Optics
Magneto-optic perturbation theory for near-complete violation of Kirchhoff's law of thermal emission at low magnetic fields
Daniel Cui and Aaswath P. Raman
Magneto-optic photonic systems can violate Kirchhoff's law of thermal emission by breaking Lorentz reciprocity. We develop a dispersive perturbation theory yielding an analytical expression for magneto-optical resonance frequency shifts in plasmonic
semiconductors under applied
magnetic fields. This expression shows the shift is governed by the overlap of the mode's optical
spin density with the magneto-optical material. We use this expression to design a III-V metasurface that achieves nonreciprocal emissivity contrast of 0.8 at only 0.1 T, and demonstrate that the theory can explain order-of magnitude differences in
magnetic field sensitivity between different photonic structures.
Read the full abstract ▾
Applied Physics
Capacitive Pixelated CMOS Electronic Nose
M. A. Basyooni-M. Kabatas, Tao Shen, Kai Betlem et al. (+5 more)
Although some of the human senses can nowadays be replaced by low-cost electronic sensors such as microphones and image sensors, a compact low-cost electronic nose (E-nose) remains elusive. In this work, an E-nose is presented that can capacitively detect volatile organic compounds (VOCs). The E-nose consists of an array of 1024 capacitive microelectrodes on a complementary metal-
oxide-
semiconductor (CMOS) chip, functionalized by inkjet printing. The pixels are coated with a UV-curable ink and metal-organic frameworks (MOFs: ZIF-8, MIL-101(Cr), MIL-140A) to create chemically diverse microdomains that generate gas-specific response patterns through adsorption-driven dielectric loading. ZIF-8 exhibits the highest response to 2-butanone, whereas the UV-curable layer responds most strongly to toluene; both show low cross-sensitivity to water vapor, enabling operation under humid conditions. After calibration in pure gases, reproducible responses to controlled binary mixtures of toluene and 2-butanone are observed. The device operates at low power, combines a large 1024-pixel array with CMOS integration, and offers application-specific functionalization by inkjet printing, providing both low cost and versatility. By further extending the range of functionalization materials, the E-nose can be applied to analyze a wide variety of gases, with potential applications in safety monitoring, health, agriculture, and robotics.
Read the full abstract ▾
All authors M. A. Basyooni-M. Kabatas, Tao Shen, Kai Betlem, Chunyu Huang, Monique A. van der Veen, Frans Widdershoven, Murali K. Ghatkesar, Peter G. Steeneken
Instrumentation and Detectors
The ATLAS Trigger System
Leonardo Toffolin
The ATLAS Trigger system is a key component of the ATLAS experiment at the CERN Large
Hadron Collider (LHC), designed to reduce the event rate from the 40 MHz
proton-
proton bunch crossing frequency to an output suitable for offline storage and analysis. During Run-3 (2022-2026), major upgrades were implemented in both the hardware-based Level-1 (L1) Trigger and the software-based High Level Trigger (HLT), to cope with increased luminosity and pile-up conditions. This paper summarises the main features of the ATLAS Trigger system, its performance in Run-3, and its role in enabling precision measurements and new physics searches.
Read the full abstract ▾
Optics
Trajectory probing of complex-frequency scattering with chirped analytic pulses
Alex Krasnok and Denis Seletskiy
Characterizing resonant scatterers is challenging because their poles and zeros usually lie away from the real-frequency axis, whereas most measurements sample only real frequencies and infer off-axis behavior from fitted models. Here we introduce complex-frequency chirped pulses: finite-energy analytic waveforms that probe a device continuously along a prescribed contour in the complex-frequency plane. We give a direct synthesis rule for an in-phase/quadrature (I/Q) waveform and show that finite-duration windowing deterministically distorts the realized trajectory, which makes it necessary to analyze only a central time interval where the window contribution is small. For stable linear time-invariant devices, we extract a time-local least-squares input–output ratio and identify when it follows the continued complex-frequency response, with errors that grow at higher traversal speeds and near resonant poles. Numerical tests on a coupled-mode resonator validate the method and show that closed contours enable an integer phase-winding consistency check. We also outline an implementation based on standard arbitrary waveform generation, I/Q modulation, coherent reception, and digital signal processing.
