inference for precise and uncertainty-quantified single-shot widefield interferometric geometrical nanometrology
Damian Suski, Maria Cywinska, Julianna Winnik et al. (+5 more)
Advanced geometrical nanometrology is critical for process control in
semiconductor manufacturing, supporting applications in, e.g., photonic integrated
circuits, nanoelectronics, and emerging
quantum and optoelectronic technologies. Widefield interferometric approach provide a cost-effective, non-destructive solution for characterizing
semiconductor optical waveguides, which are fundamental to nanophotonic devices. This work presents a
Bayesian inference framework, implemented using Dynamic Nested Sampling, for estimating
geometric parameters - such as width and height - of a
semiconductor optical waveguide from a single widefield interferogram. The proposed framework reduces the need of leveraging near field scanning microscopy methods for measurements. The notable advantage is that
Bayesian statistics not only provide the estimated parameter values but also quantify the uncertainty of the inference results and the fitness of the used model. The proposed full-field, single-shot interferometric approach, supported by
Bayesian-based data analysis, achieves high accuracy and sensitivity - down to successful measurement of 8 nm rib waveguide - while remaining resilient to noise. Thus, the demonstrated methodology provides a cost-effective, robust, and scalable tool for
semiconductor fabrication monitoring and process verification, as confirmed by both numerical simulations and experimental validation on optical waveguides. This method contributes to high-precision nanometrology by integrating advanced statistical modeling and inference techniques.
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All authors Damian Suski, Maria Cywinska, Julianna Winnik, Michal Jozwik, Piotr Zdankowski, Azeem Ahmad, Balpreet S. Ahluwalia, Maciej Trusiak
Computational Physics · Cross-listed from quant-ph
Optimal Interpolation of Entanglement Purification Protocols
Matthew Barber and Stefano Pirandola
Bipartite
entanglement purification is the conversion of copies of weakly entangled pairs shared between two separated parties into a smaller number of strongly entangled shared pairs using only local operations and classical communication. Choosing between different
entanglement purification protocols generally involves weighing up a trade-off between the ratio of strongly entangled pairs produced to weakly entangled pairs consumed, which we call the rate of the protocol, and the degree of the
entanglement of the strongly entangled pairs, typically measured by the fidelity of those pairs to maximally entangled states. By randomly choosing a protocol according to a probability distribution over a list of protocols for each pair we want to produce, we can achieve rates and fidelities not achieved by any of the original protocols. Here, we show how to choose this distribution to maximize the rate at which we produce
qubit pairs with a given fidelity to a Bell state or, equivalently, to maximize the fidelity to a Bell state of the
qubit pairs produced at a given rate. We investigate both the asymptotic case, where the number of initial pairs goes to infinity, and the finite-size regime, where protocols are restricted to a finite number of weakly entangled pairs.
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Chemical Physics · Cross-listed from astro-ph.EP
Prebiotic Chemistry Insights for Dragonfly: Thermodynamics of Amino Acid Synthesis in Selk Crater on Titan
Ishaan Madan and Ben K.D. Pearce
Saturnian moon Titan presents a compelling testbed for probing prebiotic chemistry beyond early Earth. Impact-generated melt pools provide transient aqueous habitats in an otherwise cryogenic environment. We use Cantera
equilibrium models to assess whether mixtures of hydrogen cyanide (HCN), acetylene (C2H2), and ammonia (NH3) can drive amino acid synthesis in Selk-sized craters. Across twenty-one amino acids (twenty proteinogenic plus beta-alanine), NH3-free systems yield only proline, alanine, and beta-alanine, whereas adding as little as 1% NH3 (relative to H2O) renders almost the full suite accessible, with yields peaking at 2% and tapering thereafter. The NH3-free alanine result implies alternative pathways beyond classical Strecker or aminonitrile hydrolysis, suggesting acetylene, abundant on Titan but scarce on early Earth, as a plausible feedstock. We identify acrylonitrile (detected on Titan) as a thermodynamically favorable intermediate that can convert to alanine under aqueous conditions in an NH3-free pathway. For glycine and alanine production from nitrile hydrolysis, comparison with laboratory kinetics shows that our
equilibrium models predict near-complete conversion, while observed rates yield only partial products over weeks. Yet estimated chemical equilibration times (years-centuries) are far shorter than melt lifetimes, supporting plausibility of
equilibrium in situ. These predictions are directly testable with Dragonfly mass spectrometer (DraMS), for which we recommend pre-flight standards to test proline, alanine, beta-alanine, cysteine, and methionine. The first three offer the best chances for amino acid detection regardless of ammonia availability; the latter two offer diagnostic tools for determining the presence of reactive sulfur in post-impact Titan ponds.
