High Energy Astrophysical Phenomena
Widespread steep X-ray spectra of luminous $z\simeq5.85-7.6$ quasars and their implications for future X-ray surveys
A. Tortosa, L. Zappacosta, E. Piconcelli et al. (+51 more)
Luminous
quasars (QSOs) at the Epoch of Reionization (EoR, $z\gtrsim6$) host billion-solar-mass black holes assembled within the first Gyr of
cosmic time. Their X-ray emission probes the innermost accreting regions and constrains the physical state of the corona during early supermassive black hole (SMBH) growth. Previous results from the HYPerluminous
quasars at the Epoch of ReionizatION (HYPERION) sample of $z \gtrsim 6$ QSOs, powered by the most rapidly accreted SMBHs, suggested a link between X-ray spectral slope and long-term growth efficiency. We test whether the steep X-ray spectra of luminous z>6 QSOs are a general property of early SMBH growth or a peculiarity of the most efficiently grown SMBHs, using a sample extended mainly to the lowest growth efficiencies. We analysed the largest and most X-ray sensitive sample of 28 luminous QSOs at $z \simeq 5.85-7.56$ (26 used in the correlation analysis), combining HYPERION targets, luminous non-HYPERION sources, and QSOs powered by moderately accreting SMBHs newly observed in our XMM-Newton Large Programme. We re-derived black hole masses and growth indicators for the entire sample homogeneously, incorporating the latest JWST-based spectroscopic measurements to ensure the most consistent and high-quality characterization across all targets. We found no statistically significant correlation between coronal spectral parameters and BH growth indicators: luminous $z \gtrsim 6$ QSOs systematically show steep X-ray continua, with high
photon indices and/or low high-energy cutoffs, consistent with cold coronae, possibly linked to rapid
accretion. Finally, if steep X-ray slopes are common among early AGN, they may significantly reduce count rates, at fixed intrinsic luminosity, in future surveys of the EoR, affecting the detectability of steep-spectrum X-ray sources and thus the census of the early AGN population.
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All authors A. Tortosa, L. Zappacosta, E. Piconcelli, I. Saccheo, G. Lanzuisi, E. Bertola, F. Vito, M. Bischetti, M. Brazzini, M. Castellano, S. Carniani, A. Comastri, S. Gallerani, R. Gilli, F. La Franca, P. Madau, R. Maiolino, G. Miniutti, B. Musiimenta, C. Piscitelli, F. Salvestrini, F. Tombesi, R. Tripodi, C. Vignali, M. Volonteri, A. Bongiorno, L. Borrelli, M. Brusa, I. V. Chilingaria, F. Civano, S. Cristiani, V. D'Odorico, D. De Cicco, C. Done, M. Elvis, X. Fan, C. Feruglio, F. Fiore, A. Grazian, M. Guainazzi, F. Haardt, A. Luminari, N. Menci, R. Middei, F. Nicastro, M. Paolillo, M. Perna, S. Puccetti, R. Schneider, V. Testa, R. Valiante, L. Vallini, E. Vanzella, G. Vietri
Statistical Mechanics
Trend formation with sparse global sampling
Sarath Sankar, Abhijit Sinha, Shankar Ghosh et al. (+2 more)
Achieving global coordination without a central controller or dense global communication is a defining challenge for both biological collectives and engineered swarms. We introduce and analyze a minimal model in which self-propelled
agents in a bounded domain periodically reorient their motions toward the centroid of a small, randomly chosen subset of their peers, with no direct sensing of any individual neighbor's position or heading. We show that this sparse, non-local sampling rule reliably drives an initially disordered population to a globally aligned, nematic state, and that shrinking the sampled subset – down to the minimum of two
agents – accelerates ordering rather than impeding it: the resulting estimation noise, filtered through a
geometric turning rule, is itself the engine of
symmetry breaking. We derive an analytical criterion, in quantitative agreement with simulation, that predicts when this ordering succeeds as a function of the sampling size and sampling frequency. We confirm the mechanism experimentally in a swarm of up to fifteen differential-drive
robots. These results identify sparse random sampling as an information-efficient route to collective coordination, with implications for understanding animal collectives and for designing communication-limited robotic swarms.
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All authors Sarath Sankar, Abhijit Sinha, Shankar Ghosh, Vikram Tripathi, Amitava Bhattacharya
Atmospheric and Oceanic Physics · Cross-listed from cs.AI
SciExam for ENSO: Can AI Agents Build Climate Models?
Yinling Zhang, Langchen Liu, Dongbin Xiu et al. (+4 more)
Language-model
agents are increasingly asked to carry out open-ended scientific research, yet their results are usually graded against a known answer, a rubric, or a
language-model reviewer, none of which can tell whether a new scientific model is valid. The AI Science Exam for El Nino-Southern Oscillation (SciExam for ENSO) is a
benchmark in which
agents build low-order
stochastic models of ENSO, the dominant mode of interannual
climate variability, from real observations. Within a six-hour budget,
agents process the observations, write their own diagnostics, which are then frozen, and develop a model using only these diagnostics as feedback. Hidden graders then test whether the model reproduces ENSO's statistics, recovers unobserved variables, and forecasts held-out years, and score a published model in the same way. Across twelve
agent systems, six produce models that score higher than the published model, mainly through better reconstruction and forecasting. The simplified forms of the stronger models are each compatible with one of the two competing explanations of ENSO's warm-cold asymmetry, an open debate that the task never mentions. Controlled runs of the top system under varied information suggest that its scores do not come from recalling the dated observational record and that the information it receives shapes how it builds its model. SciExam for ENSO can thus evaluate
agent research where no answer is known, and the results suggest that
agents can already build competitive models whose structures bear on questions that scientists still debate.