Read the full abstract ▾
Fluid Dynamics · Cross-listed from cs.LG
Learning Pore-scale Multiphase Flow from 4D Velocimetry
Chunyang Wang, Linqi Zhu, Yuxuan Gu et al. (+9 more)
Multiphase flow in porous media underpins subsurface energy and environmental technologies, including geological CO$_2$ storage and underground hydrogen storage, yet pore-scale dynamics in realistic three-dimensional materials remain difficult to characterize and predict. Here we introduce a
multimodal learning framework that infers multiphase pore-scale flow directly from time-resolved four-dimensional (4D) micro-velocimetry measurements. The model couples a graph network simulator for Lagrangian tracer-particle motion with a 3D U-Net for voxelized interface
evolution. The imaged pore geometry serves as a boundary constraint to the flow velocity and the multiphase interface predictions, which are coupled and updated iteratively at each time step. Trained autoregressively on experimental sequences in capillary-dominated conditions ($Ca\approx10^{-6}$), the learned surrogate captures transient, nonlocal flow perturbations and abrupt interface rearrangements (Haines jumps) over rollouts spanning seconds of physical time, while reducing hour-to-day–scale direct numerical simulations to seconds of inference. By providing rapid, experimentally informed predictions, the framework opens a route to ”digital experiments” to replicate pore-scale physics observed in multiphase flow experiments, offering an efficient tool for exploring injection conditions and pore-geometry effects relevant to subsurface carbon and hydrogen storage.
Read the full abstract ▾
All authors Chunyang Wang, Linqi Zhu, Yuxuan Gu, Robert van der Merwe, Xin Ju, Catherine Spurin, Samuel Krevor, Rex Ying, Tobias Pfaff, Martin J. Blunt, Tom Bultreys, Gege Wen
Atmospheric and Oceanic Physics
Recent Weakening of the Global Radiative Feedback
Senne Van Loon, Maria Rugenstein, Mark D. Zelinka et al. (+1 more)
Earth's
climate stability, characterized by the global radiative feedback parameter ($\lambda$), varies decadally due to changing surface temperature patterns. Recent variations in $\lambda$ are poorly understood as coordinated model simulations typically end in 2014. We apply a convolutional neural network trained on
climate model simulations to observation-based surface temperature reconstructions to estimate variations in $\lambda$ up to 2025. We find that $\lambda$ reached a minimum (maximum stability) around the mid 1990s ($\lambda\simeq -3 {\rm Wm^{-2}/K}$), but has since weakened significantly ($\lambda\simeq -2\, {\rm Wm^{-2}/K}$). We confirm these results with
climate model simulations extended to 2022. The recent $\lambda$ weakening is not significantly affected by El Niño Southern Oscillation or Pacific Decadal Oscillation. Attribution reveals that warming in the subtropical Northeast Pacific is an important driver of the recently weakened feedback, confirmed by targeted experiments in E3SMv2. Our approach enables near real-time monitoring of Earth's
climate stability.