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Optics
Systematic validation of time-resolved diffuse optical simulators via non-contact SPAD-based measurements
Weijia Zhao, Linlin Li, Kaiqi Kuang et al. (+6 more)
Objective: Time-domain diffuse optical imaging (DOI) requires accurate forward models for
photon propagation in scattering media. However, existing simulators lack comprehensive experimental validation, especially for non-contact configurations with oblique illumination. This study rigorously evaluates three widely used open-source simulators, including MMC, NIRFASTer, and Toast++, using time-resolved experimental data. Approach: All simulations employed a unified mesh and point-source illumination. Virtual source correction was applied to FEM solvers for oblique incidence. A time-resolved DOI system with a 32 $\times$ 32 single-
photon avalanche diode (SPAD) array acquired transmission-mode data from 16 standardized phantoms with varying absorption coefficient $\mu_a$ and reduced scattering coefficient $\mu_s'$. The simulation results were quantified across five metrics: spatial-domain (SD) precision, time-domain (TD) precision, oblique beam accuracy, computational speed, and mesh-density independence. Results: Among three simulators, MMC achieves superior accuracy in SD and TD metrics, and shows robustness across all optical properties. NIRFASTer and Toast++ demonstrate comparable overall performance. In general, MMC is optimal for accuracy-critical TD-DOI applications, while NIRFASTer and Toast++ suit scenarios prioritizing speed with sufficiently large $\mu_s'$. Besides, virtual source correction is essential for non-contact FEM modeling, which reduced average errors by > 34% in large-angle scenarios. Significance: This work provides benchmarked guidelines for simulator selection during the development phase of next-generation TD-DOI systems. Our work represents the first study to systematically validate TD simulators against SPAD array-based data under clinically relevant non-contact conditions, bridging a critical gap in biomedical optical simulation standards.
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All authors Weijia Zhao, Linlin Li, Kaiqi Kuang, Yang Lin, Claudio Bruschini, Jiaming Cao, Ting Li, Edoardo Charbon, Wuwei Ren
Computational Physics · Cross-listed from cs.LG
HeatGen: A Guided Diffusion Framework for Multiphysics Heat Sink Design Optimization
Hadi Keramati, Morteza Sadeghi and Rajeev K. Jaiman
This study presents a generative optimization framework based on a guided denoising
diffusion probabilistic model (DDPM) that leverages surrogate gradients to generate heat sink designs minimizing pressure drop while maintaining surface temperatures below a specified threshold. Geometries are represented using boundary representations of multiple fins, and a multi-fidelity approach is employed to generate training data. Using this
dataset, along with vectors representing the boundary representation geometries, we train a denoising
diffusion probabilistic model to generate heat sinks with characteristics consistent with those observed in the data. We train two different residual neural networks to predict the pressure drop and surface temperature for each geometry. We use the gradients of these surrogate models with respect to the design variables to guide the geometry generation process toward satisfying the low-pressure and surface temperature constraints. This inference-time guidance directs the generative process toward heat sink designs that not only prevent overheating but also achieve lower pressure drops compared to traditional optimization methods such as CMA-ES. In contrast to traditional black-box optimization approaches, our method is scalable, provided sufficient training data is available. Unlike traditional
topology optimization methods, once the model is trained and the heat sink world model is saved, inference under new constraints (e.g., temperature) is computationally inexpensive and does not require retraining. Samples generated using the guided
diffusion model achieve pressure drops up to 10 percent lower than the limits obtained by traditional black-box optimization methods. This work represents a step toward building a foundational generative model for electronics cooling.