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All authors Yinling Zhang, Langchen Liu, Dongbin Xiu, Xueyan Zou, Xu Kuang, Mengdi Wang, Shilong Liu
High Energy Physics (Theory)
Black hole radial geometry from a quantum spin chain
Boris A. Khanikati
We show that the radial differential equation for a scalar field in an anti-de Sitter black brane background can be encoded in an exactly solvable semi-infinite
spin chain independent of dimensionality. The radial coordinate emerges as
lattice quasimomentum.
Spin coherence encodes the geometry, while the spectrum and response recover gravitational observables and yield exact screening-mass sum rules. We implement the finite chain on a superconducting processor. At finite filling and low energies, the model yields a conformal field theory in a coordinate proportional to proper radial distance.
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Mathematical Physics · Cross-listed from math.PR
The Tutte embedding of tree-weighted planar maps converges to Liouville quantum gravity
Nina Holden
Suppose $M$ is a planar map with a macroscopic boundary decorated by a spanning tree $T$, such that $(M,T)$ has been sampled from the critical Boltzman measure. We show that $M$ converges to a Liouville
quantum gravity disk with parameter $\gamma=\sqrt{2}$ under the Tutte
embedding as the length of the boundary of $M$ goes to infinity. Furthermore, conditioned on $(M,T)$, the simple random walk on $M$ converges to a planar Brownian motion modulo reparametrization of time.
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Quantum Physics
Standard estimators cannot represent fault-tolerant workloads at measured error rates: evaluated, evidence-based uncertainty for quantum resource estimation
Furqan Nasir and Sher Jeel Ahmad
Estimates of the
quantum resources needed to run fault-tolerant
algorithms are almost always reported as single numbers, even though they rest on uncertain hardware parameters and on cost models that disagree. We present an open, tool-agnostic framework that propagates evidence-based priors over fault-tolerant hardware parameters through five surface-code cost models, adds correlated-input sensitivity analysis, and evaluates the resulting intervals with a pre-registered three-layer protocol. Applied to RSA-2048, ECC-256, AES-256, and a Hubbard-model simulation on a superconducting surface-code architecture, it produces three coupled findings that together undermine the single-number convention. Propagating the error rates hardware has actually demonstrated, whose median across five large-array devices is about 4 x 10^-3 rather than the conventional 10^-3, widens the 90% physical-
qubit interval to roughly forty times and lifts its median about 4.6 times above the conventional point estimate. Against the same inputs, five independently structured cost models, including two third-party
estimators (the Azure
Quantum Resource
Estimator and Qualtran) that agree with each other to within about ten percent, disagree by a stable factor of two, a structural uncertainty that no single tool reveals. Most consequentially, both third-party
estimators exceed their representable envelope for 80 to 100 percent of the evidence-based parameter space, and the AES-256 gate count overflows one tool's counter outright, so these workloads cannot be represented at measured error rates at all. The optimistic convention conceals every one of these effects. The uncertainty machinery we use is standard. The contribution is its evidence-based application, the pre-registered evaluation, and the finding that standard tools break down precisely where hardware operates.
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Statistical Mechanics · Cross-listed from econ.EM
From a Hierarchy of Stochastic Differential Equations to a Hierarchy of Generalized Beta Distributions
Siqi Shao and R. A. Serota
We introduce a mean-reverting
stochastic differential equation with a three-component
stochastic term and show that it generates a hierarchy of steady-state (stationary) distributions. At the top level, the hierarchy is described by a modified-Beta distribution, while one- and two-parameter reductions produce compact-support, power-law-tailed, and exponential-type limiting families within a single
stochastic framework. We then construct two generalized extensions of this hierarchy. In the first, the power transformation is applied directly at the level of the
stochastic differential equation; in the second, the same transformation is applied only after the stationary modified-Beta hierarchy has been obtained. While these two procedures agree on important lower branches and limiting cases they generally differ at the top level. The generalized hierarchy is therefore not unique: nonlinear transformation and stationary-state reduction do not commute. For both routes, we derive the probability density and cumulative distribution functions, express their parameters in terms of the underlying
stochastic dynamics, clarify the relations among their limiting cases, and compare the resulting families with the traditional Generalized Beta framework.
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Quantum Physics
Optimized discrete Wigner representations and non-stabilizerness in qubit systems
A.B. Klimov, C. Muñoz and I. Sainz
The apparent absence of a stabilizer-specific phase-space description for
qubit systems is not fundamental, but arises from fixing a state-independent discrete Wigner map. Allowing the phase-space representation to adapt to the state through a simple optimization procedure, we prove that every
qubit stabilizer state admits an exact delta-function discrete Wigner representation for any fixed phase-space partition. This optimization can be implemented directly at the level of measurement statistics by selecting maximal probabilities across mutually unbiased bases forming the partition. This construction naturally leads to a non-stabilizer witness defined by the optimized value of the Wigner function at the origin. Stabilizer states saturate the universal lower bound of this witness for pure states, whereas a value strictly larger than the bound certifies non-stabilizerness. Although the witness is not a finite-size resource monotone, it becomes sharply concentrated and effectively invariant under random Clifford transformations in the large-
qubit limit, suggesting an asymptotically linear growth law for broad families of non-stabilizer states. Our results restore a meaningful
geometric phase-space characterization of
qubit stabilizer structure and provide an operational diagnostic of non-stabilizerness.