Read the full abstract ▾
All authors Senne Van Loon, Maria Rugenstein, Mark D. Zelinka, Timothy Andrews
Optics
Physics-Guided Inverse Design of Optical Waveforms for Nonlinear Electromagnetic Dynamics
Hao Zhang, Jack Hirschman, Randy Lemons et al. (+7 more)
Structured optical waveforms are emerging as powerful control fields for the next generation of complex photonic and electromagnetic systems, where the temporal structure of light can determine the ultimate performance of scientific instruments. However, identifying optimal optical drive fields in strongly nonlinear regimes remains challenging because the mapping between optical inputs and system response is high-dimensional and typically accessible only through computationally expensive simulations. Here, we present a physics-guided deep learning framework for the inverse design of optical temporal waveforms. By training a light-weighted surrogate model on simulations, the method enables gradient-based synthesis of optical profiles that compensate nonlinear field distortions in driven particle-field systems. As a representative application, we apply the approach to the generation of electron beams used in advanced
photon and particle sources. The learned optical waveform actively suppresses extrinsic emittance growth by more than 52% compared with conventional Gaussian operation and by approximately 9% relative to the theoretical flattop limit in simulation. We further demonstrate experimental feasibility by synthesizing the predicted waveform using a programmable pulse-shaping platform; incorporating the measured optical profile into beamline simulations yields a 31% reduction in the extrinsic emittance contribution. Beyond accelerator applications, this work establishes a general way for physics-guided inverse design of optical control fields, enabling structured light to approach fundamental performance limits in nonlinear photonic and high-frequency electromagnetic systems.
Read the full abstract ▾
All authors Hao Zhang, Jack Hirschman, Randy Lemons, Nicole R. Neveu, Joseph Robinson, Auralee L. Edelen, Tor O. Raubenheimer, Dan Wang, Ji Qiang, Sergio Carbajo
Plasma Physics
Non-dimensional confinement scaling in similar negative triangularity plasmas on the DIII-D and TCV tokamaks
A. Marinoni, C. Chrystal, S. Coda et al. (+25 more)
Similarity experiments were performed on the DIII-D and TCV tokamaks to explore the scaling of energy confinement in negative triangularity
plasmas using non-dimensional variables. Near up-down symmetric
plasmas with large top-bottom averaged negative triangularity were created in a lower single null configuration, with the shape of the separatrix being closely matched between the two devices. The normalized energy confinement is found to weakly improve at increasing collisionality and, between the two devices, shows a machine size scaling behavior between Bohm and gyro-Bohm. Engineering scaling on a large DIII-D
dataset is in agreement with the non-dimensional experiment.
Read the full abstract ▾
All authors A. Marinoni, C. Chrystal, S. Coda, R. Coosemans, C. Marini, M. Podesta, O. Sauter, M. Agostini, M.E. Austin, E. Belli, J. Candy, M. Gorelenkova, D. Hamm, A.W. Hyatt, M. Knolker, M. La Matina, P. Lunia, S. Mordijck, A.O. Nelson, T.H. Osborne, C. Paz-Soldan, L. Porte, U. Sheikh, F. Scotti, K.E. Thome, M. Van Zeeland, the DIII-D, TCV Teams
Space Physics
CLARE: Classification-based Regression for Electron Temperature Prediction
Michael Liang, Blake DeHaas, Naomi Maruyama et al. (+3 more)
Electron temperature (Te) is an important parameter governing space weather in the upper atmosphere, but has historically been underexplored in the space weather machine learning literature. We present CLARE, a machine learning model for predicting electron temperature in the Earth's plasmasphere trained on AKEBONO (EXOS-D) satellite measurements as well as
solar and geomagnetic indices. CLARE uses a classification-based regression architecture that transforms the continuous Te output space into 150 discrete classification intervals. Training the model on a classification task improves prediction accuracy by 6.46% relative compared to a traditional regression model while also outputting uncertainty estimation information on its predictions. On a held out test set from the AKEBONO data, the model's Te predictions achieve 69.67% accuracy within 10% of the ground truth and 46.17% on a known geomagnetic storm period from January 30th to February 7th, 1991. We show that machine learning can be used to produce high-accuracy Te models on publicly available data.