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Optics
HAMscope: a snapshot Hyperspectral Autofluorescence Miniscope for real-time molecular imaging
Alexander Ingold, Richard G. Baird, Dasmeet Kaur et al. (+5 more)
We introduce HAMscope, a compact, snapshot hyperspectral autofluorescence miniscope that enables real-time, label-free molecular imaging in a wide range of biological systems. By integrating a thin
polymer diffuser into a widefield miniscope, HAMscope spectrally encodes each frame and employs a probabilistic deep learning framework to reconstruct 30-channel hyperspectral stacks (452 to 703 nm) or directly infer molecular composition maps from single images. A scalable multi-pass U-Net architecture with
transformer-based
attention and per-pixel uncertainty estimation enables high spatio-spectral fidelity (mean absolute error ~ 0.0048) at
video rates. While initially demonstrated in plant systems, including lignin, chlorophyll, and suberin imaging in intact poplar and cork tissues, the platform is readily adaptable to other applications such as neural activity mapping, metabolic profiling, and histopathology. We show that the system generalizes to out-of-distribution tissue types and supports direct molecular mapping without the need for spectral unmixing. HAMscope establishes a general framework for compact, uncertainty-aware spectral imaging that combines minimal optics with advanced deep learning, offering broad utility for real-time biochemical imaging across neuroscience, environmental monitoring, and biomedicine.
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All authors Alexander Ingold, Richard G. Baird, Dasmeet Kaur, Nidhi Dwivedi, Reed Sorenson, Leslie Sieburth, Chang-Jun Liu, Rajesh Menon
Computational Physics · Cross-listed from cond-mat.str-el
Accelerating two-dimensional tensor network optimization by preconditioning
Xing-Yu Zhang, Qi Yang, Philippe Corboz et al. (+2 more)
We revisit gradient-based optimization for infinite projected entangled pair states (iPEPS), a tensor network ansatz for simulating many-body
quantum systems. This approach is hindered by two major challenges: the high computational cost of evaluating energies and gradients, and an ill-conditioned optimization landscape that slows convergence. To reduce the number of optimization steps, we introduce an efficient preconditioner derived from the leading term of the metric tensor. We
benchmark our method against standard optimization techniques on the Heisenberg and Kitaev models, demonstrating substantial improvements in overall computational efficiency. Our approach is broadly applicable across various contraction schemes, unit
cell sizes, and
Hamiltonians, highlighting the potential of preconditioned optimization to advance tensor network
algorithms for strongly correlated systems.
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All authors Xing-Yu Zhang, Qi Yang, Philippe Corboz, Jutho Haegeman, Wei Tang
Fluid Dynamics
Conservation of magnetic-helicity fluctuations due to spatial decorrelation of fluxes in decaying MHD turbulence
Justin Kin Jun Hew, David N. Hosking, Christoph Federrath et al. (+2 more)
Hosking & Schekochihin (2021, Phys. Rev. X 11, 041005) have proposed that statistically isotropic decaying MHD
turbulence without net
magnetic helicity conserves the mean square fluctuation level of
magnetic helicity in large volumes – or, equivalently, the integral over space of the two-point correlation function of the
magnetic-helicity density, denoted $I_H$. Formally, the conservation and gauge invariance of $I_H$ require the vanishing of certain boundary terms related to the strength of long-range spatial correlations. These boundary terms represent the ability (or otherwise) of the
turbulence to organise fluxes over arbitrarily large distances to deplete or enhance fluctuations of
magnetic helicity. In this work, we present a theory of these boundary terms, employing a methodology analogous to that of Batchelor & Proudman (1956, Philos. Trans. R. Soc. A 248, 369) to determine the relevant asymptotic forms of correlation functions. We find that long-range correlations of sufficient strength to violate the conservation of $I_H$ cannot develop dynamically if the
evolution equation for the
magnetic vector potential is chosen to be local in space. Likewise, we find that such correlations cannot develop for a wide class of gauge choices that make this equation non-local (including the Coulomb gauge). Nonetheless, we also identify a class of non-local gauge choices for which correlations that are sufficiently strong to violate the conservation of $I_H$ do appear possible. We verify our theoretical predictions for the case of the Coulomb gauge with measurements of correlation functions in a high-resolution numerical simulation.
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All authors Justin Kin Jun Hew, David N. Hosking, Christoph Federrath, James R. Beattie, Neco Kriel
Fluid Dynamics
Local chemotactic response of $Escherichia$ $coli$ in fluid and near surfaces
Adam Gargasson, Julien Bouvard, Carine Douarche et al. (+2 more)
Bacteria can adjust their swimming behaviour in response to chemical variations, a phenomenon known as chemotaxis. This process is characterised by a drift velocity that depends non-linearly on the concentration of chemical species and its "local" gradient. To study this process more effectively, we optimised a 3-channel microfluidic device to generate a stable, linear concentration profile of chemoattractants. This setup allows us to monitor the response of $Escherichia$ $coli$ to casamino acids or $\alpha$-methyl-DL-aspartic acid at the individual level. By analysing the movement of a population of individuals both in fluid and on surfaces, we achieve faster, more accurate quantification of the population's chemotactic response. In the fluid, the chemotactic response is described by the equation $v_c=\chi(c) \nabla c$, with $\chi(c) = \chi_0 /[(1 + c/c_-)(1 + c/c_+)]$ the chemotactic susceptibility. For $c_- \ll c \ll c_+$, i.e. when bacteria perform chemotaxis, the bacterial chemotactic velocity is proportional to the concentration gradient divided by the concentration and $v_c \propto \nabla c/c = \nabla (\log c)$. However, on surfaces, the chemotactic flux is inhibited.
