Computational Physics
Input-schema identifiability limits in physics-informed surrogates for mechanics-governed flow
Daniel Cieslak and Andrzej Czyzewski
Physics-informed and data-driven surrogates are increasingly used to approximate mechanics-governed flow fields, but the target quantities assigned to such models are not always identifiable from the input variables available at prediction time. We introduce an input-schema identifiability certificate for computational surrogates. Starting from a reduced physical model, the certificate decomposes a target field into components that are measurable from geometry, components that require boundary-condition information, and components identifiable only up to a
symmetry quotient. This yields a pre-training audit: it predicts which oracle-channel interventions should reduce error, which should fail, and which ambiguity cannot be removed by changing the architecture, loss, optimizer, or sample size. We instantiate the framework for incompressible tubular flow using a Cosserat-rod reduction, where lumen velocity separates into a mesh-measurable tangent direction, a boundary-condition-dependent magnitude, and a signed-orientation ambiguity. Controlled experiments on patient-specific aortic CFD geometries, analytic Womersley flows, and an advection-
diffusion transfer problem confirm the predicted pattern: supplying signed direction collapses angular error to the oracle regime, whereas supplying magnitude without orientation leaves the predicted sign ambiguity and yields 16-33 percent per-node sign flips. The results provide a mechanics-based diagnostic for deciding whether a surrogate modelling task is physically identifiable before training, and expose failure modes that aggregate error metrics can hide.
Read the full abstract ▾
Physics and Society
The Sustainability Paradox of Biodegradable Packaging: A Life Cycle Perspective on Chitosan-Based Food Packaging
Maria Cosic, Milena Cosic, Amy Kim et al. (+9 more)
Chitosan-based nanomaterials are being increasingly explored as sustainable alternatives to petroleum-derived food packaging, yet their environmental performance across the full life cycle remains insufficiently understood. This review critically evaluates these systems from a life cycle perspective and examines how material origin, processing pathways, functional performance, and end-of-life behavior collectively influence sustainability outcomes. Beginning with chitin extraction from crustacean waste, key processing steps, including demineralization, deproteinization, and nanoparticle synthesis, are assessed in terms of chemical intensity, energy demand, and associated emissions. Manufacturing routes, including solvent-based and green synthesis approaches, are compared with those of conventional plastics to identify relative environmental burdens. The use phase is analyzed with respect to antimicrobial functionality, shelf life extension, and potential reductions in food waste. End-of-life pathways, including biodegradation and composting, are evaluated alongside uncertainties related to degradation behavior and nanoparticle fate. By synthesizing these stagewise interactions, this review highlights critical trade-offs that are often overlooked in sustainability narratives and examines whether chitosan-based nanomaterials provide net environmental benefits in food packaging applications.
Read the full abstract ▾
All authors Maria Cosic, Milena Cosic, Amy Kim, Siddhartha Pahari, Grace Wu, Rayna Zhou, Trisha Daftari, Shaily Gupta, Reyna Michelle Suneel, Srinivasan Latha, Rashi Sharma, Utkarsh Chadha
Physics and Society
A Benchmark Generator of Realistic Logistic Transport Networks with Nonlinear Edge Costs and Transshipment Capacities
Varvara Kovaleva and Alexander Ponomarenko
Routing many commodities through a logistic network at minimum joint cost of vehicle movements and transshipment at intermediate storages becomes substantially harder – and substantially more realistic – once two features are present together: edge costs that are nonlinear in the transported volume, because freight travels in fixed-capacity vehicles, and hard upper bounds on the volume reloaded at each storage. No public
benchmark captures this combination, and the locations of commercial distribution centres are proprietary. We close this gap with a parameterised generator of synthetic logistic networks, calibrated on real road graphs of nine European countries, five U.S.\ states and the European part of Russia extracted from OpenStreetMap. The generator reproduces the structural signatures of upper-level distribution networks – near-planar
topology, low vertex degree, short edges and large diameters – by combining Zipf-distributed city sizes, a spatial-network budget model trading edge-building cost against routing convenience, centrality-driven storage capacities, and a doubly constrained
gravity demand model. We release 35 fully synthetic instances ($10$–$150$ storages) and 15 real-geometry instances in an open, self-describing, fully reproducible file format. An empirical characterisation computed directly from the released files shows that synthetic and real-geometry instances are structurally consistent (two-sample Kolmogorov–Smirnov distances of $0.10$ on degree and edge-length distributions, with small and quantified mean deviations), and a baseline-solver study demonstrates that the corpus discriminates instance difficulty across orders of magnitude. The
benchmark is a faithful, reusable testbed for the development and comparison of exact and heuristic
algorithms.