Read the full abstract ▾
All authors Michael Liang, Blake DeHaas, Naomi Maruyama, Xiangning Chu, Takumi Abe, Koh-Ichiro Oyama
Optics · Cross-listed from cond-mat.mes-hall
Annihilation of Dirac points and its topological obstruction in a photonic Kagome lattice
Zhaoyang Zhang, Matthieu Finck, Changchang Li et al. (+7 more)
Dirac points (DPs) are topological singularities that determine the extraordinary properties of two-dimensional materials. They are generally classified by discrete topological invariants, which determine the possibility of DPs' annihilation upon their collision. Here, we study the behaviors of DPs within a photonic Kagome
lattice created in atomic vapor. With optically engineering the potential difference among three sites constituting the Kagome unit
cell while preserving time-reversal
symmetry and the stability of an isolated DP, the DPs move in reciprocal space. By employing conical diffraction to measure their position and the topological invariant (Euler number), we demonstrate an obstruction to DPs' annihilation during collision and a transition to a case where the Euler number changes and annihilation occurs. Such topological transition is induced by a non-Abelian frame rotation of the eigenstates around the Brillouin zone
torus. The associated conversion of the DP quaternionic charges during their motion explains the change of Euler number.
Read the full abstract ▾
All authors Zhaoyang Zhang, Matthieu Finck, Changchang Li, Shun Liang, Jerome Dubois, Yumin Tian, Jiahao Wen, Yanpeng Zhang, Guillaume Malpuech, Dmitry Solnyshkov
Classical Physics · Cross-listed from nlin.CD
An angular-momentum preserving dissipative model for the point-mass N -body problem
Matheus Lazarotto and Clodoaldo Ragazzo
A simple mathematical model emulating energy dissipation due to
tidal effects is proposed. In this model, forces acting between masses remove energy but preserve the total angular momentum of the system. We study the effect of such forces on the particular family of orbits in central configurations, and show that a specific dependence on the mutual distances between the bodies leads to homographic equations equivalent to those of the two-body problem with dissipation. We then describe in detail the
topology of solutions of the dissipative two-body system via Poincaré compactification. Finally, we present equations averaged over Keplerian motion showing no influence of the dissipation on periapsis precession.
Read the full abstract ▾
Atmospheric and Oceanic Physics
FloeNet: A mass-conserving global sea ice emulator that generalizes across climates
William Gregory, Mitchell Bushuk, James Duncan et al. (+8 more)
We introduce FloeNet, a machine-learning emulator trained on the Geophysical Fluid Dynamics Laboratory global sea ice model, SIS2. FloeNet is a mass-conserving model, emulating 6-hour mass and area budget tendencies related to sea ice and snow-on-sea-ice growth, melt, and advection. We train FloeNet using simulated data from a reanalysis-forced ice-ocean simulation and test its ability to generalize to pre-industrial control and 1% CO2 climates. FloeNet outperforms a non-conservative model at reproducing sea ice and snow-on-sea-ice mean state, trends, and inter-annual variability, with volume anomaly correlations above 0.96 in the Antarctic and 0.76 in the Arctic, across all forcings. FloeNet also produces the correct thermodynamic vs dynamic response to forcing, enabling physical interpretability of emulator output. Finally, we show that FloeNet outputs high-fidelity coupling-related variables, including ice-surface skin temperature, ice-to-ocean salt flux, and melting energy fluxes. We hypothesize that FloeNet will improve polar
climate processes within existing atmosphere and ocean emulators.