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All authors Adam Gargasson, Julien Bouvard, Carine Douarche, Peter Mergaert, Harold Auradou
Plasma Physics
Restoring momentum conservation to magnetized quasilinear diffusion
I. E. Ochs
Wave interactions with magnetized particles underly many
plasma heating and current drive technologies. Typically, these interactions are modeled by bounce-averaging the quasilinear Kennel-Engelmann
diffusion tensor over the particle orbit. However, as an object derived in a two-dimensional space, the Kennel-Engelmann tensor does not fully respect the conservation of four-momentum required by the action conservation
theorem, since it neglects the absorption of perpendicular momentum. This defect leads to incorrect predictions for the wave-induced cross-field particle transport. Here, we show how this defect can easily be fixed, by extending the tensor from two to four dimensions and matching the form required by four-momentum conservation. The resulting extended tensor, when bounce-averaged, recovers the form of the
diffusion paths required by action-angle
Hamiltonian theory. Importantly, the extended tensor should be easily implementable in Fokker-Planck codes through a mild modification of the existing Kennel-Engelmann tensor.
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Optics
Highly efficient DUV generation at 100 kHz via Yb pumped four-wave mixing in stretched hollow-core fibers
Ruaridh Forbes, Paul Hockett and Rune Lausten
We report the generation of the fifth harmonic of Yb at 206 nm with pulse energies exceeding 16 $\mu$J and durations of approximately 100 fs at a repetition rate of 100 kHz. The deep ultraviolet pulses are produced using four-wave difference frequency mixing in a He-filled stretched hollow-core fiber, driven by a pump at 343 nm and seeded at 1030 nm. Guided by simulations, we carefully optimize the process, resulting in a conversion efficiency of $\sim$30% from the 343 nm pump beam.
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Instrumentation and Detectors
Photothermal resistivity alignment of optical fibers to SNSPD
Martin Baránek, Dušan Lorenc, Tomáš Ščepka et al. (+5 more)
We demonstrate a straightforward optoelectronic fiber
alignment technique for superconducting nanowire single-
photon detectors (SNSPDs) that exploits the temperature-dependent resistance of the nanowire under optical absorption. The target nanowire is illuminated via the fiber, and the local absorption of light heats the wire, causing a change in its resistivity. Scanning the fiber over the nanowire, the change in its resistivity is monitored by lock-in amplifier, mapping the spatial photothermal response correlated to absorption and coupling efficiency. The maximum of the response corresponds to optimal fiber-SNSPD
alignment. This method allows for aligning the fiber to the center of the meander with sub-micron precision. The response is robust to variations in the angle and height of the fiber, providing an alternative or complement to fiber-to-chip
alignment methods based on the back-reflection or transmission measurement.
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All authors Martin Baránek, Dušan Lorenc, Tomáš Ščepka, Ján Šoltýs, Iuliia Vetrova, Štefan Haščík, Miroslav Grajcar, Pavol Neilinger
Medical Physics · Cross-listed from nucl-ex
$\beta^+$ radioactive nuclei created during proton therapy
Izabela Skwira-Chalot, Przemysław Sekowski, Agata Taranienko et al. (+4 more)
During
proton therapy, the beam flux decreases due to inelastic interactions with
nuclei. At the highest energies used in
proton therapy around 25%
protons initiate
nuclear reactions. This report presents the cross section measurements of
proton-induced production of three $\beta^+$ emitters – $^{11}$C, $^{13}$N, $^{15}$O – with half-lives between 2 and 20 minutes, using solid C, BN and SiO$_2$ targets. Stacks of up to 15 targets were irradiated simultaneously with
proton beams of kinetic energy below 58 MeV at the AIC-144 cyclotron of the Institute of
Nuclear Physics, Polish Academy of Sciences. The measured cross sections follow the excitation function obtained in the previous experiments, with uncertainty of a few percent.
