History and Philosophy of Physics · Cross-listed from cs.AI
Spacetime Formation under Requirements: Contextual Realization and Form-Dependent Probability
Song-Ju Kim
Quantum cognition often explains order effects, contextuality, and violations of the law of total probability by replacing classical probability with
quantum probability on a fixed event structure. This paper proposes a different interpretation:
quantum probability is the fixed-spacetime projection of contextual spacetime formation under finite-state requirements. The framework begins not with time, space, objects, or probabilities, but with requirements such as finite representational capacity, single-state semantic stability, context-sensitive intervention, avoidance of explicit context labels, coherent world-formation, and intersubjective transformability. When these requirements cannot be realized within a single global Boolean event structure, the mismatch appears, under fixed-spacetime projection, as noncommutativity, interference, and
quantum-like probability. Building on prior single-state approaches to contextuality, we reinterpret classical contextual bookkeeping cost as the fixed-spacetime shadow of contextual spacetime formation. Auxiliary
memory or context labels in a classical representation correspond, in this account, to holonomy-like mismatch among locally Boolean logic-worlds. The interference term is the cross term generated when locally classical realization contributions are nontrivially glued and projected back into a fixed classical spacetime form. The result is a transcendental-operational realist account: objecthood, eventhood, probability, and spacetime are treated as forms of realization under requirements, while objectivity is defined by invariants preserved across observer- and history-dependent spacetime formations.
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Applied Physics
Different roles of quantum interference in a quantum dot photocell with two intermediate bands
Shun-Cai Zhao, Jing-Yi Chen and Xin Li
It is generally believed that
quantum interference can improve the transport of photo-generated carriers in a photocell, thereby improve the photoelectric conversion efficiency. In this work, we explicitly explore different roles of
quantum interferences in the photoelectric conversion efficiency in a
quantum dot (QD) photocell with two intermediate bands. The increasing transition rates from different charge transport channels bring out first increasing, then decreasing, and then monotonically decreasing photoelectric conversion efficiencies. And the photoelectric conversions increase with
quantum coherence generated by the upper transition rates owing to their robust
quantum interference. However, the conversion efficiency decrease with the
quantum interference induced by two lower-transition rates due to the shortened population lifetime in the intermediate bands. These results provide insight into different roles of
quantum interferences in photoelectric conversion efficiency, and may provide some artificial strategies to achieve efficient photoelectric conversion via the adjusted
quantum interferences in a QD photocell with multi-intermediate bands.
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General Physics
Binary Classifier Wire-Resistance Attack on KLJN: Impact of Narrowing the Resistor Gap
Mehmet Yildirim and Laszlo B. Kish
It is shown that narrowing the difference between the high and low resistor values in the Kirchhoff Law-Johnson Noise (KLJN) key exchange strongly affects security against a recently introduced binary classifier-based wire resistance attack. Using time domain simulations of a non-ideal KLJN loop with finite cable resistance, we generate large ensembles of secure (HL/LH) bits and evaluate the mean-square noise voltages at Alice's and Bob's ends. For each bit, these mean-square values form a point in a two-dimensional classifier plane, where the separation between the HL and LH point clouds characterizes the information available to an eavesdropper (Eve). We quantify Eve's success probability p by a simple decision rule based on the sign of the difference between the measured mean-square voltages. For strongly asymmetric resistors (for example RL = 4 kOhm and RH = 10 kOhm) and realistic wire resistances, the HL and LH clouds are fully separable and Eve's p approaches 1, which confirms that the classifier attack can practically recover all secure bits. As the low resistor value approaches the high one (for example RL = 9 kOhm and RH = 10 kOhm) at the same cable resistance, the HL and LH clouds increasingly overlap, and the measured p drops close to 0.7, approaching the ideal limit p = 0.5 as RL approaches RH. A surprising phenomenon is that, in this classifier-based scenario, increasing the wire resistance can decrease the information leak. This counterintuitive effect is strikingly the opposite of the behavior in the classical Bergou-Scheuer-Yariv wire resistance attack, where the mean-square voltages at the two ends of the wire are simply compared.
