Space Physics
An Enhanced "Flux-Corrected Transport"-Based Plasmasphere Refilling Model
Jaden Fitzpatrick, Kausik Chatterjee and Naomi Maruyama
A previously developed multi-ion, two-stream Flux-Corrected Transport (FCT) hydrodynamic model for plasmasphere refilling has been extended to incorporate self-consistent electron temperature
evolution. The past assumption of a constant temperature along the modeled flux tube has been replaced by solving the electron energy equation, permitting spatially and temporally varying temperature. This improvement provides a more physically complete representation of the pressure and ambipolar electric-field gradients that influence ion transport. The extended model allows us to investigate two-stage refilling behavior established by prior observations and simulations. The model continues to reproduce the expected dominance of H+, enhanced early-time O+ contributions, and the coupling between H+ and He+ through the ambipolar electric field during the transition between stages. Sensitivity experiments with modified initial ion concentrations, including cases representing seasonal effects, highlight the distinct roles of each ion species in shaping the refilling trajectory. Comparisons across L-shells 3 and 4 further confirm the robustness of the model framework for future extension to three-dimensional geometries. Overall, by incorporating more realistic temperature variations, this enhanced model strengthens the physical understanding for interpreting complex multi-ion transport processes during plasmasphere recovery following geomagnetic storms.
Read the full abstract ▾
Chemical Physics
A generalized rate law for inhomogeneous system and turbulence-chemistry decoupling of reaction rate calculation in combustion
Xiang-Yuan Li, Xin-Yu Zhang and ChuanFeng Yue
In this work, the rate law for inhomogeneous concentration distributions has been formulated, by applying spatial integration over the products of species concentrations. Reaction rates for typical reactions have been investigated by assuming a linear concentration distribution in the grid. A few examples of one-dimensional concentration distributions, straight line, piecewise, and sine function, for a selected second order reaction have been taken to illustrate the validations of the method developed. Difference between the reaction rates by spatial integration and by mean concentrations have been discussed. It is revealed that the chemical reaction rates for combustion simulation can be calculated by appropriate sub-grid modeling of concentration distributions, without needs of the explicit consideration of turbulent combustion interactions, and the reaction rates for the species transport equation in turbulent combustion simulations can be accurately calculated if the concentration distributions of species within the grid are correctly defined.
Read the full abstract ▾
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Unveiling the amorphous ice layer during premelting using AFM integrating machine learning
Binze Tang, Chon-Hei Lo, Tiancheng Liang et al. (+6 more)
Premelting plays a key role across physics, chemistry, materials and biology sciences but remains poorly understood at the atomic level due to surface characterization limitations. We report the discovery of a novel amorphous ice layer (AIL) preceding the quasi-
liquid layer (QLL) during ice premelting, enabled by a machine learning framework integrating atomic force microscopy (AFM) with molecular dynamics simulations. This approach overcomes AFM's depth and signal limitations, allowing for three-dimensional surface structure reconstruction from AFM images. It further enables structural exploration of premelting interfaces across a wide temperature range that are experimentally inaccessible. We identify the AIL, present between 121-180K, displaying disordered two-dimensional hydrogen-bond network with solid-like dynamics. Our findings refine the ice premelting phase diagram and offering new insights into the surface growth dynamic, dissolution and interfacial chemical reactivity. Methodologically, this work establishes a novel framework for AFM-based 3D structural discovery, marking a significant leap in our ability to probe complex disordered interfaces with unprecedented precision and paving the way for future disciplinary research, including surface reconstruction, crystallization, ion solvation, and biomolecular recognition.