Read the full abstract ▾
All authors William Gregory, Mitchell Bushuk, James Duncan, Elynn Wu, Adam Subel, Spencer K. Clark, Bill Hurlin, Oliver Watt-Meyer, Alistair Adcroft, Chris Bretherton, Laure Zanna
Optics · Cross-listed from quant-ph
Doppler-induced tunable and shape-preserving frequency conversion of microwave wave packets
Felix Ahrens, Enrico Bogoni, Renato Mezzena et al. (+7 more)
In superconducting electronics, the ability to control the frequency of microwave wave packets is crucial for several applications, such as the operation of superconducting
quantum processors and the readout of superconducting sensors. We introduce a new approach to microwave frequency conversion that harnesses a dynamic Doppler effect induced by a propagating front that separates regions of different phase velocities. Employing a high-kinetic-inductance superconducting transmission line in a travelling-wave geometry, we were able to implement frequency shifts of microwave wave packets at 500$\,$MHz and 4$\,$GHz of up to 3.7$\,$% while fully preserving their temporal shape. In contrast to conventional methods based on frequency-mixing, our Doppler-induced frequency-conversion method avoids spurious mixing products, is continuously tunable by a quasi-dc current amplitude, and allows to imprint arbitrary patterns on the instantaneous frequency profile of temporally long wave packets. By engineering transmission lines that allow for larger phase-velocity changes and/or by cascading multiple Doppler-induced frequency conversions, an unlimited amount of frequency shifting is in principle attainable. These features demonstrate the potential of our frequency-conversion technique as a promising tool for advanced control of microwave wave packets for different
quantum applications.
Read the full abstract ▾
All authors Felix Ahrens, Enrico Bogoni, Renato Mezzena, Andrea Vinante, Nicolò Crescini, Alessandro Irace, Andrea Giachero, Gianluca Rastelli, Iacopo Carusotto, Federica Mantegazzini
Physics and Society · Cross-listed from econ.GN
Topology as information: Network effects in corporate lending
Anna Pirogova, Anna Mancini, Tiziano Squartini et al. (+1 more)
A central challenge in financial economics is understanding how credit networks form under informational noise. We introduce the concept of topological capital, arguing that banks increasingly rely on topological certification, interpreting a borrower's connectivity as a primary proxy for creditworthiness. Using a novel
dataset of bank-firm relationships manually extracted from Italian financial statements, we implement a multi-stage empirical framework, benchmarking empirical patterns against a maximum-
entropy benchmark, to separate the determinants of credit access from those of loan volumes. Our results indicate that network
topology systematically outperforms traditional fundamentals. In the link-formation stage, connectivity breeds further connectivity through an amplified preferential attachment mechanism. In the loan-sizing stage, network strength absorbs the explanatory power of balance-sheet metrics, documenting a profound network substitution effect where topological signals effectively replace physical collateral across all corporate segments. For SMEs, we identify a critical signal divergence: reported debt acts as a risk signal, while network footprint serves as
market validation. Furthermore, we reveal a diversification paradox: while firms fragment debt to avoid hold-up risks, over-diversification leads to a complexity penalty that stagnates credit depth and inflates systemic Loss Given Default. Ultimately, our findings signal the twilight of the balance sheet as the primary anchor of corporate lending, calling for a shift toward topological macro-prudential supervision to manage vulnerabilities invisible to traditional bilateral indicators.
Read the full abstract ▾
All authors Anna Pirogova, Anna Mancini, Tiziano Squartini, Giulio Cimini
Plasma Physics
Combination of quasi-isodynamic and piecewise omnigenous magnetic fields
J. L. Velasco, I. Calvo, V. Fernández-Pacheco et al. (+5 more)
Due to their simultaneous optimization for radial and parallel neoclassical transport, quasi-isodynamic fields have been the main choice of stellarator
magnetic configuration for most fusion reactor candidates in recent years. However, achieving a high degree of quasi-isodynamicity often comes at the cost of a strong shaping of the flux surfaces of the stellarator and complex coil geometries. In this work, the concepts of quasi-isodynamicity and piecewise omnigenity are combined to form QI-pwO fields. These fields are quasi-isodynamic in the low-field region of the
magnetic surface, whereas they significantly depart from quasi-isodynamicity in the high-field region without sacrificing the neoclassical transport properties of quasi-isodynamic fields. This departure could make it easier to integrate the optimization of neoclassical transport with other physical and technological aspects of a stellarator reactor.