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Mathematical Physics
Iterated Graph Systems (II): Bernoulli percolation and critical-exponent universality classes on hierarchical lattices
Ziyu Neroli
We study Bernoulli percolation and its universality classes on hierarchical
lattices generated by edge iterated graph systems (EIGS). By examining eight critical exponents, we prove that no finite family of substitution-multiplicative dimensions completely determines universality, providing a rigorous proof of the failure of dimension-based universality discussed by Hauser and Saxena [Phys. Rev. B 34 (1986), 8193-8195]. Although the six exponentials
theorem provides number-theoretic criteria for scale commensurability, we further disprove the
conjecture that critical-exponent universality requires commensurate discrete renormalisation scales.
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Statistical Mechanics
Effective-Geometry Rescaling and Universal Critical Behavior in the Anisotropic Three-State Potts Model on the Square Lattice
Fan Yang, Jian Gao, Lu Liu et al. (+1 more)
We study the two-dimensional anisotropic three-state Potts ferromagnet on the square
lattice using Wolff single-cluster Monte Carlo simulations and finite-size scaling. For coupling ratios $\lambda=J_y/J_x=0.5$, $0.75$, and $1$, finite-size scaling of the correlation ratio yields critical behavior consistent with the two-dimensional three-state Potts universality class. The scaling of the leading Fisher zeros gives a correlation-length exponent consistent with $\nu=5/6$, while their cumulative density is consistent with the expected specific-heat exponent $\alpha=1/3$. We further characterize the anisotropy at criticality using directional correlation ratios $R_x$ and $R_y$ together with directional FK wrapping probabilities. For $\lambda=0.5$ on a physically square
lattice, $R_x$ and $R_y$ approach distinct critical values while yielding a common correlation-length exponent. From the wrapping probabilities, we independently determine an effective aspect ratio $\rho_e^\square=0.6413(5)$, in close agreement with the theoretical value $\rho_e^{\square,\mathrm{th}}\simeq0.64150030$ obtained from the isoradial representation. Using the theoretical value to set the physical aspect ratio restores directional equivalence, $R_x\simeq R_y$, and brings the overall correlation ratio toward the isotropic-square reference. The results show that spatial anisotropy changes the effective critical geometry without altering the bulk three-state Potts universality class.
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All authors Fan Yang, Jian Gao, Lu Liu, Yuhai Liu
Computational Physics
Self-Shielded Multigroup Microscopic Cross Section Generation Using Targeted Artificial Neural Networks
Joshua Nichols, Andrew Osborne, Ben Whewell et al. (+1 more)
Multigroup
neutron transport models are used widely in simulation codes due to their
memory and compute efficiency compared to continuous energy methods. However, multigroup codes rely on nuclear data prepared using approximate methods that require significant manual effort and expertise to account for self-shielding effects. In addition, multigroup nuclear data libraries prepared in this way can be limited in their applicability for modeling diverse reactors and operating conditions. Here we show that trained artificial neural networks can predict self-shielded microscopic cross sections accurately in pincell simulations containing uranium dioxide fuel with a fixed geometry and temperature. The model's predictive accuracy remains high across fuel enrichment and burnup ranges encountered in light water reactors. Critically, the neural networks' predictions require only the atomic concentration of the fuel's constituent nuclides as inputs. The networks are trained using 8,704 samples of training and validation data generated using continuous energy OpenMC pincell simulations. The trained neural networks predict self-shielded total, fission, absorption, elastic, and total scattering cross sections for combinations of 90 nuclides in the CASMO-8 energy group structure. The neural networks' predictions of self-shielded cross sections are validated by comparison with 1,500 samples of reference cross sections unseen during training and tallied using OpenMC simulations. The predicted and ground truth cross sections in the test data set agree to within 0.119% on average. When the predicted cross sections are used in a multigroup
eigenvalue run, they achieve a mean absolute error in keff of 200 pcm.
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All authors Joshua Nichols, Andrew Osborne, Ben Whewell, Nathan Gibson
Strongly Correlated Electrons
Resolving Spectral Gaps of the Frustrated Spin-1/2 $J_1$-$J_2$ Chain via Dual Spin-Sector Variational Quantum Eigensolver
Sk Saniur Rahaman
Finding excited states and resolving spectral gaps in frustrated
quantum spin systems using Variational
Quantum Eigensolvers is complicated by state-ordering inversions. In the frustrated
spin-1/2 $J_1$-$J_2$ Heisenberg chain, standard single-sector variational
quantum deflation collapses onto intermediate triplet excitation, preventing the isolation of excited singlets and the singlet-singlet gap. To resolve these gaps, a Dual
Spin-Sector Variational
Quantum Eigensolver framework is introduced for near-term
quantum architectures. By partitioning the search across magnetization sectors $S^z=0$ and $S^z=1$, the lowest triplet is resolved as an unconstrained ground state in $S^z=1$ with zero deflation penalty. Leveraging exact global
spin-rotation
symmetry, this state is mapped analytically into $S^z=0$ without ancilla overhead, unlocking dual deflation via shallow compute-uncompute
circuits. Tracking both singlet-triplet and singlet-singlet gaps across coupling ratios $J_2/J_1$ from $0$ to $1$ for chains up to $16$
qubits faithfully captures all critical phenomena: the continuous Berezinskii-Kosterlitz-Thouless transition, exact Majumdar-Ghosh dimer degeneracy, level-ordering inversion in the gapless
liquid phase, and incommensurate spiral excitations.