Read the full abstract ▾
All authors Binze Tang, Chon-Hei Lo, Tiancheng Liang, Jiani Hong, Mian Qin, Yizhi Song, Duanyun Cao, Ying Jiang, Limei Xu
Optics
Design of Microlens Arrays for Thermal Imaging with Spintronic Poisson Bolometers
Ziyi Yang, Leif Bauer and Zubin Jacob
Infrared (IR) detectors are widely used due to their ability to sense thermal radiation. Recently, a room-temperature infrared detector known as the spintronic Poisson bolometer was introduced. While offering fast digital readout, its sensitivity is currently limited by its small photosensitive area and array fill factor. Beyond this specific detector, many emerging detector architectures also require substantial in-pixel electronics or engineering tradeoffs, which can reduce the fill factor and degrade optical coupling efficiency. In this work, we present design guidelines for spherical plano-convex microlens arrays that enhance light collection in spintronic Poisson bolometer arrays in the mid-wave infrared (MWIR). Guided by the simulations, we fabricate a microlens array sample to demonstrate that the chosen geometrical parameters are experimentally attainable and compatible with the fabrication process. We then systematically explore the scaling laws between sensor active size, microlens geometries, and the resulting light collection metrics, yielding practical design rules that are broadly relevant to MWIR detector arrays with limited active area. A radiometric-
stochastic model is used to quantify the resulting sensitivity improvements for the spintronic Poisson bolometer. Our work is the first systematic simulation of a microlens design with spintronic Poisson bolometer arrays, bridging microphotonics, spintronics, and thermal imaging.
Read the full abstract ▾
Optics
Enhanced Sensitivity to Blackbody Radiation in Spintronic Poisson Bolometers
Ziyi Yang, Sakshi Gupta, Jehan Shalabi et al. (+5 more)
High-sensitivity long-wave infrared (LWIR) detection is crucial for observing weak thermal radiation. Recently, the Poisson bolometer has been proposed as a fundamentally new platform for uncooled infrared detection. In contrast to traditional analog detectors, where signal and noise are determined by continuous currents or voltages, the Poisson bolometer's signal and noise are governed by Poissonian counting statistics regardless of the light source. In this work, we demonstrate advancements in uncooled infrared detection towards cryogenic-level sensitivity through the integration of spintronic and plasmonic materials. Specifically, a spintronic Poisson bolometer is experimentally integrated with a plasmonic nanoantenna array optimized for broadband LWIR absorption to enhance the temperature increase of the sensing layer. The plasmonic absorber exhibits an absorptance exceeding 60% across the LWIR spectrum, matching the peak of room-temperature blackbody radiation. We demonstrate that these devices are capable of achieving a noise equivalent differential temperature (NEDT) of 35 mK at a 50 Hz frame rate, demonstrating room-temperature performance comparable to the most sensitive uncooled LWIR detectors reported to date. This work opens up a pathway to removing bulky and expensive cooling requirements from high-sensitivity LWIR detection and imaging applications, such as remote sensing, high-speed imaging, and industrial monitoring.
Read the full abstract ▾
All authors Ziyi Yang, Sakshi Gupta, Jehan Shalabi, Daien He, Leif Bauer, Mohamed A. Mousa, Angshuman Deka, Zubin Jacob
Fluid Dynamics
Lift reversal from vortex-surface phase coupling in a heaving foil near a free surface
Qimin Feng, Tianjun Han and Qiang Zhong
Classical descriptions of flapping propulsion near a free surface emphasize the energetic penalties of wave generation, treating the interface primarily as an energy sink. Here, we show that the same deformable boundary can also act as a phase-dependent kinematic constraint on vertical force generation. Using force measurements, particle image velocimetry and potential-flow simulations, we characterize how a free surface reorganizes
vortex shedding for a heaving hydrofoil at moderate Reynolds number (O(10^4)). For moderate to deep submergence, the cycle-averaged lift undergoes a systematic transition from repulsion to suction as the unsteady number increases. The reversal occurs within a narrow band of unsteady numbers, where the phase-shifted surface motion generates vertical advection that alters the pairing of trailing-edge
vortices and redirects the wake momentum flux. A force decomposition shows that the reversal arises from a coordinated change in quasi-steady pressure loading and wake-induced force. These results identify the phase of the free-surface response, organized by unsteady number, as a key parameter governing near-surface lift and illustrate how deformable boundaries can reconfigure unsteady loading through
vortex-surface phase coupling.