Read the full abstract ▾
All authors J. L. Velasco, I. Calvo, V. Fernández-Pacheco, M. Padidar, H. Liu, E. Sánchez, G. Yu, C. Zhu
Chemical Physics
Nuclear-Electronic Quantum Dynamics in a Plasmonic Nanocavity
Jonathan H. Fetherolf, Tim Duong, Tao E. Li et al. (+1 more)
Plasmonic nanocavities are a promising platform for strong light-matter coupling and enhanced spectroscopies at the single-molecule level. These nanoscale environments are challenging to model due to their strongly
multimodal character and short cavity lifetimes. Herein, we study the effects of these environments using real-time
nuclear-electronic orbital time-dependent density functional theory (RT-NEO-TDDFT) coupled to multiple classical cavity modes in a manner that includes cavity loss. In RT-NEO-TDDFT, the
quantum mechanical densities of all electrons and specified
nuclei, typically
protons, are propagated in real time. We show that a cavity with many modes at different frequencies can be used to probe and modify the
nuclear-electronic
quantum dynamics of chemical systems. Ultrafast excited-state
proton transfer reactions can be probed through the time- and energy-resolved cavity emission of a multimode cavity. Under strong coupling conditions, the cavity can modify the dynamics, in some cases suppressing
proton transfer and exhibiting Rabi-like oscillations of the cavity emission due to polariton formation. Utilizing the spectral density for an experimentally relevant nanoparticle-on-mirror single-molecule cavity, we show that an excited-state
proton transfer system can evolve into resonance with the cavity even when initially out of resonance with the dominant cavity peak. In this case, tuning the dominant cavity peak to be resonant with the electronic transition leads to polariton formation for a small collection of molecules. The RT-NEO framework with multimode cavities enables the efficient simulation of chemical reactions in physically realistic electromagnetic environments, providing fundamental insights into the dynamics and associated spectroscopic signatures.
Read the full abstract ▾
All authors Jonathan H. Fetherolf, Tim Duong, Tao E. Li, Sharon Hammes-Schiffer
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Optimal Experimental Design for Reliable Learning of History-Dependent Constitutive Laws
Kaushik Bhattacharya, Lianghao Cao and Andrew Stuart
History-dependent constitutive models serve as macroscopic closures for the aggregated effects of micromechanics. Their parameters are typically learned from experimental data. With a limited experimental budget, eliciting the full range of responses needed to characterize the constitutive relation can be difficult. As a result, the data can be well explained by a range of parameter choices, leading to parameter estimates that are uncertain or unreliable. To address this issue, we propose a
Bayesian optimal experimental design framework to quantify, interpret, and maximize the utility of experimental designs for reliable learning of history-dependent constitutive models. In this framework, the design utility is defined as the expected reduction in parametric uncertainty or the expected information gain. This enables in silico design optimization using simulated data and reduces the cost of physical experiments for reliable parameter identification. We introduce two approximations that make this framework practical for advanced material testing with expensive forward models and high-dimensional data: (i) a Gaussian approximation of the expected information gain, and (ii) a surrogate approximation of the Fisher information
matrix. The former enables efficient design optimization and interpretation, while the latter extends this approach to batched design optimization by amortizing the cost of repeated utility evaluations. Our numerical studies of uniaxial tests for viscoelastic solids show that optimized specimen geometries and loading paths yield image and force data that significantly improve parameter identifiability relative to random designs, especially for parameters associated with
memory effects.
Read the full abstract ▾
Accelerator Physics
The N=126 Factory: A New Multi-Nucleon Transfer Reaction Facility
A.A. Valverde, M.S. Martin, W.S. Porter et al. (+23 more)
Multi-nucleon transfer (MNT) reactions between two heavy ions offer an effective method of producing heavy,
neutron-rich
nuclei that cannot currently be accessed efficiently using traditional production techniques. These
nuclei are important for understanding many astrophysical phenomena, such as the formation of the r-process $A\sim 195$ abundance peak. The N=126 Factory currently commissioning at Argonne National Laboratory's ATLAS facility will make use of these reactions to allow for the study of these
nuclei. To convert MNT reaction products, which have a wide angular distribution, into a collimated, bunched beam suitable for experiments, a series of apparatus will be used. These start with a large-volume gas catcher for stopping the reaction products, which are then extracted through a radiofrequency quadrupole ion guide, undergo preliminary dipole
magnetic separation, cooling and bunching in a Cooler-Buncher, and final separation using a multi-reflection time-of-flight mass separator, before final delivery to experimental systems.