Read the full abstract ▾
Physics and Society
Mobility-Informed Coupling of ABM, PDE, and ODE Models for Pandemic Simulation in Germany
Kristina Kehrer and Tim O. F. Conrad
Simulating epidemic spread across an entire country requires balancing fine-grained realism with computational feasibility. We address this trade-off with a multiscale, hybrid modeling framework for simulating the spread of COVID-19 across Germany. The spatial domain is split into regions, each represented either by a high-resolution
agent-based model (ABM) incorporating mobility data from mobile phones or by a faster, less detailed model based on partial (PDEs) or ordinary differential equations (ODEs). Data-driven jump processes model mobility between regions, enabling individuals to be transferred between model domains. Building on earlier studies on pairwise coupling strategies, we develop a unified framework that combines all three model classes within a single simulation environment. To demonstrate the framework's utility, we systematically compare ABM, PDE, and ODE representations of Berlin embedded in a nationwide simulation of Germany, investigate regional travel restrictions, and evaluate the Zero-COVID and No-COVID strategies. The results indicate that model resolution can be reduced in sufficiently homogeneous regions without substantially altering epidemic dynamics. Further, they reveal that mobility restrictions can lead to non-intuitive outcomes, including cases in which regional border closures increase infection numbers both locally and nationally. These effects are observed even between non-adjacent regions, illustrating how emergent, system-wide dynamics arise from local mobility restrictions. We quantify computational performance in terms of runtime savings and validate the framework against real-world infection data. The results show that the hybrid framework substantially reduces computational cost without sacrificing predictive accuracy, offering a practical tool for evaluating regional mobility restrictions and public health interventions at national scale.
Read the full abstract ▾
General Physics
Geometric Phase-Space Structure in Cosmological Solutions of Einstein's Field Equations
Hassan Ugail
Einstein field equations allow cosmological dynamics to depart from the Friedmann-Lemaitre-Robertson-Walker (FLRW) idealisation in several physically different ways. Matter may become spatially inhomogeneous, the local expansion scalar may vary across a hypersurface, the expansion may acquire anisotropic components through shear, and the free gravitational field may be encoded in nonzero Weyl
curvature. The key question is not only how far a model is from FLRW, but which
geometric mechanism is responsible. A single departure from FLRW number cannot distinguish these mechanisms. This paper introduces a compact
geometric diagnostic framework that keeps them separate while using standard quantities in general relativity. The framework is observer-explicit and domain-explicit, intended as a practical tool for comparing analytic and numerical solution families rather than as a new invariant classification of spacetime. Buchert's kinematical backreaction is retained as a derived explanatory quantity rather than a separate axis, since it is already fixed by the expansion-variance and shear contributions. A single
curvature normalisation is used for all Weyl diagnostics. The method is tested on six
benchmarks, namely FLRW, Bianchi-I, Kasner, Lemaitre-Tolman-Bondi
dust, scalar-perturbed FLRW, and tensor-perturbed FLRW. These
benchmarks occupy distinct regions of the diagnostic space, and the
magnetic Weyl contribution appears only in the tensor case. The classification remains stable under changes of perturbation amplitude, spatial resolution, averaging domain, constraint reliability, and a leading-order observer tilt. The
curvature expressions for the exact
benchmarks are verified symbolically against metric-derived Weyl invariants, and the supporting computer code, numerical results, tables, and figures are publicly available.
Read the full abstract ▾
Applied Physics · Cross-listed from cond-mat.soft
Inverse Laplace Transform for Dynamic Light Scattering: Impact of Regularization
Pierre Pajuelo, Stéphane G. Roux, Adrien Meynard et al. (+3 more)
Dynamic Light Scattering (DLS) analyzes particle dynamics from the autocorrelation functions of scattered light intensity, yet extracting accurate relaxation time distributions from noisy data is challenging. We develop an inverse problem approach to recover this distribution by inverting the Laplace transform with physics-based regularization, called the CONTIN method. We improve it to use it on noisy data, across a wide range of time scales, with a selection of the regularization strength through a data-driven L-curve criterion. Our approach enhances robustness under high noise and reveals multi-scale dynamics in complex systems. Validation is performed on simulated data, compared to the Cramér-Rao bound and to parametric methods, and on experimental data from Carbopol microgels. It demonstrates superior accuracy over parametric methods, especially for broad time distributions. The
algorithm's logarithmic discretization and variance-reduced correlation estimation enhance performance, offering a powerful tool for non-parametric DLS analysis and deeper insights into soft matter dynamics.
Read the full abstract ▾
All authors Pierre Pajuelo, Stéphane G. Roux, Adrien Meynard, François Liénard, Éric Freyssingeas, Pierre Borgnat
Physics and Society
Sovereign Stress Avalanches and Network Amplification in Latin America
Diego Vallarino
This paper studies sovereign stress avalanches and network amplification in Latin American credit
markets using monthly J.P. Morgan EMBI Global Diversified spreads for eleven sovereigns over 2007-2026. Country stress events are defined as positive log-spread innovations exceeding country-specific
volatility thresholds, and regional avalanches count the number of stressed countries in each month. The empirical design combines finite-sample power-law diagnostics, threshold robustness checks, a country-level reshuffling placebo, and rolling correlation, partial-correlation, and minimum-spanning-tree networks. Avalanche sizes are heavy-tailed, with an estimated exponent of 1.77, while spread changes and inter-event times lie in a heavy-tail boundary regime. The placebo shows synchronization far above independent stress timing, with p-values below 0.001. Large avalanches coincide with denser and more spectrally amplifying raw-correlation networks, but not after partial-correlation filtering, indicating common-factor co-movement rather than conditional regional propagation. Network metrics describe contemporaneous stress regimes rather than early-warning signals. The results provide a finite-size criticality framework for monitoring sovereign fragility in emerging
markets.