Read the full abstract ▾
Instrumentation and Detectors
Updated Simulation of GRETA Detector Response and Exploration of Temperature Sensitivity
Arin Manohar, Mario Cromaz, Christopher Campbell et al. (+2 more)
The Gamma-Ray Energy Tracking Array (GRETA) is a next-generation gamma-ray spectrometer designed to push the frontiers of
nuclear structure and astrophysics experiment. Its high sensitivity is enabled by high-precision localization of gamma-ray interactions within its active detector volume, and the subsequent tracking of gamma-ray scattering sequences. In order to perform gamma-ray tracking, we need to simulate signal generation in the detectors accurately. This requires both accurate calculations of charge movement in the
semiconductor volume, as well as a faithful reproduction of real-world experimental effects such as the electronics response. This work addresses the fidelity of the calculated signals for GRETA in two ways. An updated approach has been applied to find an optimized parameterization of the electronics response, while the impact of the detector temperature was also explored to best reproduce experimental signals and improve the position localization performance for GRETA. The results suggest that the electronics response can be simplified without impacting performance, and that the response correction parameters can effectively compensate for the changes in signal which arise due to the
crystal temperature, resulting in minimal sensitivity of the position resolution to the assumed temperature.
Read the full abstract ▾
All authors Arin Manohar, Mario Cromaz, Christopher Campbell, Heather Crawford, Marco Salathe
Geophysics
Bayesian Full-waveform Monitoring of CO2 Storage with Fluid-flow Priors via Generative Modeling
Haipeng Li, Nanzhe Wang, Louis J. Durlofsky et al. (+1 more)
Quantitative monitoring of subsurface changes is essential for ensuring the safety of geological CO2 sequestration. Full-waveform monitoring (FWM) can resolve these changes at high spatial resolution, but conventional deterministic inversion lacks uncertainty quantification and incorporates only limited prior information. Deterministic approaches can also yield unreliable results with sparse and noisy seismic data. To address these limitations, we develop a
Bayesian FWM framework that combines reservoir flow physics with generative prior modeling. Prior CO2 saturation realizations are constructed by performing multiphase flow simulations on prior geological realizations. Seismic velocity is related to saturation through rock physics modeling. A variational autoencoder (VAE) trained on the priors maps high-dimensional CO2 saturation fields onto a low-dimensional, approximately Gaussian latent space, enabling efficient
Bayesian inference while retaining the key geometrical structure of the CO2 plume.
Hamiltonian Monte Carlo (HMC) is used to infer CO2 saturation changes from time-lapse seismic data and to quantify associated uncertainties. Numerical results show that this approach improves inversion stability and accuracy under extremely sparse and noisy acquisition, whereas deterministic methods become unreliable. Statistical seismic monitoring provides posterior uncertainty estimates that identify where additional measurements would most reduce ambiguity and mitigate errors arising from biased rock physics parameters. The framework combines reservoir physics, generative priors, and
Bayesian inference to provide uncertainty quantification for time-lapse monitoring of CO2 storage and other subsurface processes.
Read the full abstract ▾
All authors Haipeng Li, Nanzhe Wang, Louis J. Durlofsky, Biondo L. Biondi
Data Analysis and Statistics · Cross-listed from cs.LG
Optimized Architectures for Kolmogorov-Arnold Networks
James Bagrow and Josh Bongard
Efforts to improve Kolmogorov–Arnold networks (KANs) with architectural enhancements have been stymied by the complexity those enhancements bring, undermining the interpretability that makes KANs attractive in the first place. Here we study overprovisioned architectures combined with sparsification, deep supervision, and depth selection, to learn compact, interpretable KANs without sacrificing accuracy. Crucially, we focus on differentiable mechanisms under a principled minimum description length objective, jointly optimizing activations, structure, and depth end-to-end. Experiments across function approximation
benchmarks, dynamical systems
forecasting, and real-world prediction tasks demonstrate that sparsification alone is insufficient, but the combination with depth selection achieves competitive or superior accuracy while discovering substantially smaller models. The result is a principled path toward models that are both more expressive and more interpretable, addressing a key tension in scientific machine learning.