Circuit depth scaling up to twenty layers provides an overparameterization mechanism overcoming classical local minima in strongly frustrated regimes, reducing relative energy errors below $1\%$ and elevating state fidelities above $97\%$ on $16$
qubits. Projective measurement sampling demonstrates resilience against shot noise, providing a practical blueprint for exploring frustrated
quantum magnetism on near-term
quantum devices.
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Fluid Dynamics
Phase-controlled lateral forcing of droplets on a vibrating bath
Lawrence A. Wilen, Zahra Shamsi, Johnathan Hoggarth et al. (+1 more)
We introduce a method for controlling the lateral motion of droplets bouncing on a vibrated
liquid bath by superimposing a small periodic rocking motion onto the conventional vertical forcing. We show experimentally that the lateral velocity varies linearly with the rocking amplitude $\alpha$ and sinusoidally with its phase $\phi$ relative to the vertical forcing, while the response depends strongly on the droplet's vertical bouncing mode. A time-averaged model of a harmonic bouncer yields a time-averaged lateral force $\overline{F}_x=mg\alpha\sin(\Phi_b+\phi)$, where $\Phi_b$ is the droplet impact phase. Periodic rocking thus provides a simple, programmable means of exerting lateral forces on bouncing droplets, enabling controlled manipulation of passive and active droplets in pilot-wave hydrodynamics.
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All authors Lawrence A. Wilen, Zahra Shamsi, Johnathan Hoggarth, Bauyrzhan K. Primkulov
Fluid Dynamics
SAGE-PINN: A Singularity-free Axisymmetric Geometry-Encoded Physics-Informed Neural Network for Axisymmetric Multiphysics Flow in Cylindrical Coordinates
Raj Maurya, Joyprakash Akhuli and Doyel Pandey
Physics-informed neural networks (PINNs) provide a mesh-free framework for multiphysics modeling, yet fluid flow in axisymmetric cylindrical coordinates remains largely unexplored because the governing operators are singular at the axis and momentum and energy are strongly coupled through advection and buoyancy. We introduce SAGE-PINN, a Singularity-free Axisymmetric Geometry-Encoded Physics-Informed Neural Network for this class of problems. SAGE-PINN removes the coordinate singularity by construction: the parity-preserving variable $s=r^2$ and a modified stream-function representation eliminate explicit $1/r$ terms from the trained residuals and satisfy mass conservation identically. Boundary-fitted analytical ansätze enforce the boundary conditions exactly, while staged momentum-thermal-joint training, residual normalization, Péclet- and Richardson-number continuation, and a physics-scaled drift guard stabilize the coupled optimization. The framework is trained without labelled data, using only physics and boundary constraints, and its predictions are validated against finite-element reference solutions for steady nanofluid flow and heat transfer through a stenosed artery over a wide range of flow and thermal regimes, without case-specific tuning. Mass conservation remains at single-precision round-off, and over the central operating range the axial velocity and temperature are reproduced within $1.6\%$ and $9.1\%$ relative $L_2$ error, respectively. The main deterioration occurs only in the limiting regimes of strongly buoyancy-influenced slow flow and highly advection-dominated heat transfer.
Embedding cylindrical geometry, conservation structure, and boundary physics directly into the network provides a robust route to PINN simulation of axisymmetric multiphysics flows.
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Mathematical Physics · Cross-listed from cs.DS
Color Coding for the Sherrington-Kirkpatrick Model
Alina Harbuzova, Saba Lepsveridze, Mahbod Majid et al. (+1 more)
We give a
polynomial-time
algorithm for approximating the partition function of mean-field mixed $p$-
spin models to arbitrarily high accuracy throughout the second-moment regime. This improves the quasipolynomial-time
algorithm of Bencs, Huang, Lee, Liu, and Regts (arXiv:2507.15616) and answers their open question. In particular, our result covers the entire replica-symmetric regime of the Sherrington–Kirkpatrick model. The
algorithm is deterministic, runs in time
polynomial in $n$ and $1/\varepsilon$, and succeeds for every typical realization of the disorder. Our main technical contribution is a new algorithmic application of color coding to the classical high-temperature expansion introduced by Aizenman, Lebowitz, and Ruelle in their study of fluctuations of the Sherrington–Kirkpatrick partition function. We combine this approach with the zero-freeness established by Bencs et al. (arXiv:2507.15616) to convert additive approximations into multiplicative ones.
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All authors Alina Harbuzova, Saba Lepsveridze, Mahbod Majid, Ankur Moitra
Superconductivity
Correlating DC SQUID Performance with the Location of Trapped Magnetic Flux Using Scanning SQUID Microscopy
Bochao Xu, Ian W. Haygood, Kyle Jackman et al. (+3 more)
Holes in the ground planes of superconducting
circuits, commonly referred to as moats, are known to be an effective means of mitigating the deleterious effects of residual
magnetic flux in superconducting
circuits. Previous studies have utilized scanning SQUID microscopy (SSM) to image the effectiveness of various moat geometries and the maximum
magnetic fields where they no longer prevent
vortices in the ground plane but have not directly correlated the location of flux with
circuit performance measurements. In this study we employ SSM to image the effectiveness of various moat configurations in trapping unwanted flux near a DC SQUID, and when the moats are not effective, the location of
vortices in the ground plane. During the same cooldown, and without disturbing the
circuit, we measure the
circuit performance to directly correlate this with the
vortex location. We show that there are preferential pinning sites for fluxons regardless of moat configuration, demonstrating the utility of the SSM in provide feedback for
circuit layout, modeling, and failure analysis.