Read the full abstract ▾
General Physics
Generalized Fock–Lorentz Transformations from Projective Conformal Coordinates: Covariant Structure, Sector Classification, and Oscillator Limits
Abdelmalek Boumali, N. Jafari and M. Botshekananfard
We develop a covariant projective formulation of generalized Fock–Lorentz (GFL) transformations based on the auxiliary Minkowski coordinates $X^{\mu}=x^{\mu}/[1+a_{\nu}x^{\nu}/R]$, where $R$ is a deformation length and $a^{\mu}$ is a constant deformation vector. Ordinary Lorentz transformations acting linearly on $X^{\mu}$ induce nonlinear transformations of the physical coordinates $x^{\mu}$ with a unique denominator $\mathcal{D}_{a}(x;\Lambda)$ fixed by the conformal factor. The construction gives a unified invariant interval, clarifies the singular hypersurface of the projective chart, and separates three inequivalent sectors according to the causal character of $a^{\mu}$: time-like, space-like, and null. We emphasize two points that are often obscured by analogy with the standard FL case: the coordinate velocity of light is generally defined by an implicit linear relation, and the familiar explicit FL expression is valid only in the purely time-like sector. The time-like apparent mass $m_{\rm app}(t)=m_{0}/(1+ct/R)$ and the associated one-dimensional Klein–Gordon and Dirac oscillator spectra are treated here only as limiting consistency checks of the generalized spacetime construction and are related explicitly to the companion momentum-space-dual formulation. The genuinely new dynamical result is obtained in the space-like sector, where the weak-gradient apparent mass generates a parity-breaking cubic anharmonicity; the first-order cubic shift vanishes by parity, while the combined second-order cubic and first-order quartic corrections yield a definite $R^{-2}$ shift of the oscillator operator. These results provide a transparent basis for future applications of projective relativistic kinematics without relying on a dark-universe interpretation.
Read the full abstract ▾
Chemical Physics
My Chemical Harness: Evolutionary Molecular Design over Synthetic Pathways with Large Language Model Agents
César Ojeda, Darius A. Faroughy, Maryam Karimi et al. (+3 more)
Designing molecules with target properties is most useful when candidate structures are accompanied by feasible synthetic routes. We introduce My Chemical Harness, a route-native evolutionary framework for goal-directed molecular design in which the search population consists of executable synthetic pathways rather than isolated molecular graphs. Each route is built from purchasable building blocks and reaction templates, executed by deterministic chemistry tools, and scored through task-specific molecular oracles. Large
language models (LLMs) are used only as strategy controllers that select high-level preferences over route length, move type, reaction families, motifs, and exploration pressure, while local code performs route construction, validation, deduplication, scoring, selection, and
memory updates. This separation lets the LLM guide exploration without allowing it to introduce hallucinated products or unsupported reaction steps. On a soluble epoxide hydrolase proxy task, our LLM
agent improves over single pass LLM and deterministic controllers, reaching state-of-the-art performance across the sEH score, synthetic accessibility score, and AiZynthFinder success rate metrics. These results suggest that constrained LLM
agents can play a significant role in molecular discovery without requiring training, fine-tuning, or dedicated generative models.
Read the full abstract ▾
All authors César Ojeda, Darius A. Faroughy, Maryam Karimi, Payam Zarrintaj, Mir Mehdi Seyedebrahimi, Martín Carballo-Pacheco
Popular Physics
The Era of Extremely Large Optical Telescopes II: The GMT and TMT
Priya Hasan
The advent of Extremely Large
Telescopes (ELTs) ground-based optical and infrared observatories with primary mirrors exceeding 20m marks a transformative era in observational astronomy. This article explores the dawn of this new age, focusing on two of the next generation of optical/ infrared facilities: the
Giant Magellan
Telescope (GMT) and the Thirty Meter
Telescope (TMT)\footnote{An accompanying paper by the author is on the European Extremely Large
Telescope (ELT).} We describe the key technological innovations enabling the construction of these ambitious observatories, notably, segmented mirror design, advanced adaptive optics (AO), and
laser guide
star systems. Collectively, these breakthroughs yield more than an order of magnitude increase in both light-gathering area and spatial resolution, delivering image sharpness that, for widefield observations, surpassing space-based
telescopes. We shall also discuss the comparison of space and ground-based
telescopes and highlight their individual strengths and why space
telescopes are not the solution to the satellite interference to ground-based
telescopes. Ground based
telescopes will continue to always be leaders in astronomical observations.
Read the full abstract ▾
Data Analysis and Statistics
Confidence, Statistical Evidence and Relative Belief with Applications to a Problem in Particle Physics
Michael Evans and Siqi Zheng
Probability theory provides a clear definition of what is meant by evidence in favor, against or none either way, of an event occurring for an unobserved response, via the principle of evidence. This is immediately applicable when carrying out a proper
Bayesian analysis. Even without a prior, this imposes restrictions on reported inferences as these need to reflect the likelihood ordering. Relative belief inferences satisfy this requirement and, when the errors in these inferences are controlled, they also satisfy repeated sampling, or frequentist, requirements such as achieving given confidence levels. Relative belief inferences are considered here for the construction of intervals for uncertainty quantification in the context of a Poisson model for a signal with background noise. These intervals are contrasted with the well-known Feldman-Cousins intervals for this problem.