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Computational Physics · Cross-listed from cs.LG
Predicting Euler Characteristics and Constructing Topological Structure Using Machine Learning Techniques
Gyunghun Yu, Seong Min Park, Han Gyu Yoon et al. (+4 more)
This study proposes a novel approach to extract topological properties, specifically the Euler characteristic, from input images using neural networks without relying on large pre-existing
datasets but with a single
geometric image. Inspired by solid-state physics, where topological properties of
magnetic structures are derived from
spin field analysis, our model generates a unit vector field from an image, interpreted as a
spin configuration. The Euler characteristic is then predicted by computing the skyrmion number of this generated
spin configuration. Remarkably, the network learns to construct
chiral magnetic textures without access to ground-truth
chiral spin configurations, relying instead on only a single, simple
geometric image and the straightforward skyrmion number computation. Furthermore,
spin configurations generated by independently trained networks can be non-unique due to inherent degrees of freedom. To constrain these degrees of freedom and further refine the
spin configuration, we incorporate a
magnetic Hamiltonian, comprising exchange interaction, Dzyaloshinskii-Moriya (DM) interaction, and anisotropy, as an additional, physics-informed loss function. We validate the model's efficacy on complex geometrical shapes and demonstrate its applicability to practical tasks.
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All authors Gyunghun Yu, Seong Min Park, Han Gyu Yoon, Tae Jung Moon, Jun Woo Choi, Hee Young Kwon, Changyeon Won
Plasma Physics
Profiles of the Power Density and Other Properties of Hydrogen Magnetohydrodynamic Generators at Conditions
Osama A. Marzouk
Hydrogen and some of its derivatives (such as e-methanol, e-methane, and e-ammonia) are promising energy carriers that have the potential to replace conventional fuels, thereby eliminating their harmful environmental impacts. An innovative use of hydrogen as a zero-emission fuel is forming weakly ionized
plasma by seeding the combustion products of hydrogen with a small amount of an alkali metal vapor (cesium or potassium). This formed
plasma can be used as a working fluid in supersonic open-cycle magnetohydrodynamic (OCMHD) power generators. In these OCMHD generators, direct-current (DC) electricity is generated straightforwardly without rotary turbogenerators. In the current study, we quantitatively and qualitatively explore the levels of electric conductivity and the resultant volumetric electric output power density in a typical OCMHD supersonic channel, where thermal
equilibrium plasma is accelerated at a Mach number of two (Mach 2) while being subject to a strong applied
magnetic field (applied
magnetic-field flux density) of five teslas (5 T), and a temperature of 2300 K (2026.85 {\deg}C). We varied the total pressure of the pre-ionization seeded gas mixture between 1/16 atm and 16 atm. We also varied the seed level between 0.0625% and 16% (pre-ionization mole fraction). We also varied the seed type between cesium and potassium. We also varied the oxidizer type between air (oxygen-nitrogen mixture, 21-79% by mole) and pure oxygen. Our results suggest that the ideal power density can reach exceptional levels beyond 1000 MW/m3 (or 1 kW/cm3) provided that the total absolute pressure can be reduced to about 0.1 atm only and cesium is used for seeding rather than potassium.
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Optics
Permanent and Transient Synchronized Chaos in Large Arrays of Complex-Coupled Semiconductor Lasers
Zhanning Liu and Herbert G. Winful
Synchronized chaos has previously been predicted and observed in a small number (3) of mutually coupled
lasers. In this work, we demonstrate that this phenomenon can theoretically persist in significantly broader scenarios, extending to complex coupled arrays of up to 11
lasers and arrays with finite built-in disorder. We quantify the resulting high-dimensional dynamics by computing Lyapunov spectra and the associated Lyapunov dimension, confirming that the observed states are chaotic rather than quasi-periodic. Furthermore, we uncover a regime of transient synchronized chaos where the system eventually escapes from perfectly synchronized chaotic state into an asynchronous state. We find that the lifetime of these transient states follows a bi-exponential distribution.