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All authors Bochao Xu, Ian W. Haygood, Kyle Jackman, Coenrad J. Fourie, Pete F. Hopkins, Michael L. Schneider
Instrumentation and Methods for Astrophysics
BOLT: Imaging Lightning and Terrestrial Gamma-ray Flashes at the Pierre Auger Observatory
Eric-Teunis de Boone for the Pierre Auger Collaboration
Lightning-related phenomena are known to interact with and influence all detector systems of the Pierre Auger Observatory. Notably, the Surface Detector has recorded unique signals associated with Terrestrial Gamma-ray Flashes (TGFs), which are rare phenomena linked to the initial processes of lightning. Studying TGFs in coincidence with their parent lightning discharges helps to constrain the mechanisms responsible for their production, providing key insights into how these high-energy emissions are generated and evolve within thunderstorms. To image the parent lightning discharges, we are developing the Broadband Observatory of Lightning and TGFs (BOLT), an interferometric lightning mapping array that enhances the unique capabilities of the Observatory by providing high-resolution 3D (spatial and temporal) imaging of lightning propagation. BOLT consists of VHF
radio detectors that have been previously developed for the Auger Engineering
Radio Array, sensitive in the 30 MHz to 80 MHz range. The array is co-located with the Surface Detector to enable direct correlation between lightning activity and candidate TGF signals at the Pierre Auger Observatory. The BOLT array is in commissioning, with successful station firmware modifications to acquire and stream multi-second waveforms.
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Quantum Physics
Dynamically protected erasure qubit via low-frequency charge driving
Ahmed Hajr, Ke Wang, Bingcheng Qing et al. (+6 more)
Dynamical protection via strong driving can enable resilient
quantum processing on imperfect physical hardware. However, the practical utility of such schemes is frequently limited by parasitic processes such as drive-induced dephasing and leakage. Here, we demonstrate that sub-GHz charge driving of superconducting Kerr oscillators (KOs) in the transmon regime circumvents this trade-off, simultaneously activating fast parametric interactions and protecting the encoded
qubit from low-frequency noise. The key is the frequency dependence of the charge sensitivity: using the AC Stark shift as a probe, we find that the KO frequency sensitivity to a charge drive grows quadratically with both drive frequency and amplitude, making the oscillator weakly sensitive to $1/f$ charge noise yet strongly coupled to drives near 1~GHz. Exploiting this, we first demonstrate cooling and reset of the KO in 82~ns, to a residual population below $0.7\%$—lower than its $2.5\%$ steady-state thermal population. Next, using two KOs, we demonstrate logical control and dynamical protection of a dual-rail
qubit with nearly fourfold erasure bias. Finally, using a single end-of-
circuit erasure check, we achieve an error per Clifford of $5.6\times10^{-4}$, which falls to $1.5\times10^{-4}$ after post-selection, with 25~ns gates.
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All authors Ahmed Hajr, Ke Wang, Bingcheng Qing, Akel Hashim, Bibek Bhandari, Dominic Briseño-Colunga, Justin Dressel, Andrew N. Jordan, Irfan Siddiqi
High Energy Astrophysical Phenomena
The NANOGrav 15 yr Dataset: Modified Functional Forms for Profile-Evolution Time Delays in Pulsar Timing Models
Sofia V. Sosa Fiscella, Michael T. Lam, Olivia West et al. (+49 more)
Radio pulsars exhibit frequency-dependent variations in the shape of their pulse profiles that introduce systematic delays in the observed pulse times of arrival (TOA) across an observing band. The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) models these delays using a frequency-dependent (FD) log-
polynomial function whose coefficients are fit to TOA measurements at different
radio frequencies. However, FD parameters are prone to absorbing power from other chromatic effects, such as interstellar dispersion, leading to fitted FD values that overestimate profile
evolution and vary significantly across
datasets. To separate these effects, we introduce two new mathematical formulations of profile
evolution: a monomial expansion (IFD) and a Legendre
polynomial expansion (LIFD). We test these models on observations of 11
pulsars in the NANOGrav 15-year
dataset and on simulated data, and also compare their performance to that of frequency-resolved pulse templates. We find that Legendre-based models generally predict timing delays that more closely replicate the true profile
evolution, reducing the residual offset relative to FD by as much as 85% for the simulated data. Additionally, we find that the choice of profile
evolution model can bias the fitted dispersion model, with FD-based models overestimating the injected DM by 0.013 pc cm^-3 compared to 0.0002 pc cm^-3 for LIFD. These results support the use of Legendre bases to mitigate covariances between chromatic effects, directly benefiting targeted timing studies of these effects and the development of advanced noise-modeling techniques for nanohertz gravitational-wave detection.