Read the full abstract ▾
All authors A.A. Valverde, M.S. Martin, W.S. Porter, A.M. Houff, M. Brodeur, J.A. Clark, Y. Cho, A. Jacobs, R.A. Knaack, F. Köhler, K. König, O.S. Kubiniec, A. LaLiberte, B. Liu, B. Maass, A. Mitra, P. Mueller, C. Müller-Gatermann, W. Nörtershäuser, M.B. Oberling, J. Palmes, C. Quick, E.S.C. Ribeiro, J. Rohrer, G. Savard, J. Spahn
Applied Physics · Cross-listed from quant-ph
A Traveling-Wave Parametric Amplifier With Integrated Diplexers
C. Denney, K. Genter, K. Cicak et al. (+4 more)
Traveling-Wave Parametric Amplifiers (TWPAs) are ubiquitous in superconducting
circuit readout, providing high gain with near-
quantum-limited noise performance across a wide bandwidth. Their operation, however, relies on a strong microwave pump tone that is typically delivered using off-chip passive components, such as directional couplers or diplexers. These external elements add loss, increase system complexity, and limit scalability. Here, we present a traveling-wave parametric amplifier that incorporates on-chip input and output diplexers for pump routing. This co-fabricated architecture offers a compact and scalable solution for superconducting-
circuit readout.
Read the full abstract ▾
All authors C. Denney, K. Genter, K. Cicak, J. D. Teufel, J. Aumentado, F. Lecocq, M. Malnou
Computational Physics · Cross-listed from hep-ph
All-electron dark matter-electron scattering with random-phase approximation dielectric screening and local field effects
Cyrus Dreyer, Rouven Essig, Marivi Fernandez-Serra et al. (+2 more)
Accurate predictions for dark matter-electron scattering in solids require an all-electron treatment together with a faithful description of dielectric screening beyond simple approximations. In particular, local field effects, arising from microscopic inhomogeneities of the electronic response, can significantly modify scattering rates across relevant momentum and energy scales. We present an all-electron framework for computing dark matter-electron scattering rates that incorporates dielectric screening at the random-phase approximation (RPA) level, including local field effects. Using crystalline silicon as a
benchmark, we show that local field effects play an important role both at large momentum transfers, spanning multiple Brillouin zones, and at low momentum near the plasmon resonance. We compute electron
recoil spectra and projected sensitivities for non-relativistic
halo dark matter and for boosted dark matter or other dark-sector particles, which are sensitive to the impact of local field effects in these high and low momentum regimes, respectively. We further present RPA dielectric functions including local field effects for Ge, GaAs, SiC, and diamond, enabling a systematic comparison across target materials. These developments are implemented in the open source code QCDark2.
Read the full abstract ▾
All authors Cyrus Dreyer, Rouven Essig, Marivi Fernandez-Serra, Megan Hott, Aman Singal
Data Analysis and Statistics
Classifying hadronic objects in ATLAS with ML/AI algorithms
Leonardo Toffolin
The identification of hadronic final states plays a crucial role in the physics programme of the ATLAS Experiment at the CERN LHC. Sophisticated artificial intelligence (AI)
algorithms are employed to classify jets according to their origin, distinguishing between
quark- and
gluon-initiated jets, and identifying hadronically decaying heavy objects such as W
bosons and top
quarks. This contribution summarises recent developments in constituent-based tagging architectures, including graph neural networks (GNNs) and
transformer-based approaches, their performance in simulated and real data, and future perspectives towards data-driven optimisation and model-independent tagging strategies.