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All authors Izabela Skwira-Chalot, Przemysław Sekowski, Agata Taranienko, Adam Spyra, Tomasz Matulewicz, Jan Swakoń, Joanna Matulewicz
Optics
Depth-enhanced molecular imaging with two-photon oblique plane microscopy
Kevin Keomanee-Dizon, Yaakov Clenman, Alejandra Duran et al. (+6 more)
High-numerical-aperture (NA) oblique plane microscopy enables noninvasive fluorescence imaging of subcellular dynamics without requiring radical sample modification. However, performance degrades at depth in multicellular specimens as scattering and refractive-index heterogeneity raise out-of-focus background. We report a two-
photon oblique plane microscope that improves resolution at depth by combining high-NA single-objective detection with multiphoton plane illumination. The microscope achieves $\sim\!300$ nm lateral and $\sim\!650$ nm axial resolution, with single-molecule sensitivity in vivo. Compared with two-
photon point scanning, the lower illumination NA delivers an order of magnitude lower peak intensity, enabling $>\!5\times$ faster volumetric acquisition (up to $3.25 \times 10^6$ voxels s$^{-1}$) with reduced photodamage. In multicellular contexts, near-infrared nonlinear excitation enhances contrast throughout the illumination depth by $\sim\!2\times$ and restores volumetric resolving power by $>\!2\times$ relative to linear excitation. We demonstrate these capabilities through molecular imaging of epithelial tissue, stem-
cell-derived gastruloids, and living fruit fly embryos, including multicolor transcription-factor dynamics, optogenetic subcellular control, and single-mRNA tracking, all using standard glass-based mounting.
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All authors Kevin Keomanee-Dizon, Yaakov Clenman, Alejandra Duran, Sergey Ryabichko, Pauline Hansen, Tohn Borjigin, Richard Thornton, Jared E. Toettcher, Harold M. McNamara
Optics
Modular multi-axis stepper motor driver with remote control for use in microscopy
Mathias S. Fischer, David Grass and Martin C. Fischer
Mechanical translation of samples along several axes is often required in microscopy, and automated positioning requires motorizing the translation stages. Stepper motors are commonly employed but require specialized driver electronics for reliable operation. Here we describe a low-cost, open-source controller design that drives several stepper motors and implements important safety features, such as monitoring of mechanical limit switches, stall detection, and protective software limits. If rotational encoders in the motors or linear encoders on the stages are available, the controller can monitor the stage position and correct it in a feedback loop. The stages can be controlled with serial commands via USB, or optionally from a remote control box that incorporates a joystick for real-time multi-axis speed control, rotary dials for fine axis positioning, and a display to indicate the stage positions. To ease adoption, we provide driver libraries in Python and C, a driver for the commonly used microscopy control software $\mu$Manager, and an example graphical user interface for calibration and testing.
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Instrumentation and Detectors
An ultrafast plenoptic-camera system for high-resolution 3D particle tracking in unsegmented scintillators
Till Dieminger, Saúl Alonso-Monsalve, Christoph Alt et al. (+7 more)
Neutrino detectors, particle calorimeters and some dark matter detectors require dense and massive active materials. An extremely fine
segmentation is desirable to achieve precise three-dimensional particle tracking. However, such systems introduce significant challenges in construction and demand a large number of readout electronics channels, leading to extremely high costs. In this article, we propose an alternative approach to elementary particle detection that enables ultrafast three-dimensional high-resolution imaging in large volumes of unsegmented scintillator. Enabling technologies are plenoptic systems and time-resolving single-
photon avalanche diode array imaging sensors. Together, they enabled us, using a plenoptic camera, to reconstruct the origin of single
photons in the scintillator. A case study focused on
neutrino detection demonstrates full event reconstruction with a spatial resolution of two hundred micrometres. This work paves the way for a class of particle detectors whose capabilities should be further enhanced through future developments and expanded to Cherenkov light detection, medical imaging and
neutron detection.