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All authors Sofia V. Sosa Fiscella, Michael T. Lam, Olivia West, Ruggero Valdata, Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald, Zaven Arzoumanian, Paul T. Baker, Paul R. Brook, H. Thankful Cromartie, Kathryn Crowter, Megan E. DeCesar, Paul B. Demorest, Timothy Dolch, Elizabeth C. Ferrara, William Fiore, Emmanuel Fonseca, Gabriel E. Freedman, Nate Garver-Daniels, Peter A. Gentile, Joseph Glaser, Deborah C. Good, Jeffrey S. Hazboun, Ross J. Jennings, Megan L. Jones, David L. Kaplan, Matthew Kerr, Duncan R. Lorimer, Jing Luo, Ryan S. Lynch, Alexander McEwen, Maura A. McLaughlin, Natasha McMann, Bradley W. Meyers, Cherry Ng, David J. Nice, Timothy T. Pennucci, Benetge B. P. Perera, Nihan S. Pol, Henri A. Radovan, Scott M. Ransom, Paul S. Ray, Ann Schmiedekamp, Carl Schmiedekamp, Brent J. Shapiro-Albert, Taylor Starkman, Ingrid H. Stairs, Kevin Stovall, Abhimanyu Susobhanan, Joseph K. Swiggum, Haley M. Wahl
Fluid Dynamics
Conditional Flow Matching for Generation of 3D Multi-variable Instantaneous Urban Microclimate Fields
Peng Liu, Shaoxiang Qin, Theodore Potsis et al. (+3 more)
Rapid and accurate prediction of urban wind and temperature fields is important for urban microclimate design and
climate adaptation. Large-eddy simulation (LES) effectively resolves these instantaneous fields, but its application is limited in iterative design of urban microclimate applications due to high computational cost. Existing regressive data-driven models offers quick outputs, but they produce only deterministic point predictions that inherently fail to represent turbulent stochasticity. This paper adopts a novel generative framework of Conditional Flow Matching (CFM) that uses building geometry and mean flow as guidance to generate plausible three-dimensional instantaneous velocity and temperature fields for urban microclimate in seconds. To overcome the GPU
memory bottleneck of pixel space 3D generation, the model operates in parallel on overlapping pixel space through a shared-noise initialization that preserves high spatial continuity of flow structure across the entire domain. Against reference LES data, the CFM surrogate can rapidly and accurately restore the first-order statistics with Normalized Root Mean Square Error (NRMSE) of 2.99% for wind and 1.77% for temperature, second-order
turbulence metrics with NRMSE of 7.17% for wind and 8.84% for temperature, turbulent kinetic energy with NRMSE of 7%, probability density function and vertical profiles in representative locations. Wind engineering application of local gust prediction demonstrate that the speed and accuracy of CFM, supporting the use of generative AI for making
turbulence-aware resilient urban design and
climate adaptation more computationally feasible.
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All authors Peng Liu, Shaoxiang Qin, Theodore Potsis, Lili Ji, Dingyang Geng, Liangzhu Leon Wang
Quantum Physics
Design-Time Conformance Checking for Pulse-Level Quantum Control
Rylan Malarchick
A pulse-level
quantum control program is written against a device whose limits are recorded, if at all, in vendor documentation and source code. A program that exceeds them can be refused at compile time. It can also be accepted and silently altered, or compile and then fail at the board. In the last two cases the experiment runs, and the data does not correspond to the program that was written. We present qconform, a checker that decides whether a pulse program is realizable on a device, given a versioned capability descriptor for that device. Every constraint in a descriptor cites the toolchain observation that established it. The checker is design-time, offline, and deterministic, and it uses no floating point. It reports a verdict, the rules it applied, and a coverage manifest that names what it did not check. We evaluate qconform by differential testing against the QICK and Qblox toolchains, on three QICK board configurations and one Qblox cluster. Over 1263 program runs of 969 distinct programs, the checker accepted no program that a toolchain refuses. On both vendors, a descriptor pinned to one toolchain release accepts programs that older releases refuse. The evaluation also found ten defects in the checker and its descriptors, all fixed.
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Mathematical Physics · Cross-listed from math.DG
Compact toric Einstein four-manifolds
Mingyang Li and Song Sun
This is a continuation of our previous paper on toric gravitational instantons. We develop a systematic theory of compact simply-connected Einstein four-
manifolds with toric
symmetry. Applications include (1) The construction of simply connected positive Einstein four-
manifolds with arbitrarily large $b_2$. Our method is based on local gluing combined with a global virtual counting argument. The key idea involves a particular design of rod structures to enable a gluing construction and restrict possible degenerations. (2) A topological characterization of algebraically special toric Einstein metrics. This uses special
curvature identities for $W^+$ in the toric setting. (3) Uniqueness, up to scaling and isometry, on toric four-
manifolds admitting algebraically special Einstein metrics. The proofs uses a variational study of the Einstein–
Hilbert functional. (4) Diffeomorphism classification of compact toric Einstein four-
manifolds in terms of an improved Hitchin-Thorpe inequality $\chi\geq3|\tau|$. In particular, our construction recovers all the classical toric Einstein metrics, including the Page and Chen–LeBrun–Weber metrics, starting from the round sphere through moduli spaces of conical Einstein metrics.
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Optics
Lattice tuning of optical and mechanical interactions in gold nanodisk metasurfaces
Juan I. Sangiorgio, Diya Xie, Emiliano Cortés et al. (+2 more)
The interaction between neighboring plasmonic nanoparticles can modify their intrinsic optical and mechanical resonances, enabling collective responses to be controlled through the geometry of their arrangement. Periodic arrays therefore provide a means of controlling both electromagnetic and mechanical interactions through a common geometrical parameter, the
lattice pitch. Joint control of these two channels is particularly useful for engineering how coherent mechanical excitations couple to the substrate and are optically read out at the nanoscale. Here, we investigate the optical and mechanical response of gold nanodisk metasurface patches on silica as a function of
lattice pitch using two-color pump-probe spectroscopy and numerical simulations. Optically, simulations show that varying the pitch tunes a Rayleigh anomaly across the broad localized surface plasmon resonance of the nanodisks, producing a strongly asymmetric collective optical response. Pump-probe measurements further reveal a pronounced wavelength dependence of the optical readout of coherent mechanical vibrations. Mechanically, we investigate how coupling between the nanodisks and the substrate depends on the character of the nanodisk resonances. The intrinsic nanodisk modes exhibit markedly different sensitivities to the periodic environment. One remains nearly independent of pitch, whereas another undergoes a pronounced frequency shift mediated by elastic interactions through the substrate. Numerical simulations further reveal that a third nanodisk resonance couples resonantly to a
lattice-selected acoustic mode, giving rise to an avoided crossing and mode hybridization. These distinct mechanical responses are governed by how efficiently each mode couples to near-surface elastic motion in the substrate. These results demonstrate that
lattice periodicity can simultaneously tune optical sensitivity and substrate-mediated mechanical coupling, providing a versatile platform for tailoring nanoscale optomechanical responses in the GHz regime.