Read the full abstract ▾
Biological Physics · Cross-listed from eess.IV
Jonathan Schwartz, Utz Heinrich Ermel, C. Braxton Owens et al. (+6 more)
Cryo-electron
tomography (cryoET) has emerged as a powerful tool in structural and cellular biology by enabling direct visualization of macromolecular structures within intact
cells, thereby linking molecular architecture to cellular organization in a native context. Realizing the full potential of cryoET, however, increasingly depends on advances in computational analysis, particularly machine learning (ML), to interpret its complex and information-rich data. Despite rapid progress, ML development for cryoET remains bottlenecked by the lack of standardized, well-annotated
benchmarks. Existing evaluations are typically small, task-specific, and are assembled in isolation, limiting robust comparisons across methods. Here, we present POPSICLE, a
benchmark suite for cryoET
segmentation and macromolecular localization built from the CryoET Data Portal - an open, ML-ready repository of tomographic data, metadata, and annotations. POPSICLE spans eukaryotic and prokaryotic systems, both purified and fully in situ samples, and dense voxel-wise
segmentation as well as sparse localization tasks. Built on a living data resource, it can expand as new
datasets and annotations become available. Baseline experiments reveal substantial variation in model rankings across tasks, underscoring the need for
benchmarks tailored to the unique characteristics of cryoET rather than evaluation practices adapted from adjacent biomedical imaging domains. POPSICLE thus provides an open and extensible foundation for reproducible ML evaluation in cryoET.
Read the full abstract ▾
All authors Jonathan Schwartz, Utz Heinrich Ermel, C. Braxton Owens, Zhuowen Zhao, Ariana Peck, Gus L.W. Hart, Grant J. Jensen, Bridget Carragher, Dari Kimanius
Optics
Multi-channel Optical Vision Model
Ali Momeni, Guillaume Noetinger, Tim Tuuva et al. (+1 more)
Spatial multiplexing is one of the natural strengths of optics, yet in optical neural networks, it is often used mainly as parallel throughput. Here, we show that spatial multiplexing in an optical neural network can be used not only to process multiple inputs in parallel, but also to define a trainable representational coordinate of the model. In three implemented scenarios, parallel-input processing, class-code readout and channel-mixed feature interaction, spatial channels act as independent learners, structured code dimensions, and interacting feature groups. The programmable free-space optical processor is trained through an online physical-forward/surrogate-backward scheme, where measured optical outputs define the forward pass while a differentiable surrogate estimates gradients and is continually fine-tuned during training from newly acquired optical data. We demonstrate these channel roles in image classification and regression tasks using multi-layer architectures with more than one million trainable optical phase parameters. We further implement a hybrid optical-electronic
vision-
language model, in which the optical neural network provides visual
tokens to a digital
transformer decoder for controlled image-captioning tasks. These results establish spatially multiplexed optical channels as a programmable feature and readout space for hybrid optical
vision models.
Read the full abstract ▾
All authors Ali Momeni, Guillaume Noetinger, Tim Tuuva, Romain Fleury
Applied Physics
Limits of Trap-assisted Photomultiplication Gain
Ardalan Armin
Photodiodes based on trap-assisted current injection can exhibit internal photomultiplication with apparent
quantum efficiencies far exceeding unity, raising the question of whether such gain fundamentally enhances detector sensitivity. We employ a minimal analytical framework based on a single gain-active trapped state coupling photogenerated carriers to contact injection. The gain is intrinsically self-limiting: the injection process that amplifies the current simultaneously accelerates relaxation of the gain-enabling state, producing an inherently nonlinear, operating-point-dependent response. The form of this nonlinearity is not universal – once the trap level is generalized to an energetic distribution and recombination is allowed to be bimolecular, the same mechanism yields superlinear, linear, or strongly sublinear responses. A single chord gain is therefore not a meaningful device descriptor, and chord-gain comparisons across the literature conflate devices in different regimes. Treating trap occupancy and injection as coupled
stochastic processes, we show that internal gain introduces a strictly non-negative fluctuation penalty from the dissipative dynamics that sustain the gain state. A local, small-signal detectivity exhibits a finite optimum yet cannot exceed the intrinsic thermodynamic limit of the underlying unity-gain photodiode. Gain is thus equivalent to driven
stochastic amplification: it can suppress downstream readout noise, but cannot reduce the fundamental noise floor set by the primary photodetection process.
Read the full abstract ▾
Instrumentation and Detectors · Cross-listed from hep-ex
Performance of the Eos detector with water
Eos Collaboration: S. Arora, M. Askins, A. J. Bacon et al. (+62 more)
In this manuscript we present the first results from Eos, a four tonne optical detector located at the University of California, Berkeley. The primary goal of Eos is to demonstrate the performance capabilities of scintillation-based, 'hybrid' detector technology for future
neutrino detectors. The data presented were collected while both the inner target vessel and the outer buffer vessel were filled with water. The water target acts as a well-understood medium that produces only Cherenkov light, which can be used to calibrate and develop the detector model and reconstruction
algorithms prior to the deployment of scintillating material. Using deployed optical and radioactive calibration sources, a series of detailed detector calibrations are performed. These enable a suite of tests for various reconstruction
algorithms. Simulations that use calibrated models are compared with the data across a variety of different types of calibration sources, source positions, and rotations.