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All authors Till Dieminger, Saúl Alonso-Monsalve, Christoph Alt, Claudio Bruschini, Noemi Bührer, Edoardo Charbon, Kodai Kaneyasu, Matthew Franks, Tim Weber, Davide Sgalaberna
Fluid Dynamics · Cross-listed from cond-mat.soft
Spatio-temporal dynamics of surfactant driven secondary invasion in Gaussian pore networks
Debanik Bhattacharjee, Guy Z. Ramon and Yaniv Edery
Capillarity-dominated two-phase displacement in porous media often continues beyond the initial invasion-percolation (IP) breakthrough, as surfactants alter interfacial properties and reopen pathways once sealed by capillary forces. This study examines such secondary invasion, where adsorption-driven reductions in interfacial tension and contact-angle shifts lower entry thresholds in yet uninvaded throats, enabling further displacement at a fixed inlet pressure. To capture this process, we employ a time-dependent pore-network framework that couples IP with a reduced-order transport-adsorption module. Local fluxes are governed by Poiseuille flow, interfacial adsorption follows a Langmuir isotherm, and wettability
evolution is modeled through a calibrated phenomenological relation. Heterogeneity is prescribed by Gaussian throat-size distributions whose variance controls structural disorder. The resulting invasion trajectories are sigmoidal, consistent with Gaussian cumulative statistics, indicating that surfactant mass-transfer kinetics and network variance primarily rescale invasion timescales while preserving the overall functional form. The framework thus connects interfacial conditioning to time-varying capillary thresholds and reveals how surfactant-mediated processes govern post-breakthrough dynamics in heterogeneous porous systems.
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Optics
Tutorial: A practical guide to the alignment of defocused spatial light modulators for fast diffractive neural networks
Guillaume Noetinger, Tim Tuuva and Romain Fleury
The conjugation of multiple spatial light modulators (SLMs) enables the construction of optical diffractive neural networks (DNNs). To accelerate training, which is limited by the low refresh rate of SLMs, spatial multiplexing of the input data across different spatial channels is possible, maximizing the number of available spatial degrees of freedom (DoFs). Precise
alignment is required in order to ensure that the same physical operation is performed across each channel and thus the learning operation of the network. We present a semi-automatic procedure for this experimentally challenging
alignment resulting in a pixel-level conjugation. It is scalable to any number of SLMs and may be useful in wavefront shaping setups where precise conjugation of SLMs is required, e.g. for the control of optical waves in phase and amplitude. The resulting setup functions as an optical DNN capable of processing hundreds of inputs simultaneously, thereby reducing training times and experimental noise through spatial averaging. We further present a characterization of the setup and an
alignment method.
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Plasma Physics
Gyrokinetic Simulations of a Low Recycling Scrape-off Layer without a Lithium Target
Akash Shukla, Jonathan Roeltgen, Michael Kotschenreuther et al. (+7 more)
Low-recycling regimes are appealing because they entail a high edge temperature and low edge density which are good for core confinement. However, due to considerably enhanced heat flux, the exhaust problems become severe. In addition, in the low-recycling regime, the conventional fluid simulations may not capture the physics of the Scrape-Off Layer (SOL)
plasma that lies in the long mean free path regime; kinetic calculations become necessary. In this paper, by performing both Kinetic and fluid simulations, we explore the feasibility of a low-recycling regime in the
magnetic geometry of the Spherical Tokamak for Energy Production (STEP); kinetic effects come out to be crucial determinants of the SOL dynamics. The simulation results indicate that a high SOL temperature and low SOL density could be achieved even when the divertor target is not made of a low recycling material. This can be done by using a low recycling material as a wall material. This is an important step towards demonstrating the feasibility of a low-recycling scenario. Lithium, a commonly used low recycling material, tends to evaporate at high heat fluxes which counteracts the desired high temperature, low density regime, and materials that can handle high heat fluxes are generally high recycling. Comparisons of gyrokinetic and fluid simulation results indicate that one can take advantage of kinetic effects to address some of the issues associated with a low-recycling SOL. Specifically, kinetic simulations show better confinement of
impurities to the divertor region and greater broadening of the heat flux width due to drifts when compared with fluid simulations.
Impurity confinement would help prevent core contamination from sputtering, and a broader heat flux width would help reduce the peak heat load at the target in the absence of detachment.