Read the full abstract ▾
Optics · Cross-listed from quant-ph
Transition from Statistical to Hardware-Limited Scaling in Photonic Quantum State Reconstruction
Attila Baumann, Zsolt Kis, János Koltai et al. (+1 more)
The theoretical efficiency of classical shadow
tomography is predicated on a perfect Haar-random
unitary ensemble, yet this mathematical ideal remains physically unattainable in near-term hardware. Here, we report the experimental discovery of a fundamental accuracy bound on integrated photonic processors: a “Hardware Horizon” where the reconstruction error undergoes a sharp phase transition. While the error initially obeys the predicted statistical scaling $\mathcal{O}(M^{-1/2})$, it abruptly saturates at a floor determined by the spectral distortions of the realized
unitary group. By deriving a phenomenological error model, we decouple the competing mechanisms of static coherent spectral distortion and dynamic decoherence, demonstrating that this intrinsic noise floor imposes a hard bound that statistical accumulation cannot overcome. These findings establish that the utility of shadow
tomography on NISQ (noisy intermediate-scale
quantum) hardware is defined by a specific scaling law involving hardware parameters, necessitating active compensation strategies to bridge the gap between theoretical purity and the noisy reality of integrated photonics.
Read the full abstract ▾
All authors Attila Baumann, Zsolt Kis, János Koltai, Gábor Vattay
Chemical Physics
Permutation invariant multi-scale full quantum neural network wavefunction
Pengzhen Cai, Yubing Qian, Li Deng et al. (+8 more)
Solving the intricate
quantum behavior of interacting particles is key to unlocking the mysteries of condensed matter, but capturing their complex correlations across different scales remains a monumental challenge. We introduce a neural network framework that overcomes this barrier by modeling the full
quantum wavefunction of a system, including electrons,
nuclei and
muons, directly capturing the full
quantum effects beyond the Born-Oppenheimer approximation. The neural network approximates joint wavefunction of different interacting particles with a rigorous handling of permutation invariance, enabling simultaneous treatment of
nuclear quantum effects and electron-
nucleus-
muon couplings without explicit excited states. Validated on molecular systems, this approach offers a computationally feasible way to model full
quantum phenomena in complex many-body systems, establishing a direct connection between fundamental particle properties and emergent material behavior.
Read the full abstract ▾
All authors Pengzhen Cai, Yubing Qian, Li Deng, Weizhong Fu, Lei Yang, Zhiyu Sun, Xin-Zheng Li, En-Ge Wang, Liangwen Chen, Weiluo Ren, Ji Chen
Biological Physics · Cross-listed from cond-mat.stat-mech
Non-Markovian Entropy Dynamics in Living Systems from the Keldysh Formalism
Feiyi Liu, Min Guo, Hongwei Tan et al. (+1 more)
Living systems are open nonequilibrium systems that continuously exchange energy, matter, and information with their environments, leading to
stochastic dynamics with
memory and active fluctuations. In this study, we develop a non-Markovian theoretical framework for the
entropy dynamics of living systems based on the Keldysh functional formalism and
stochastic thermodynamics. The approach naturally incorporates colored environmental noise,
memory-dependent dissipation, and many-body interactions, yielding generalized Langevin dynamics and non-Markovian master equations. Within this framework we derive an exact frequency-domain expression for the
entropy production rate and show that violations of the fluctuation-dissipation relation provide a direct thermodynamic signature of active biological fluctuations. We further demonstrate that environmental
memory enhances low-frequency fluctuations and
entropy production, leading to critical slowing down near dynamical instability. These results provide a microscopic physical foundation for the
entropy "bathtub" picture of living systems and connect
entropy evolution with development, aging, and death in nonequilibrium dynamics.
Read the full abstract ▾
All authors Feiyi Liu, Min Guo, Hongwei Tan, Yang Wang