Read the full abstract ▾
All authors Eos Collaboration: S. Arora, M. Askins, A. J. Bacon, Z. Bagdasarian, A. Baldoni, L. Bartoszek, M. Bergevin, Y. Bezawada, E. Blucher, J. Boissevain, R. Bonventre, E. J. Callaghan, D. F. Cowen, K. DeHolton, M. Diwan, M. Dubnowski, P. Englezos, S. Gadamsetty, C. Grant, B. Harris, M. R. Hebert, S. Jeon, T. Kaptanoglu, A. Katt, J. R. Klein, T. Kroupova, L. Lebanowski, A. Mastbaum, C. Mauger, G. Mayers, M. Miller, J. Nachtman, S. Naugle, J. Newby, M. Newcomer, A. Nikolica, G. D. Orebi Gann, A. Phipps, L. Pickard, R. C. Pitelka, L. Ren, A. Rincon, R. Rosero, N. Rowe, H. J. Ryoo, J. Ryshkewitch, J. Saba, S. Schoppmann, J. Shen, M. Smiley, H. Song, H. Steiger, B. Tam, E. Tiras, W. H. To, M. R. Vagins, R. Van Berg, J. Wallig, G. Wendel, M. Wetstein, M. Wurm, G. Yang, M. Yeh, E. D. Zimmerman, A. Zummo
Instrumentation and Detectors
Electric Field Optimization of High-Voltage Vacuum Feedthroughs
Lin Si, Evan Angelico and Giorgio Gratta
We report on the optimization of high voltage
vacuum feedthroughs meant to minimize the electric field on the
vacuum side of the device. We find that commercial feedthroughs generally have center conductor diameters which are too small, resulting in unnecessarily large fields. We study the problem with analytical calculations and finite element analysis, and present a simple, optimized retrofit for the commercial feedthroughs studied here, without compromising their outgassing properties. This work is important for applications whereby the “
vacuum side” of the feedthrough is, in fact, filled with a dielectric which may not have the voltage
rigidity of
vacuum.
Read the full abstract ▾
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Finite-temperature Fe K-edge X-ray absorption simulations reveal local structural dynamics of an iron(II) photosensitizer in solution and the crystalline phase
Patrick Müller, Lorena Fritsch, Matthias Bauer et al. (+1 more)
Interpreting metal K-edge spectra of flexible photosensitizers requires a structural model that separates electronic signatures from thermal motion, solvent disorder, and
crystal-packing effects. We combine Fe K-edge X-ray absorption measurements with second-generation Car–Parrinello ab initio molecular dynamics and all-electron Gaussian and augmented-plane-wave simulations for an iron(II) N-heterocyclic carbene photosensitizer in acetonitrile solution and in the crystalline phase. Ensemble-averaged spectra reproduce the main near-edge features in both environments and preserve the experimentally observed similarity of the first Fe coordination shell upon dissolution. Comparison with radial distributions extracted from extended fine-structure measurements validates the Fe–N and Fe–C coordination shells sampled by the trajectories, while element-resolved pair distributions explain why higher-shell experimental contrast is rapidly lost. The same dynamical ensembles reveal a broad out-of-plane distribution of the terpyridine nitrogen atom and a nearly octahedral distribution of the Fe-centered coordination planes. The results show that finite-temperature X-ray absorption simulations can provide a compact structural-dynamics picture of molecular transition metal photosensitizers by linking local spectra, solvent-phase ligand motion, and medium-range structural disorder within one trajectory-based description.
Read the full abstract ▾
All authors Patrick Müller, Lorena Fritsch, Matthias Bauer, Thomas D. Kühne
Space Physics
LEAP: A Rapid Neural Surrogate of Multi-Fluid MHD at Europa
Sachin Alexander Reddy, Abigail R. Azari, Corey J. Cochrane et al. (+6 more)
Characterizing Europa's subsurface ocean is a key objective of the Europa Clipper and JUICE missions in the search for life beyond Earth. Although the ocean's induced
magnetic field provides key constraints on habitability, interpretation is complicated by perturbations arising from Jupiter's
plasma interaction with Europa. Physics-based models (e.g. magnetohydrodynamic, MHD) required to characterize these effects are physically comprehensive, but have a prohibitive computational cost. To address this, we introduce Learning Europa's Atmosphere and
Plasma (LEAP), a
transformer-based surrogate trained on outputs from a state-of-the-art multi-fluid MHD code to predict
magnetic field perturbations along spacecraft trajectories. LEAP evaluates in milliseconds on a laptop, whereas MHD takes 12 hrs on a high-performance computer (~40,000x speed-up). The model has test set errors of -/+ 2.6 nT, and for the Galileo E4 and E14 flybys of Europa it matches the parent MHD model in accuracy. Its enhanced speed enables large-scale parameter surveys and probabilistic estimations of
plasma conditions, establishing a new framework for accelerated
plasma interaction modeling. LEAP can also inform future MHD simulations while learning from them. Beyond Europa, this framework could be expanded to planning future missions or to other high-priority bodies, including Uranus and Neptune.