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All authors Juan I. Sangiorgio, Diya Xie, Emiliano Cortés, Gustavo Grinblat, Andrea V. Bragas
Fluid Dynamics · Cross-listed from math.NA
A hat function based positive contact discontinuity capturing Boltzmann scheme
Balwinder Singh and S. V. Raghurama Rao
The suitable moments of the Maxwell velocity distribution function recover the conserved variable and flux vectors of the Euler equations of gas dynamics. However, the Maxwellian has infinite support in velocity space, which poses challenges for the numerical analysis of kinetic schemes based on it. Although Maxwellian is a natural choice, it is not the only distribution function that preserves these key moments. In his seminal paper, Perthame (1990) introduced a compactly supported hat function as an alternative. This paper presents the formulation and analysis of a contact discontinuity capturing Boltzmann scheme based on peculiar velocity, constructed using the hat function. Owing to the compact support of the hat function in velocity space, the numerical analysis of the proposed scheme is considerably simplified, allowing for a direct proof of its positivity. The scheme, extended to second-order accuracy without compromising the positivity-preserving property of its first-order counterpart, is tested on several
benchmark inviscid flow problems in one- and two-dimensions.
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Quantum Physics
CV-QKD with noisy coherent states and realistic displacement receivers
L. F. Melo, J. J. Lacerda, John A. Mora Rodríguez et al. (+1 more)
Near-optimal binary coherent state discrimination can be achieved by performing a displacement operation followed by
photon detection. This displacement serves as a control parameter that can be tuned to optimize a chosen figure of merit, which makes such receivers attractive beyond state discrimination. Here, we turn this discrimination strategy into a complete continuous-variable
quantum key distribution (CV-QKD) protocol based on a binary alphabet of noisy coherent states (displaced thermal states) and displacement receivers. We develop a strategy for estimating the channel parameters with the same displacement receiver, since standard quadrature-based methods do not apply. While a key map can be obtained with on/off detection, we demonstrate that reliable parameter estimation requires
photon-number-resolving detection with sufficient resolution. Finite detector resolution underestimates the observed noise and constitutes a potential security loophole, which we quantify and show to be mitigable with modest resolution. We analyze the protocol's security against entangling-cloner collective attacks in the asymptotic regime, for both direct and reverse reconciliation, and show that it can generate secret keys even under noisy state preparation and imperfect detection. Interestingly, we find that the displacement that minimizes the discrimination error probability also performs well for key generation, thus providing a useful and analytically tractable tuning strategy for the CV-QKD receiver. We evaluate the protocol's performance over a range of transmittances and noise parameters, showing that the displacement receiver can outperform conventional homodyne detection in experimentally relevant regimes. Together, our results point to displacement receivers as a promising alternative for CV-QKD implementations.
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All authors L. F. Melo, J. J. Lacerda, John A. Mora Rodríguez, Alexandre B. Tacla
Materials Science
Magnetic Order from First-Principles Linear Response
Yaroslav Zhumagulov, Emiliano Cruz-Aranda, Denis Kochan et al. (+1 more)
The
magnetic order of a material is usually predicted by comparing the energies of trial
spin configurations, a strategy that cannot reach orders outside the chosen set and rapidly becomes intractable as the number of
magnetic sites grows. We show that the order can instead be identified directly from the paramagnetic state: the leading eigenmodes of its self-consistent magnetization response, obtained with
spin–orbit coupling at arbitrary wavevector within the primitive
cell, determine both the ordering wavevector and the pattern of moments, with no candidate configurations. With no prior assumption about the order, the method correctly reproduces the G-type stacking with out-of-plane moments in the altermagnet candidate KV$_2$Se$_2$O, the $3{:}1$ ferrimagnetic tetrahedra of Cu$_2$OSeO$_3$, and the incommensurate spiral instability of monolayer NiI$_2$. Our results establish the response of the paramagnetic state as a predictive and unbiased route to assessing
magnetic order, enabling systematic searches for complex magnets.
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All authors Yaroslav Zhumagulov, Emiliano Cruz-Aranda, Denis Kochan, Oleg V. Yazyev
Optics
Lithography-free in situ photonic inverse design
Yiqi Zhao, Martin M. Stein, Fan O. Wu et al. (+6 more)
Inverse design has emerged as a powerful engineering approach in nanophotonics, leading to devices with unintuitive designs that extend beyond human intuition. However, inverse-designed devices have been limited by costly and time-consuming fabrication iterations, simulation-reality gaps, and the fixed nature of etched devices. Separately, programmable photonics has emerged as an approach to modify the function of photonic devices after fabrication; recent free-form programmable-photonic devices have been scaled to have many degrees of freedom, in principle providing platforms well-suited for programmably realizing inverse design of conventional linear-photonic functions. Here we demonstrate in situ training of a single free-form programmable-photonic device to achieve a variety of functions that each would have been implemented in separate devices in the conventional inverse-design paradigm. Our platform is a thin-film lithium niobate slab waveguide with full programmability over the refractive index of the two-dimensional slab, with a tuning range of up to 1.7e-3 for each of the 10^4 effective pixels. We demonstrate 4 x 4 Gaussian-mode permutations, 4 x 4 Hermite-Gaussian mode demultiplexing, and 8 x 8 Gaussian-mode permutations, with maximum crosstalk of -27dB, -13dB, and -13dB, as well as 4 x 4
matrix-vector multiplication. We further demonstrate engineering of spectral responses, showcasing both wavelength-independent operation throughout the 1500-1600 nm window and wavelength demultiplexing between 1310 nm and 1550 nm, with maximum crosstalk of -8.4dB. Since the device is reconfigured for each function after fabrication, the investment in device fabrication, characterization, and calibration can be amortized across the various photonic functions the device is ultimately used for.