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Chemical Physics · Cross-listed from quant-ph
Crossing the 12,000-atom barrier with heterogeneous quantum-classical supercomputing: quantum chemistry of protein-ligand complexes
Kenneth M. Merz, Jr., Akhil Shajan et al. (+22 more)
We develop a workflow decomposing a molecule into fragments via
quantum embedding and simulating them with a heterogeneous
quantum-classical (HQC) method. We sample fragment electronic configurations on two 156-
qubit quantum processors (ibm$\_$cleveland, ibm$\_$kobe), using up to 94
qubits, running 21,006
circuits for over 239 hours, collecting $3.0 \cdot 10^9$ measurement outcomes – the most resource-intensive HQC computation for
quantum chemistry to date. We compute fragment wavefunctions via optimized subspace diagonalization on supercomputers Fugaku, Miyabi-G, and ROQUO, achieving 72.5$\%$ parallel efficiency with scalable distributed linear
algebra kernels. We simulate two
protein-ligand complexes spanning dispersion- and electrostatics-dominated regimes (11,608, 12,635 atoms), demonstrate $>40\times$ increase in system size and up to $210\times$ improvement in accuracy over previous state-of-the-art, with HQC matching coupled-cluster (CCSD) accuracy in fragment energies. We present the first HQC
protein-ligand binding prediction using a mixed-basis set and an automated end-to-end workflow enabling practical HQC calculations of large
protein systems.
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All authors Kenneth M. Merz, Jr., Akhil Shajan, Danil Kaliakin, Fangchun Liang, Yuichi Otsuka, Tomonori Shirakawa, Lukas Broers, Han Xu, Miwako Tsuji, Mitsuhisa Sato, Seiji Yunoki, Ryo Wakizaka, Yukio Kawashima, Jun Doi, Hitomi Takahashi, Toshinari Itoko, Hiroshi Horii, Thaddeus Pellegrini, Javier Robledo Moreno, Kevin J. Sung, Ella Fejer, Robert Walkup, Seetharami Seelam, Mario Motta
Instrumentation and Detectors · Cross-listed from quant-ph
Low-cost Ultra-low Noise DAC System-on-Module for Scalable Ion-Trap Electrode Control
Mitchell G. Peaks, Mia M. Kaarls and Crystal Noel
A new design for an open-hardware Digital-to-Analog Converter System-on-Module is presented for low-noise ion-trap electrode control. The design specifications were established to fill the technical needs of a modular, scalable DC electrode control platform with sufficient bandwidth, noise characteristics and control flexibility. Critically, a priority was placed on supply-chain management considerations and cost effectiveness for scaling. The system is based upon the Texas Instruments DAC81416 and AMD Xilinx Spartan-7 FPGA for the analog signal and compute architecture respectively. Performance characterization of a prototype device suggests the design is suitable for a variety of ion-trap physics experiments and
quantum computing applications.
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Applied Physics
Fast reduction of electron-beam-activated graphene oxide by an infrared laser pulse
Israt Ali, Hilaire Mba, Matthieu Picher et al. (+3 more)
Rapid and controllable reduction of
graphene oxide (GO) remains a critical challenge for realizing its full technological potential. Here, we report the reduction of GO by a synergistic electron-beam-assisted single-pulse near-infrared (NIR)
laser process. We find that the efficiency of 1064-nm
laser photoreduction can be dramatically enhanced by prior exposure of the sample to a high-energy electron beam. Time-resolved electron energy-loss spectroscopy measured with a dynamic transmission electron microscope (DTEM) is used to quantitatively track the oxygen concentration
evolution after subsequent NIR
laser pulse irradiation. This finds an oxygen diffusivity for the process of 1.4 +/- 0.3 x 10$^{-9}$ m$^2$/s, which suggests oxygen is released by
diffusion through defects and imperfections in the film. Electron beam irradiation is found to change the optical absorptivity of GO in the NIR region, and COMSOL simulations are carried out to estimate the thermal heating cycle resulting from the
laser pulse. Structural characterization via selected-area electron diffraction (SAED) and high-resolution transmission electron microscopy (HRTEM) find localized restoration of sp$^2$ bonding accompanied by turbostratic disorder in the reduced GO. Together, these results point to a mechanism involving the creation of defects and vacancies produced by electron beam irradiation, which increases the efficiency of NIR light absorption and oxygen
diffusion normal to the layers. This study demonstrates the important role of such defects in controlling the photochemistry of GO and its photoreduction kinetics.