Read the full abstract ▾
Atmospheric and Oceanic Physics
Considerations of Earth climate sensitivity based on peculiarities of planetary heat capacity using system identification method: Runaway greenhouse effect scenario is still possible
Alexei V Karnaukhov, Sergei F Lyuksyutov, Artem V Aliakin et al. (+2 more)
System identification method (SIM) was used to evaluate the Earth
equilibrium climate sensitivity. According to our simulations, the
equilibrium climate sensitivity was found to be between 2 deg C and 7 deg C. Analysis of the changes in heat inventory of oceans, atmosphere, land, and cryosphere was based on the experimental data of IPCC6. The equation derived for Earth's global surface temperature (GST) shows that the sum of the dimensionless feedback coefficients from water vapor, methane, and Earth albedo could be less than 1. However, due to the positive feedback from carbon dioxide (the combined greenhouse catastrophe) and the revised
equilibrium climate sensitivity estimate based on an increase in
equilibrium climate sensitivity, the probability of the runaway greenhouse effect increases significantly. It is still less than the critical number when not considering the feedback associated with carbon dioxide, water vapor, and methane buildup in Earth's atmosphere. The analysis considers the thermal dynamics of the oceans and other factors, including the exponential growth of the Earth's global temperature based on IPCC6 data.
Read the full abstract ▾
All authors Alexei V Karnaukhov, Sergei F Lyuksyutov, Artem V Aliakin, Mikhail E Prokhorov, Sergei I Blinnikov
Chemical Physics
A Software Package for Generating Robust and Accurate Potentials using the Moment Tensor Potential Framework
Josiah Roberts, Biswas Rijal, Simon Divilov et al. (+6 more)
We present the Plan for Robust and Accurate Potentials (PRAPs), a software package for training and using moment tensor potentials (MTPs) in concert with the Machine Learned Interatomic Potentials (MLIP) software package. PRAPs provides an automated workflow to train MTPs using active learning procedures, and a variety of utilities to ease and improve workflows when utilizing the MLIP software. PRAPs was originally developed in the context of
crystal structure prediction, in which one calculates convex hulls and predicts low energy metastable and thermodynamically stable structures, but the potentials PRAPs develops are not limited to such applications. PRAPs produces two potentials, one capable of rough estimates of the energies, forces and stresses of almost any chemical structure in the specified compositional space – the Robust Potential – and a second potential intended to provide more accurate descriptions of ground state and metastable structures – the Accurate Potential. We also present a Python library, mliputils, designed to assist users in working with the chemical structural files used by the MLIP package.
Read the full abstract ▾
All authors Josiah Roberts, Biswas Rijal, Simon Divilov, Jon-Paul Maria, William G. Fahrenholtz, Douglas E. Wolfe, Donald W. Brenner, Stefano Curtarolo, Eva Zurek
Data Analysis and Statistics · Cross-listed from math.DS
Data-driven modelling of autonomous and forced dynamical systems
Robert Szalai
The paper demonstrates that invariant foliations are accurate, data-efficient and practical tools for data-driven modelling of physical systems. Invariant foliations can be fitted to data that either fill the phase space or cluster about an invariant
manifold. Invariant foliations can be fitted to a single trajectory or multiple trajectories. Over and underfitting are eliminated by appropriately choosing a function representation and its hyperparameters, such as
polynomial orders. The paper extends invariant foliations to forced and parameter dependent systems. It is assumed that forcing is provided by a volume preserving map, and therefore the forcing can be periodic, quasi-periodic or even chaotic. The method utilises full trajectories, hence it is able to predict long-term dynamics accurately. We take into account if a forced system is reducible to an autonomous system about a steady state, similar to how Floquet theory guarantees reducibility for periodically forced systems. In order to find an invariant
manifold, multiple invariant foliations are calculated in the neighbourhood of the invariant
manifold. Some of the invariant foliations can be linear, while others nonlinear but only defined in a small neighbourhood of an invariant
manifold, which reduces the number of parameters to be identified. An invariant
manifold is recovered as the zero level set of one or more of the foliations. To interpret the results, the identified mathematical models are transformed to a canonical form and instantaneous frequency and damping information are calculated.