Read the full abstract ▾
All authors Sachin Alexander Reddy, Abigail R. Azari, Corey J. Cochrane, Xianzhe Jia, Tom A. Nordheim, Lukas Mandrake, Steven D. Vance, Camilla Harris, Ioana Ciucǎ
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Graphlet Histogram Representation Database of Inorganic Crystals
Aaditya Panigrahi, Yanjun Liu, Omri Lesser et al. (+2 more)
Machine learning models for materials property prediction increasingly rely on representations learned end-to-end from large density-functional-theory databases, limiting their applicability when only scarce experimental data are available. Domain-knowledge-driven representations precomputed from
crystal structures alone offer a data-efficient, interpretable alternative, but existing approaches capture at most composition or bonding connectivity and discard local structural geometry. Here, we present Graphlet-MP, a database of graphlet histogram representations for 149,082 inorganic
crystals from the Materials Project (MP). Seventy-nine distributions describe each material over three hierarchical graphlet orders: atomic sites, bonded pairs, and bond-angle triplets, extracted via screened Voronoi tessellation from the crystallographic information file. We provide a complete technical specification of the representation, an Earth Mover's Distance metric for comparing materials in this space, and the full precomputed database. An accompanying open-source codebase enables users to generate graphlet histograms for arbitrary
crystal structures, including experimentally determined ones, and to extend the database to new materials or target properties.
Read the full abstract ▾
All authors Aaditya Panigrahi, Yanjun Liu, Omri Lesser, Krishnanand Mallayya, Eun-Ah Kim
Fluid Dynamics
Effect of a magnetostatic field on laminar premixed hydrogen-air flames
Tristan Lapaire, Sofiane Al Kassar, Antonio Attili et al. (+1 more)
Magnetic fields have shown potential to affect flame characteristics; however, the mechanisms of interaction are not fully understood. This paper investigates the effect of
magnetic fields on premixed hydrogen-air flames that are prone to intrinsic instabilities, with a focus on the role of
magnetic forces on the flame behaviour. The study is conducted using direct numerical simulations. Two flame conditions, both with an equivalence ratio of 0.5, are studied, one with the reactants at atmospheric conditions and the other at high pressure and high temperature. Different configurations of the
magnetic field are investigated, each characterised by a different gradient of the square of the magnitude of the
magnetic field, oriented in the direction opposite to the velocity of the incoming reactants. Results show that the investigated configurations of the
magnetic field can reduce the flame consumption speed, an effect that is substantial in the lower pressure case, while it becomes negligible at high pressure. The effect of the
magnetic forces increases with increasing gradient of the
magnetic field and is mainly due to the reduction of the flame area. Results also show that the effects of
magnetic fields on the reactivity of the flame and on the small
cell structures developed along the flame front are negligible. Analysis of the force contributions demonstrates that the change in the flame area is caused by the rotational component of the
magnetic forces, which alter the vorticity of the flow such that the finger-like structures formed by hydrodynamic instabilities tend to close. These forces are significant at low pressure, while they become negligible compared to the pressure gradient at high pressure. Ultimately, the results of this work indicate that
magnetic forces have the potential to change the flame behaviour, a mechanism that could be used for active control of flames.
Read the full abstract ▾
All authors Tristan Lapaire, Sofiane Al Kassar, Antonio Attili, Andrea Giusti
Atomic Physics
Profiling a Rydberg-Atom Electric Field Sensor for Off-Resonant Detection of Sub-100 MHz RF Signals
Michael A. Viray, Abby Halasi-Kun, Baran N. Kayim et al. (+3 more)
We present a Rydberg-Atom electric field sensor optimized to detect signals at sub-100 MHz carrier frequencies. The sensing setup employs a sapphire vapor
cell that allows for detection of signals below 100~MHz – typical vapor
cells made of glass or quartz demonstrate strong screening of
radio frequency (RF) signals in this frequency regime. Applied signals are detected by observing AC Stark shifts in the atomic vapor energy levels. As a test case for the commercial utility of this receiver, we perform our tests at several carrier frequencies in the Industrial, Scientific, and Medical (ISM) band. At each carrier frequency, we report sensitivity, minimum detectable field, and detectable electric-field dynamic range. We also present a routine for optimizing off-resonant signal detection by tuning experimental parameters such as Rydberg coupler
laser detuning and RF local oscillator strength. This Python-based optimization routine, which can be used at any off-resonant carrier frequency, is shared on Github for others to use in their own investigations.