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All authors Israt Ali, Hilaire Mba, Matthieu Picher, Shruti Verma, Florian Banhart, Kenneth R. Beyerlein
Optics · Cross-listed from cs.LG
Machine Learning-Augmented Acceleration of Iterative Ptychographic Reconstruction
Bowen Zheng, Katayun Kamdin, David Shapiro et al. (+10 more)
Iterative ptychographic reconstruction
algorithms are widely used for coherent diffractive imaging but can exhibit slow convergence under realistic experimental conditions. We propose a machine learning-augmented approach that accelerates iterative ptychographic reconstruction by introducing a learned fast-forward operator applied during reconstruction. Following an initial warm-up using standard iterations, the fast-forward operator advances the reconstruction toward a more converged state, after which conventional iterative updates are resumed. This strategy preserves the physical consistency and flexibility of established ptychographic solvers while reducing the number of iterations required for convergence. The model is trained on diverse ptychographic
datasets and evaluated on experimental data acquired in a different year, demonstrating robustness and temporal generalization. Compared with conventional iterative solvers, the machine learning-augmented method achieves comparable reconstruction quality while converging faster in terms of Poisson negative log-likelihood, yielding over a two-fold reduction in wall-clock time. The approach has been integrated into an existing reconstruction pipeline and deployed in production at a synchrotron beamline, demonstrating practicality for real-time experimental operation.
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All authors Bowen Zheng, Katayun Kamdin, David Shapiro, Alexander Ditter, Dayne Sasaki, Emma Bernard, Roopali Kukreja, Petrus H. Zwart, Slavomír Nemšák, Apurva Mehta, Nicholas Schwarz, Alexander Hexemer, Tanny Chavez
Plasma Physics
High-throughput full-f gyrokinetics of the tokamak boundary
A.C.D. Hoffmann, M. Francisquez, T.N. Bernard et al. (+1 more)
Full-f gyrokinetics of the tokamak boundary have so far been limited to individual, expert-curated cases. We report hundreds of concurrent and unsupervised simulations of the edge and scrape-off layer (SOL) in a geometry based on the Tokamak à Configuration Variable (TCV), scanning triangularity, elongation and heating power, each evolving
turbulence for more than a millisecond, i.e. about two energy confinement times. Without tuned transport coefficients or experimental inputs, the ensemble reproduces the range of SOL heat-flux decay lengths measured in TCV, as well as the reduced SOL density fluctuations and the improved confinement of negative triangularity. The effect of triangularity on confinement is power dependent. At high power, negative triangularity improves the ion energy confinement by weakening the dominant microinstability rather than by a stronger sheared flow. At low power, positive triangularity flattens the ion temperature profile across the separatrix, which we attribute to a longer distance between the trapped ions and the cold recycling source. Local linear gyrokinetic calculations and the transport coefficients extracted from the turbulent fluxes consistently indicate a trapped-electron-mode regime, in which the conductive heat flux at the separatrix is carried by the electrons and the ion energy transport is convective. This article details the simulation setup, the ensemble statistics and the verification of the results, which are also summarized in a companion Letter.
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All authors A.C.D. Hoffmann, M. Francisquez, T.N. Bernard, G.W. Hammett
Atmospheric and Oceanic Physics · Cross-listed from cs.AI
Jinpai Zhao, Albert Cerrone, Joannes Westerink et al. (+1 more)
Large
language models and the
agents built on them are the most capable
reasoning machines ever built, and science is beginning to put them to work. Atmospheric and oceanic sciences are starting to see agentic integration, but coastal hydrodynamics remains largely untouched by AI. Simply,
agents cannot easily read its data products without saturating their context windows. A time frame from a coastal model can hold hundreds of megabytes, and if the archive's chunking does not suit the query, the
agent will spend more time downloading and compacting than gaining insights. After all, the archives were designed for storage, not for the fast, on-the-fly access
agents need.
Speech and music once hit the same wall. A minute of audio is millions of numbers, yet today's audio models hold conversations and write music. They "learned" to do this by "listening" to
tokens. Neural audio codecs turn waveforms into short sequences of discrete symbols that a model reads like words and decodes back into sound. Coastal water is a similar signal: dense, finely sampled, and repeatable. We are convinced it has an analogous compact native representation that preserves the salient physics.
Tokens are that representation: small integers, light enough for an
agent to sweep six months of a coastline in seconds.