Read the full abstract ▾
Geophysics
SoilGen: A Comprehensive Tool for Generating Synthetic Soil Profiles for Geotechnical and Seismic Analysis
Mersad Fathizadeh and Hosna Kianfar
Geotechnical and seismic applications, ranging from site response analysis and HVSR simulations to dispersion curve modeling, increasingly depend on large, well-labeled
datasets for robust model development. However, the scarcity of publicly available borehole
datasets, coupled with the proprietary nature of high-quality field records, creates a significant bottleneck for data-driven research, particularly in machine learning. To address this limitation, this study introduces SoilGen, an open-source framework that procedurally generates physically consistent multilayer soil columns as synthetic soil profiles. Unlike simple randomization, SoilGen computes a complete suite of geotechnical properties, including layer thickness, shear-wave velocity, P-wave velocity, density, and Poisson ratio, while enforcing physical constraints to ensure realism. The algorithmic foundations of the framework and its implementation are outlined, and its utility is demonstrated through representative near-surface geological scenarios relevant to site characterization and near-surface geophysics. By facilitating the rapid generation of large-scale model libraries exceeding one hundred thousand realizations, SoilGen enables comprehensive parametric studies and the training of deep learning inversion networks that require extensive labeled
datasets for shear-wave velocity profiling and related site characterization tasks.
Read the full abstract ▾
Biological Physics · Cross-listed from cond-mat.soft
Field-Particle Interactions in Curved Flows for Shape-Asymmetric Active Particles
Derek C. Gomes and Tapan C. Adhyapak
We show that
curvatures in general ambient flow profiles can align shape-asymmetric active particles, revealing a previously overlooked competition with externally applied aligning fields. Focusing on the ubiquitous case of channel flows, we then investigate the fundamental consequences of this competition for the dynamics of shape-asymmetric active particles in microchannels in the presence of orienting fields. We find that this interplay gives rise to novel mechanisms for controlling particle dynamics, with potentially broad applications, and suggests exciting possibilities such as active-particle analogs of electronic systems.
Read the full abstract ▾
Optics
Rethinking massive multiplexing in whispering gallery mode biosensing
Ivan Saetchnikov, Elina Tcherniavskaia, Andreas Ostendorf et al. (+1 more)
Accurate, label-free quantification of multiple analytes in complex biological media remains a major challenge due to limited multiplexing, signal cross-correlations, and inconsistency across sensor samples and measurement runs. We introduce a multiplexed whispering-gallery-mode (WGM) biosensing framework that overcomes these barriers by jointly advancing photonic integration and data analytics. Our glass-chip platform enables massive, parallelized and flexible multiplexing of >10000 microresonators organized into up to 100 sensing channels, with universal and modular chip design and detection hardware, while maintaining loaded Q-factors of 10^6. Our novel hybrid deep-learning framework BioCCF that integrates domain adaptation with cross-channel fusion enables harmonization of responses across sensing chips and extraction of nonlinear correlations in complex mixtures. Using a highly heterogeneous
dataset comprising over 200 hours of sensing data acquired from nine chips with different channel configurations, biological replicates, and repeated regeneration cycles, we demonstrate recalibration-free identification of solution (99.3% accuracy) and quantification of immunoglobulin G components with relative prediction error of 10^-4 under 5 min. The affordability and modularity of the platform enable distributed data acquisition and aggregation into shared repositories, providing a pathway toward continuously improving model generalization, cross-validation and a scalable, community-driven paradigm for biosensing.