Read the full abstract ▾
All authors Michael A. Viray, Abby Halasi-Kun, Baran N. Kayim, Brian C. Sawyer, Robert Wyllie, David S. La Mantia
Computational Physics · Cross-listed from quant-ph
Towards the implementation of a quantum classifier
Lorenzo Confalonieri
In this work, we investigate the use of a
quantum circuit as a binary classification model in the context of
quantum machine learning. We call this model, binary
quantum classifier. First, we describe fundamental concepts of
quantum computing and introduce the computational tool used: Qibo, an open-source framework for efficient
quantum simulations and
quantum hardware control. Then, we describe how to design a binary
quantum classifier for the classification of images and small arrays of variables by showing how to input data in the
circuit, defining a
quantum circuit model Ansatz with trainable parameters and a loss function, and implementing multiple minimizers. We test our
quantum classifier with two data sets. The first one is the MNIST data set which is composed of handwritten digits (reduced to only handwritten zeros and handwritten ones for binary classification). We study the behavior of different minimizers by increasing the number of layers of the Ansatz. The second data set represents two different high energy collisions that can occur at
colliders such as LHC (CERN). Due to in-time
proton-
proton interactions known as pile-up, we distinguish two different data sets: "without pile-up" and "with pile-up". These collisions can be represented by images of size 32x32 or by six high-level variables that we call features. By increasing the size of the training data set and the number of layers of the Ansatz, we search for the best minimizer. Splitting the data set in training set and test set, we compute: ROC curve, AUC score, confusion
matrices and test set accuracy. For "with pile-up" images, we compare the results obtained with the
quantum classifier with a small convolutional neural network. We conclude that is possible to build a binary
quantum classifier with a
quantum circuit and we highlight its performances and limitations in comparison with classical technologies.
Read the full abstract ▾
Accelerator Physics · Cross-listed from cond-mat.mtrl-sci
Synthesis and Characterization of Atomically-Sharp Superconductor-Dielectric Interface
Nathan Sitaraman, Zhaslan Baraissov, Alexis Grassl et al. (+4 more)
Modification of
superconductor-dielectric interfaces is known to strongly impact coherence times of superconducting
quantum devices. This relationship is thought to arise from differences in the concentration of "two-level system" defects in the disordered dielectrics and
superconductor-dielectric interfaces; these defects couple to electromagnetic modes in the device and cause dissipation. Zirconium
oxide barrier layers on niobium have emerged as a promising pathway to low-loss interfaces in recent years, evidently due to the crystalline nature of these layers in comparison to the amorphous niobium native
oxide. We explain the unique ability of zirconium
oxide to form a crystalline layer, to maintain a sharp interface with metallic niobium, and to prevent niobium
oxide re-growth in terms of the chemical properties of ZrO$_2$ and the Nb-Zr-O ternary system. We demonstrate a new method to grow air-stable zirconium
oxide layers on niobium with a higher level of crystallinity and a sharper
oxide-metal interface than previously shown, and provide the first comprehensive microscopic analysis of ZrO$_2$ capping layer properties. These developments pave the way toward vital performance advances in superconducting
quantum devices.
Read the full abstract ▾
All authors Nathan Sitaraman, Zhaslan Baraissov, Alexis Grassl, Hongbin Yang, Daniel Tong, David Muller, Matthias Liepe
Popular Physics · Cross-listed from astro-ph.IM
Exploring the Orbital Stability of Large, Lightweight Mirrors around Exoplanets
Shauna M. Sallmen Eric J. Korpela
Extraterrestrial civilizations might place large, lightweight mirrors into orbit around an
exoplanet, either to alter its
climate or to provide illumination to the
planet's dark side. We previously analyzed the detectability of a fleet of 1km x 1km, 1000 kg mirrors (Korpela, Sallmen, & Leystra Greene 2015). Because these mirrors are large and lightweight, their orbits are significantly affected by the
star's radiation pressure (RP). We created a simulation package based on the REBOUND N-body simulator, incorporating RP that directs starlight towards the
planet's center. RP can always affect mirror orbits, or only during orbital night. We have simulated mirrors in initially circular orbits around
exoplanets at various locations in the habitable zones of eight types of main-sequence
stars. Initial mirror orbit sizes range from 2 to 10
planet radii, and we included 4 different initial mirror orbit orientations. For each simulation, we have the mirror's survival time, trajectory, distance from the
planet center at each time, and velocity relative to the
planet at each time. We present an analysis of trends in mirror orbit stability, and relate these to the ratios of RP and gravitational accelerations, as well as the ratio of
planet orbit period to mirror orbit period.
Read the full abstract ▾
Optics
Effective Training Principles of Physical Reservoirs
Sobhi Saeed, Mehmet Müftüoglu, Glitta R. Cheeran et al. (+3 more)
Reservoir computers benefit from the inherent complexity of optical phenomena, which provide rich, often nonlinear dynamics. However, training directly on the reservoir's output renders the system prone to overfitting and computationally inefficient during the training phase. In this work, we investigate strategies to mitigate overfitting and reduce computational overhead through output
pruning and regularization. We compare loss-minimizing search methods (Equal Search and Branch and Bound) against an output-oriented statistical filtering approach (Variance Filter) and random
pruning, highlighting advantages and disadvantages of each approach and the overall importance of informed reservoir output sampling, particularly for a shrinking latent space. We further demonstrate that enforcing readout selection across the full output spectrum improves performance, especially for non-iterative methods. Additionally, we examine L1 and L2 regularization techniques (LASSO and ridge regression), both of which significantly enhance performance on highly nonlinear tasks such as the Spiral
Benchmark. While our methods are of general use, results are obtained from and discussed exemplarily for a nonlinear fiber-optical extreme learning machine. Overall, this study provides a deep analysis of the reservoirs' hidden-layer filtering mechanisms and the output-layer training, enabling optimized performance in physical reservoir computing systems.