Agents can then do more than simple lookup-related tasks; they can form and test their hypotheses as they explore the tokenized representation efficiently. Herein, we build this framework, using vector quantized variational autoencoders to compress tide level, the non-
tidal residual, and surface wind speed into 16x16 grids of
tokens along the US Atlantic and Gulf coasts.
Agents traverse and explore these grids (e.g., tracking, spatiotemporal trend analysis) considerably faster than raw archives in both on-prem and off-prem modalities, while correctly answering several types of under-specified queries.
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All authors Jinpai Zhao, Albert Cerrone, Joannes Westerink, Clint Dawson
Chemical Physics
Stochastic Cluster Expansion for Excited State Energies
Annabelle Canestraight, Russell Miller, Libor Veis et al. (+1 more)
Excited-state electronic structure in strongly correlated systems remains challenging due to the exponential scaling of the many-body
Hilbert space and the difficulty of constructing systematically controlled active spaces. Building on the
stochastic cluster expansion (SCE) framework previously developed for ground-state correlation energies, we extend the formalism to excitation gaps by expressing energy differences directly as a hierarchy of orbital-space cluster contributions. In this formulation, excitation energies are reconstructed from reduced-rank calculations involving a minimal frontier chemical subspace (FCS), treated exactly, together with
stochastic sampling of the remaining orbital environment. This approach eliminates the need for large or chemically preselected active spaces. We demonstrate the method on charge-transfer complexes and polyacenes, where accurate singlet-triplet gaps are obtained that agree with full-system results. The method converges with low-order cluster terms and provides a systematically improvable framework for excited states in correlated systems.
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All authors Annabelle Canestraight, Russell Miller, Libor Veis, Vojtech Vlcek
Optics
Two-Photon-Induced Direct 3D Printing of Freeform High-Index Phase-Change Sb2S3 Nanostructures
Abhrodeep Dey, Andrea Dellith, Anne Sauer et al. (+7 more)
Chalcogenides have recently emerged as an important class of phase-change materials (PCMs) for nanophotonics, owing to their very high refractive index (RI) and low optical loss in the visible to near-infrared range. They exhibit an ultralarge RI change (> 0.7) upon phase transition, which can be triggered by multiple stimuli such as electrical bias,
laser illumination or thermal heating. These properties make them highly appealing materials for flat optics and metasurface applications. Current nanophotonic implementations of chalcogenide PCMs mostly rely on two-dimensional (2D) or quasi three-dimensional (3D) thin film patterning based on the coating of chalcogenide materials from a solid-state target. This limits fast prototyping of 3D freeform micro- and nanostructures, thus restricting
geometric design freedom and device functionality. Here, we demonstrate a solution-phase direct printing of chalcogenide PCMs into functional structures. The method is based on dip in two
photon-induced solidification (DITPS) of a specially synthesized antimony trisulfide (Sb2S3) precursor solution. Direct printing with DITPS is simple, maskless, fast and cost effective, enabling true freeform 3D printing of photonic devices with sub micron resolution. We show direct writing of Sb2S3 helices with different wire cross section profiles on gold and ITO substrates, as well as functional planar Fresnel zone plates (FZPs) and computer generated hologram metasurfaces (CGHMs) in a single printing step. This freeform DITPS approach thus enables rapid 3D prototyping of high index metasurfaces and opens a route to integrating high-index PCMs into existing photonic architectures and device platforms.
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All authors Abhrodeep Dey, Andrea Dellith, Anne Sauer, Uwe Hübner, Henrik Schneidewind, Markus A Schmidt, Astrid Bingel, Volker Deckert, Jer-Shing Huang, Wei Wang
Data Analysis and Statistics · Cross-listed from quant-ph
Entropic Reciprocity in Time-Reversed Young Interferometry
Jianming Wen
We show that time-reversed Young interferometry reorganizes, rather than reverses, optical
entropy. A fixed detector conditions the reciprocal source–detector Green function and produces a source-label probability distribution. Marginal
entropies in the standard and time-reversed geometries are generally unequal; the reciprocal invariant is instead the mutual information between source and detector coordinates. Near a destructive response, the conditioned source-label
entropy can decrease while Fisher information for small phase, tilt, or defocus perturbations increases. The result identifies time-reversed Young interferometry as a source-space information processor with no analogue in ordinary detector-plane fringe readout.