Read the full abstract ▾
All authors Ivan Saetchnikov, Elina Tcherniavskaia, Andreas Ostendorf, Anton Saetchnikov
Computational Physics · Cross-listed from cond-mat.soft
Exploring the energy landscape of the logarithmic potential: local minima and stationary states
Paolo Amore, Victor Figueroa and Raymundo Ramos
We have performed a detailed exploration of the energy landscape for configurations of points on the sphere, interacting via the logarithmic potential, and corresponding to local minima of the total energy, up to $N = 160$. The growth of $N_{\rm conf}$ (number of distinct configurations) is exponential, as for the Thomson problem, although weaker. Using the techniques described in our previous paper~\cite{Amore25} we have also explored the solution landscape of this problem for $N \leq 24$, and found that the number of stationary states is growing exponentially.
Read the full abstract ▾
Optics
Photoelectron chiral dichroism induced by lasers without helicity via chiral hole wave-packets
Gal Bouskila, Avner Fleischer and Ofer Neufeld
Photoelectron circular dichroism (PECD) is a method where randomly oriented
chiral molecules are photoionized due to irradiation by circularly-polarized
lasers, yielding large
chiral signals in the photoelectron momentum distribution. Recently, PECD was explored with polarization-tailored light such as bi-chromatic and non-collinear drivers, which still produces significant
chiral signals. Yet, all known PECD configurations to date exhibit non-zero time-local chirality. That is, they are driven by an intrinsically helical light source. Nonetheless, '
chiral' light can also be non-helical if its chirality manifests on longer timescales (e.g. an optical centrifuge). It remains unknown whether PECD can arise from non-helical coherent light. Here we predict that PECD indeed emerges from non-helical light by employing a train of linearly-polarized intense
laser pulses with a rotating polarization axis, which are phase-coherent and time-delayed. We find strong PECD in the model
chiral molecule CBrClFH under a wide parameter regime that can be optimized up to ~8% by tuning delays between pulses, suggesting
quantum interference. We directly show that the physical mechanism for this type of PECD differs from the standard case, relying on a
chiral hole attosecond wave-packet evolving in the molecule, induced by the first linear pulse. Our work shows that multiple mechanisms can give rise to PECD on longer timescales and provides a novel approach for ultrafast chirality spectroscopy and coherent
chiral wave-packet manipulation.
Read the full abstract ▾
Biological Physics · Cross-listed from cond-mat.soft
Morphogenesis of bacterial colonies in liquid crystalline environments
Sebastian Gonzalez La Corte, Thomas G. J. Chandler, Saverio E. Spagnolie et al. (+2 more)
Natural bacterial habitats are often complex fluids with viscoelastic and anisotropic responses to stress; for example, they can take the form of
liquid crystals (LCs), with elongated microscopic constituents that collectively align while still retaining the ability to flow. However, laboratory studies typically focus on
cells in simple
liquids or complex fluids with randomly-oriented constituents. Here, we show how interactions with LCs shape bacterial proliferation in multicellular colonies. Using experiments, we find that in a nematic LC,
cells generically form aligned single-
cell-wide "chains" as they reproduce. As these chains lengthen, they eventually buckle in a highly localized manner. By combining our measurements with a continuum mechanical theory, we demonstrate that this distinctive morphogenetic program emerges because
cells are kept in
alignment due to the LC's elasticity; as each chain lengthens, growth-induced viscous stresses along its contour eventually overcome the elasticity of the surrounding nematic, leading to buckling. Our work thus reveals and provides mechanistic insight into the previously-overlooked role of LCs in sculpting bacterial life in complex environments.