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All authors Akash Shukla, Jonathan Roeltgen, Michael Kotschenreuther, David R. Hatch, Manaure Francisquez, James Juno, Tess N. Bernard, Ammar Hakim, Gregory W. Hammett, Swadesh M. Mahajan
Fluid Dynamics
Diffuse-interface modeling and simulation of the freezing of binary fluids with the Marangoni effect
Jiangxu Huang, Zhenhua Chai, Xi Liu et al. (+1 more)
This paper proposes a diffuse-interface model for simulating gas-
liquid-solid multiphase flows involving solid-
liquid phase change, solute transport, and the Marangoni effect. In this model, a phase-field method is employed to capture the
evolution of fluid-fluid interfaces, while an enthalpy-based approach is used to describe the temperature field and implicitly track the solid-
liquid interface. Solute transport is modeled using a constrained scalar-transport model combined with a pseudo-potential concentration approach. The proposed diffuse-interface model satisfies the reduction consistency, and can degenerate to the conservative phase-field method for incompressible two-phase flow and the classical enthalpy method for binary material solidification in an appropriate way. Furthermore, the model not only can preserve the mass conservation, but also can capture the volume change induced by phase change. To solve the diffuse-interface model, a
lattice Boltzmann (LB) method is then developed, and the numerical tests demonstrate that the method has a good performance in the study of the freezing process coupled with Marangoni flow, phase-change-induced volume change, and solute transport. Finally, the model is applied to investigate the freezing dynamics of a system containing an insoluble
impurity, revealing the complex interaction between the advancing freezing front and the
impurity. It is found that the numerical results are in good agreement with experimental data.
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All authors Jiangxu Huang, Zhenhua Chai, Xi Liu, Changsheng Huang
Medical Physics · Cross-listed from eess.IV
Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement
Felix F Zimmermann
Ultra-low-field (ULF) MRI promises broader accessibility but suffers from low signal-to-noise ratio (SNR), reduced spatial resolution, and contrasts that deviate from high-field standards. Image-to-image translation can map ULF images to a high-field appearance, yet efficacy is limited by scarce paired training data. Working within the ULF-EnC challenge constraints (50 paired 3D volumes; no external data), we study how task-adapted data augmentations impact a standard deep model for ULF image enhancement. We show that strong, diverse augmentations, including auxiliary tasks on high-field data, substantially improve fidelity. Our submission ranked third by
brain-masked SSIM on the public validation leaderboard and fourth by the official score on the final test leaderboard. Code is available at https://github.com/fzimmermann89/low-field-enhancement.
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Optics
Influence of Modulation Frequency Stabilization on Spectral Noise of Electromagnetically Induced Transparency
Hou Jinghua, Su Nan, Liu Yao et al. (+3 more)
The conversion of the modulation signal from the coupling light to the probe light and the conversion of the additional phase noise from the coupling light to the amplitude noise of the probe light in the electromagnetically induced transparency (EIT) spectrum of the cesium atomic ladder type three level system were investigated by detecting the probe light output noise spectrum. It was demonstrated that frequency stabilization via external modulation can effectively avoid the transfer of modulation noise. In the experiment, two frequency stabilization methods external modulation transfer spectroscopy and internal modulation transfer spectroscopy were employed to achieve the frequency stabilization of the coupling light. The probe light noise spectra at the frequency stabilized output terminal and the detection output terminal were measured under both methods.
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All authors Hou Jinghua, Su Nan, Liu Yao, Liu Zhihui, Zhang Yuchi, He Jun
Chemical Physics
A Molecular Density Functional Theory of aqueous electrolytic solution
Guillaume Jeanmairet, Luc Belloni and Daniel Borgis
We propose a generalisation of molecular density functional theory to describe inhomogeneous solvent mixture, with the objective of modelling electrolytic solutions. Two electrolytic models are presented, both within the HNC approximation. The first one is a two-components mixture representing a primitive-like model of sodium chloride, where the solvent is described as a dielectric continuum. This popular model has the advantage of simplicity, as the ions densities solely depend on spatial coordinates. Additionally, we develop a realistic three-components electrolyte model, in which water solvent is described by a third density field that depends on both spatial and orientational coordinates. The proposed methodology and its tridimensional implementation (3 spatial coordinates and 3 Euler angles) are validated by comparing the solvation properties of a sodium cation with the predictions of integral equation theory solved in 1D (1 intermolecular distance and 5 Euler angles), showing near-perfect agreement. This methodology enables the study of solvation properties of solutes of arbitrary shapes in electrolytic solutions, as demonstrated with the prototypical N-methylacetamide molecule immersed in both electrolytic solution models.