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Chemical Physics
Bhumika Jayee, Nathan M. Myers, Duo Song et al. (+3 more)
We assess the performance of the
Quantum Flow (QFlow)
algorithm employing cost-effective solvers based on the
unitary coupled-cluster ansatz with single and double excitations (QFlow-SD). The resulting energies are benchmarked against those obtained with an analogous QFlow formulation defined in the same active spaces but augmented by higher-rank excitations, including triples and quadruples (QFlow-SDTQ). Across all molecular systems considered, QFlow-SD exhibits close agreement with results from the canonical
unitary coupled cluster with singles and doubles framework, while requiring substantially fewer
qubits than the latter. For the water molecule in the cc-pVTZ basis, we further demonstrate the performance of a composite two-step downfolding strategy. In this approach, an initial coupled-cluster downfolding based on the double
unitary coupled-cluster ansatz is followed by a QFlow treatment within the resulting target space, illustrating the effectiveness of combining classical downfolding with
quantum flow optimization.
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All authors Bhumika Jayee, Nathan M. Myers, Duo Song, Eric J. Bylaska, Karol Kowalski, Nicholas P. Bauman
Fluid Dynamics
Leveraging unstructured grids for direct numerical simulations of wall turbulence
Amirreza Rouhi, Vishal Kumar, Wen Wu et al. (+2 more)
Towards computational cost saving for direct numerical simulations (DNSs) of wall
turbulence, we formulate an unstructured grid-generation framework, termed $\eta$-grid, where the wall-normal ($y$) and spanwise ($z$) grid sizes are proportional to the local Kolmogorov scale $\eta$. The framework consists of an inner layer, with a thickness $\sim 50$ viscous units, with viscous-scaled grid sizes similar to a conventional DNS grid: $0.3 \lesssim \Delta y^+ \lesssim 4, \Delta z^+ \simeq 5$ over a smooth wall, and $\ell^+/30 \lesssim \Delta y^+, \Delta z^+ \lesssim 4$ over uneven surfaces, where $\ell^+$ is the smallest surface wavelength. Above the inner layer, $\Delta y^+ \simeq \Delta z^+ \simeq 2\eta^+$. We test $\eta$-grid with finite volume and spectral element solvers, and conduct DNSs of turbulent channel flows and boundary layers over smooth wall and various streamwise-aligned riblets, up to friction Reynolds number $\delta^+_0 = 1000$. We assess the accuracy of $\eta$-grid against the conventional Cartesian grids, through comparison with the reference DNS and experimental data. Results from $\eta$-grid and the Cartesian grids differ by less than $1\%$, in terms of
turbulence statistics up to second-order, and the energy spectra. For turbulent channel flows with $10^3 \lesssim \delta^+_0 \lesssim 10^4$, the number of grid points with $\eta$-grid ($N_\eta$) scales $\propto {\delta^+_0}^{2.47}$ over a smooth wall, and $\propto {\delta^+_0}^{2.0-2.47}$ over riblets, whereas the number of grid points with a Cartesian grid and
hyperbolic-tangent $y$-grid ($N_\mathrm{Tanh}$) scales $\propto {\delta^+_0}^{3.0}$. By $\delta^+_0 = 6000$, $N_\eta/N_\mathrm{Tanh} \simeq 0.1$ over a smooth wall, and $N_\eta/N_\mathrm{Tanh} \simeq 0.04$ over typical drag-reducing riblets, with viscous-scaled spacing $15$.
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All authors Amirreza Rouhi, Vishal Kumar, Wen Wu, Melissa Kozul, Oriol Lehmkuhl
Chemical Physics
Modelling Intermediate-Current Transitions in Asymmetric-Valence Binary Electrolytes
Georgina C. Ryan, Mohit P. Dalwadi and Ian M. Griffiths
Asymmetric valences in a binary electrolyte can significantly affect the performance of systems such as reverse electrodialysis
cells, batteries, and supercapacitors. To generate a theoretical understanding of this effect, we consider a steady one-dimensional Poisson-Nernst-Planck model of an electrolytic
cell with imposed constant ionic fluxes, focusing on varying ion valences in a general asymmetric binary electrolyte. Numerical simulations reveal a smooth transition between the qualitatively distinct near-
equilibrium and strongly non-
equilibrium steady-state regimes. These regimes are distinguished by a valence-dependent transition point at an intermediate current where the classical Debye-scale boundary layer vanishes. We characterise this transition using asymptotic analysis, recovering the Gouy-Chapman and limiting-current results in the appropriate limits, and determining the correct transition results when neither is appropriate. We provide implicit solutions for the potential and ion concentrations of general asymmetric binary electrolytes and, notably, we provide explicit analytic expressions for the asymptotic composite solutions for 2z:z, z:2z, and z:z electrolytes. We show how the results can be presented in a collapsed phase diagram that can be used to predict qualitative intermediate-current steady-state behaviour in terms of ion valences and fluxes.