Read the full abstract ▾
All authors Sobhi Saeed, Mehmet Müftüoglu, Glitta R. Cheeran, Juliane Heim, Bennet Fischer, Mario Chemnitz
Instrumentation and Detectors
Radiation damage studies of silicon photomultipliers for the CMS MIP Timing Detector
Y. Musienko, A. Heering, A. Karneyeu et al. (+1 more)
Recently developed 15 um, 20 um, 25 um and 30 um
cell size Hamamatsu Silicon Photomultipliers (SiPMs) were irradiated with reactor
neutrons at JSI (Ljubljana) up to 2*10^14 n/cm^2 (1 MeV equivalent). The parameters of new and irradiated SiPMs were studied using pulsed light illumination. The effects of the
neutron radiation on breakdown voltage, signal amplitude, dark current and noise for these devices are shown and discussed.
Read the full abstract ▾
All authors Y. Musienko, A. Heering, A. Karneyeu, M. Wayne
Instrumentation and Detectors · Cross-listed from astro-ph.IM
Exploring Exoplanets with Interferometry
Sascha P. Quanz, Bertrand Mennesson, Charles Beichman et al. (+27 more)
(Extract from the Executive Summary) Humanity stands at the threshold of answering one of its most profound questions: Does life exist beyond Earth? Ongoing and upcoming space missions, together with powerful ground-based instruments, have prepared the way for a transformational next step - the detailed characterization of Earth analogs orbiting Sun-like and other
stars and the search for atmospheric biosignatures that may indicate life. Within this context, the European Space Agency's Voyage 2050 process has identified the direct detection of thermal emission from temperate terrestrial
exoplanets in the mid-infrared (mid-IR) as a top scientific priority. The Large Interferometer For
Exoplanets (LIFE) - a space-based, mid-IR nulling interferometer - is designed to meet this goal. LIFE will be capable of detecting
climate-relevant gases such as CO$_2$ and H$_2$O, identifying classical biosignatures like O$_3$ and CH$_4$, and probing additional, non-classical biosignatures. It will also provide key data for determining planetary radius, albedo, and temperature, which are essential for assessing habitability. In parallel, the U.S. National Academy has recommended a complementary mission now called the Habitable Worlds Observatory (HWO) - a ~6-meter space
telescope equipped with advanced coronagraphs to suppress starlight by a factor of ~10$^{10}$ across the visible and possibly into the near-infrared and near-ultraviolet. Together, LIFE and HWO offer synergistic capabilities, enabling a comprehensive and robust assessment of the prevalence of life-bearing
exoplanets in our
galactic neighbourhood - a first in human history. By uniting an international and interdisciplinary community of scientists and engineers, LIFE offers a credible pathway toward the direct detection and characterization of potentially habitable - and even inhabited - worlds.
Read the full abstract ▾
All authors Sascha P. Quanz, Bertrand Mennesson, Charles Beichman, Jonah T. Hansen, Felix A. Dannert, Andrea Fortier, Michael Ireland, Nicholas Beltsten, Eleonora Alei, Leonid Pogorelyuk, William O. Balmer, Denis Defrère, Gautam Vasisht, Malcolm Fridlund, Romain Laugier, Tiffany Kataria, Eugene Serabyn, Steve Ertel, Hélène Rousseau, Kevin Wagner, Rhonda Morgan, Gerard T. van Belle, Gail H. Schaefer, Jean-Philippe Berger, Taro Matsuo, Ewan Douglas, John D. Monnier, Adrian M. Glauser, Dimitri Mawet, Michael R. Meyer
Chemical Physics
Minimization of disorder as a key design principle for natural sizes of light harvesting 2 complexes
Kwang Hyun Cho, Seogjoo J. Jang and Young Min Rhee
The light harvesting 2 (LH2) complex of purple bacteria has excellent energy conversion efficiency. Clarifying the design principle behind such efficiency at the atomistic level is crucial for understanding its structure-function relationship, and can be utilized for the design of artificial light harvesting systems. To this end, we conducted comprehensive computational investigation of the dynamical and statistical nature of electronic excited states of pigment molecules in a natural LH2 complex with 9-fold
symmetry and its two non-natural in silico analogues with 6- and 12-fold
symmetries. To ensure reliable and efficient all-atomistic molecular dynamics simulations, we combined a well established interpolation approach for the construction of the potential energy surface with a neural network machine learning approach. Outcomes of these calculations clarify that non-natural forms of LH2-type complexes have significantly larger quasistatic disorder than those for the natural one. In addition, non-natural systems have more disruptions of the hydrogen bonding, underscoring its crucial role for reducing the disorder. On the other hand, local environmental dynamics are relatively insensitive to the structural changes although there is moderate enhancement in the anharmonic or interatomic components for the synthetic ones. These findings based on all-atomistic simulations provide direct computational evidence that the structure and sizes of natural LH2 complexes are designed to minimize the energetic disorder. We analyze quantitative implications of these for the energy transferring capability of the LH2 complex.
Read the full abstract ▾