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Fluid Dynamics
Active bacterial pattern formation in evaporating droplets
Twan J.S. Wilting, Adriana W.B.P. Reijnier, Michiel H.M. Brebels et al. (+3 more)
Bacteria living on surfaces are often confined to droplets. When these droplets evaporate, the motion of the
liquid-air interface and the associated internal capillary flow confine the bacteria. Here we study how E. coli bacteria interact with this capillary confinement and agglomerate at the droplet's contact line. We identify three different types of bacterial pattern formation that depend on the bacterial activity and the environmental conditions imposed by the evaporating droplet. When the evaporation is fast, the bacteria are slow or the suspension is dilute, a uniform contact-line deposit forms. However, when the capillary confinement concentrates the bacteria at the contact line beyond a critical number density, localized collective motion spontaneously emerges. In that case, the bacteria induce a local stirring of the
liquid that allows them to self-organize into periodic patterns and enables them to collectively escape from the contact line. At very high number densities, these periodic patterns get destabilized by bacterial
turbulence in the bulk of the droplet resulting in the formation of mobile bacterial plumes at the contact line. Our results show how the subtle interplay between the bacteria and the capillary flow inside the droplet that surrounds them governs their dispersal.
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All authors Twan J.S. Wilting, Adriana W.B.P. Reijnier, Michiel H.M. Brebels, Alexandre Villie, Remy Colin, Hanneke Gelderblom
Biological Physics · Cross-listed from cond-mat.soft
Broadband Dielectric and THz Spectroscopy on Bio-Related Matter: Water, Amino Acids, Proteins, and Blood
Peter Lunkenheimer, Sebastian Emmert, Martin Wolf et al. (+1 more)
In the present work, we examine the relevance and proper interpretation of broadband-dielectric and THz-spectroscopy data for the investigation of various types of biological matter. We provide an overview of the rich variety of different dynamic processes that can be detected by these experimental methods. Several experimental examples are discussed in detail, helping to understand the information that can be drawn from such studies. This includes dielectric spectra, extending well into the GHz region, for pure water, which can be considered as a simple but highly important biological molecule. We also discuss results for a prototypical aqueous solution of a
protein, belonging to one of the most important classes of biological macromolecules. Moreover, we examine broadband dielectric spectra on blood as an example of functional biological matter in organisms. To demonstrate the relevance of THz spectroscopy for the investigation of biological molecules, we finally treat such experiments applied to different amino acids.
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All authors Peter Lunkenheimer, Sebastian Emmert, Martin Wolf, Alois Loidl
Physics and Society
Global Population and Carrying Capacity in the Anthropocene: the Relative Growth Rate Insight
Aleksandra Drozd-Rzoska, Agata Angelika Sojecka and Sylwester J. Rzoska
This report provides insights into global population dynamics since the beginning of the Anthropocene, focusing on empirical data and minimizing a priori the impact of model assumptions. It explores the Relative Growth Rate concept, introduced recently to global population studies by Lehman et al. [PNAS 118, e2024150118 (2021)] and subsequently extended to its analytical counterpart [PLoS ONE 20, eo323165 (2025)]. The analysis reveals a general non-monotonic growth pattern in the Anthropocene, emphasizing the uniqueness of the Industrial Revolution era. For the first 290 years, the Doomsday critical scaling provides a superior description for population changes, with a singularity at 2026. This is followed by the crossover to an exceptional 'reversed criticality' patterm, which has held over the last six decades, to the present day. The analysis suggests that the
evolution of the human Real Intelligence (RI) population during the Innovations-driven Industrial Revolution times - a period of rapidly increasing connectivity and complexity - can serve as a model counterpart for the puzzling dynamics of Artificial Intelligence (AI) growth. The final conclusion is positive: the catastropic Doomsday singularity can be avoided due to the generic system constraints , both for RI and AI.
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Data Analysis and Statistics · Cross-listed from cs.LG
GAMMA_FLOW: Guided Analysis of Multi-label spectra by MAtrix Factorization for Lightweight Operational Workflows
Viola Rädle, Tilman Hartwig, Benjamin Oesen et al. (+4 more)
GAMMA_FLOW is an open-source Python package for real-time analysis of spectral data. It supports classification, denoising, decomposition, and outlier detection of both single- and multi-component spectra. Instead of relying on large, computationally intensive models, it employs a supervised approach to non-negative
matrix factorization (NMF) for dimensionality reduction. This ensures a fast, efficient, and adaptable analysis while reducing computational costs. gamma_flow achieves classification accuracies above 90% and enables reliable automated spectral interpretation. Originally developed for gamma-ray spectra, it is applicable to any type of one-dimensional spectral data. As an open and flexible alternative to proprietary software, it supports various applications in research and industry.
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All authors Viola Rädle, Tilman Hartwig, Benjamin Oesen, Emily Alice Kröger, Julius Vogt, Eike Gericke, Martin Baron