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Plasma Physics · Cross-listed from astro-ph.HE
Nonuniform Particle Injection into Black Hole Jets by Radiative Magnetic Reconnection
Rin Oikawa, Kenji Toma and Shigeo S. Kimura
Active
galactic nuclei often exhibit highly collimated relativistic
plasma outflows launched from the vicinity of their central black holes. One of the key theoretical challenges in understanding black hole jet formation is the origin of the
plasma that feeds the jet, which remains poorly understood, particularly in explaining the observed jet emission. In this study, we focus on electron positron pair production generated by high energy
photons from non axisymmetric
magnetic reconnection near the black hole, as suggested by recent three dimensional general relativistic magnetohydrodynamics simulations. By employing general relativistic ray tracing, we calculate the spatial distribution of the pair production rate in the jet, taking into account
photon propagation and collision angles in curved spacetime. We find that our scenario can naturally supply a sufficient amount of
plasma to explain the observed
radio emission from the M87 jet, even when
photon anisotropy is considered. Furthermore, we show that a spinning black hole plays a crucial role in shaping the spartial dsitribution of the pairs, which in turn affects jet acceleration and very high energy emission from the jet base.
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Chemical Physics
Accurate, full-dimensional computations of thousands of complex vibrational eigenstates with tree tensor network states
Henrik R. Larsson, Brieuc Le Dé and Gino E. Gamboni
Tree tensor network states (TTNSs) combined with the density
matrix renormalization group (DMRG) are emerging as powerful tools for vibrational and vibronic structure simulations in molecules with strong coupling and fluxionality. In this Perspective, we discuss how TTNS methods enable accurate, full-dimensional computations of thousands of eigenstates for molecular systems ranging from quartic-force-field
benchmarks to molecules with strong vibronic coupling and protonated water clusters as large as the 33-dimensional Eigen ion, H$_3$O$^+$$\cdot$(H$_2$O)$_3$. We emphasize the close connection and interoperability between DMRG-based TTNS methods and the multilayer multiconfiguration time-dependent Hartree method (ML-MCTDH), which share the same underlying ansatz. We also highlight practical challenges of predictive simulations, including robust error estimation, convergence of observables such as infrared intensities, and optimization of tensor network tree structures. Finally, we outline recent advances toward direct targeting of excited states and discuss opportunities for broader applications in molecular spectroscopy and
quantum dynamics.
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Physics Education · Cross-listed from astro-ph.IM
Recommendations for the Astronomy Graduate Admissions Process
Emily M. Levesque, Courtney D. Dressing, Rachel Ivie et al. (+4 more)
As the AAS Working Group on Graduate Admissions (WGGA) we are sharing brief recommendations for improving and standardizing key elements of the graduate admissions process in astronomy. Most astronomy graduate programs have large areas of overlap in their admissions processes; however, the existing small variations in requirements and mismatches in communication and transparency make admissions more challenging for students and programs alike. To improve this situation, and building on the work presented in the AAS Graduate Admissions Task Force (GATF) report we recommend a few simple and straightforward changes for application content, communication, and timelines. These include an application format that consists of 1) two 500-word recommendation letters, 2) one 1500-word application essay, 3) an applicant CV, and 4) unofficial transcripts; and an admissions timeline that includes effective and transparent communication from programs and encouraging an April 1st "down-select date" for applicants.
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All authors Emily M. Levesque, Courtney D. Dressing, Rachel Ivie, Grace Krahm, Meredith A. MacGregor, Daniel R. Piacitelli, Tom Rice