Read the full abstract ▾
All authors Sebastian Gonzalez La Corte, Thomas G. J. Chandler, Saverio E. Spagnolie, Ned S. Wingreen, Sujit S. Datta
Physics Education
Formative experience for intensive instruction physics courses: Evaluation and results in an Electromagnetism course
Marcela Vallejo, Ema Huerta, Joselen M. Pena et al. (+2 more)
The rising demand for higher education has led universities to offer courses in multiple formats, including Intensive Instruction Courses (IICs), to meet the needs of a diverse student body. While active teaching methods improve physics understanding in standard courses, little research has examined their effectiveness in IICs. This research explored the most efficient methodologies for promoting meaningful learning in intensive physics courses. To this end, an integrated pedagogical proposal was designed based on the opinions gathered from a focus group of teachers with previous experience teaching these courses, as well as on existing literature, highlighting methodologies, types of assessment, and characteristics of ICCs. To evaluate its efficiency, a quasi-experiment was conducted in which students were divided into two groups: an experimental group (EG), which followed the teaching proposal, and a control group (CG), which received non-innovated lessons. Results were measured using the BEMA electromagnetism concepts inventory before and after the intervention. Statistical analysis revealed that Hake's Gain was greater in the EG, both in general terms and in each thematic unit of the course. The intervention in the EG showed that the use of active methodologies was more efficient than those used in the CG in the context of IICs. The results obtained suggest the need to continue investigating other factors involved in the teaching-learning process of IICs.
Read the full abstract ▾
All authors Marcela Vallejo, Ema Huerta, Joselen M. Pena, Jose Leiva, Ethan Rodriguez
Optics
Poisson wavefront imaging in photon-starved scenarios
Seungman Choi, Peter Menart, Andrew Schramka et al. (+4 more)
Low-
photon phase imaging is essential in applications where the signal is limited by short exposure times, faint targets, or the need to protect delicate samples. We address this challenge with Poisson Wavefront Imaging (PWI), an optimization-based method that incorporates Poisson
photon statistics and a smoothness prior to improve wavefront reconstruction. By using multiple spatial light modulator's phase patterns, PWI enhances Fisher information, boosting theoretical accuracy and regularizing the
retrieval process effectively. In simulations, PWI approaches the theoretical phase error limit, and in experiments it reduces phase error by up to 1.6x compared to the Gerchberg-Saxton
algorithm, achieving 1.8x higher resolution wavefront imaging in low
photon regime. This method advances
photon-limited imaging with applications in astronomy,
semiconductor metrology, and biological systems.
Read the full abstract ▾
All authors Seungman Choi, Peter Menart, Andrew Schramka, Leif Bauer, Vaneet Aggarwal, In-Yong Park, Zubin Jacob
Physics and Society · Cross-listed from stat.ME
The Morphemic Origin of Zipf's Law: A Factorized Combinatorial Framework
Vladimir Berman
We present a simple structure based model of how words are formed from morphemes. The model explains two major empirical facts: the typical distribution of word lengths and the appearance of Zipf like rank frequency curves. In contrast to classical explanations based on random text or communication efficiency, our approach uses only the combinatorial organization of prefixes, roots, suffixes and inflections. In this Morphemic Combinatorial Word Model, a word is created by activating several positional slots. Each slot turns on with a certain probability and selects one morpheme from its inventory. Morphemes are treated as stable building blocks that regularly appear in word formation and have characteristic positions. This mechanism produces realistic word length patterns with a concentrated middle zone and a thin long tail, closely matching real
languages. Simulations with synthetic morpheme inventories also generate rank frequency curves with Zipf like exponents around 1.1-1.4, similar to English, Russian and Romance
languages. The key result is that Zipf like behavior can emerge without meaning, communication pressure or optimization principles. The internal structure of morphology alone, combined with probabilistic activation of slots, is sufficient to create the robust statistical patterns observed across
languages.
Read the full abstract ▾