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All authors Yiqi Zhao, Martin M. Stein, Fan O. Wu, Ryotatsu Yanagimoto, Benjamin A. Ash, Mandar M. Sohoni, Logan G. Wright, Tatsuhiro Onodera, Peter L. McMahon
Materials Science
Interface Doping Mechanism of p-Type Conductivity in Mist-CVD-Grown LiGa5O8 Thin Films
Dong Su Yu, Shashu Tomar, Zixiu Huang et al. (+7 more)
LiGa5O8 is an ultrawide-bandgap oxide
semiconductor that has recently been experimentally demonstrated to exhibit p-type conductivity; however, the origin of the observed p-type transport remains poorly understood. In this work, we investigate the origin of p-type conductivity by systematically comparing the electrical, compositional, and structural properties of mist chemical vapor deposition (mist-CVD) grown LiGa5O8 thin films exhibiting either p-type conductivity or insulating behavior under different growth conditions. The p-conductive films exhibit room-temperature hole concentrations on the order of 10^18 cm^-3. The emergence of p-type conductivity is found to be strongly correlated with the elemental composition of the films, with X-ray photoelectron spectroscopy (XPS) revealing a reduced Li/Ga ratio in the p-conductive films, indicative of a Li-deficient composition. Raman spectroscopy and X-ray diffraction (XRD) further reveal signatures of e̱ta}-Ga2O3 in the p-conductive films that are absent in the insulating films, demonstrating the formation of a secondary e̱ta}-Ga2O3 phase. Considering the reported type-II band
alignment between LiGa5O8 and e̱ta}-Ga2O3, together with theoretically predicted acceptor levels associated Li vacancies (V_Li) in LiGa5O8, the formation of mixed LiGa5O8/e̱ta}-Ga2O3 phases is proposed to facilitate electron transfer from the e̱ta}-Ga2O3 valence band to acceptor states in LiGa5O8, thereby promoting hole accumulation and p-type conductivity.
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All authors Dong Su Yu, Shashu Tomar, Zixiu Huang, Kaitian Zhang, Sarker Md. Sadman, Vijay Gopal Thirupakuzi Vangipuram, Ye Lin, Walter R. L. Lambrecht, Roberto C. Myers, Hongping Zhao
High Energy Astrophysical Phenomena
Deep Learning for Cherenkov Astronomy: Performance of GammaLearn on LST-1
G. Grolleron, C. Plard, M. Dell'aiera et al. (+5 more)
The Cherenkov
Telescope Array Observatory (CTAO) marks the next generation of Imaging Atmospheric Cherenkov
Telescopes (IACTs), offering a sensitivity improvement of up to a factor of 10 over current instruments. Its first prototype, the Large-Sized
Telescope (LST-1), is already in operation at the Roque de los Muchachos Observatory in La Palma, Spain. Deep learning methods have shown significant promise in reconstructing key properties of incident particles, such as energy, arrival direction, and type, using simulated data. Unlike traditional approaches that rely on simplified image shape parameters, deep learning can exploit the full temporal and charge information of the recorded events, providing enhanced performance, particularly at low energies (~20 GeV) accessible by LST-1. This capability is especially valuable for observing distant extragalactic sources like Active Galactic
Nuclei, which are key to probing fundamental physics and cosmology. In this work, by producing sensitivity curves, we evaluate the performance of GammaLearn, a deep learning framework tailored for IACT data analysis, by comparing it to the standard analysis used with LST-1 and applying it to real observational data from LST-1.
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All authors G. Grolleron, C. Plard, M. Dell'aiera, T. François, V. Pollet, J. Talpaert, S. Caroff, T. Vuillaume on behalf of the CTAO-LST Project
Mesoscale and Nanoscale Physics
Pseudospin Hall Transport Induced by Berry Curvature
Qinhui Jiang, Jidong Song, Qingyang Mo et al. (+4 more)
Pseudospin-1 Dirac systems exhibit unique physics distinct from conventional Dirac cones, such as flat-band crossings and non-Abelian characteristics, yet their topological transport properties have remained largely untapped in passive, time-reversal-invariant settings. Here we uncover an in-plane polarity of the Berry-
curvature texture in a pseudospin-1 Dirac
Hamiltonian, a previously unexplored
geometric degree of freedom encoded in the sign-resolved distribution of Berry
curvature despite zero net Berry flux, and reveal a new mechanics where Berry
curvature induce pseudospin Hall behaviors in a system. We show that the oriented coupling between this momentum-space polarity and a real-space mass gradient governs a
geometric selection rule that dictates the emergence of gapless pseudospin Hall modes. By engineering the intracell couplings of a four-site planar
lattice, we independently program the Berry-
curvature polarity and the spatial mass gradient without altering the host
lattice symmetry. Acoustic experiments directly confirm this directional selection rule: reversing the mass gradient closes or reopens the dispersive gap, while pseudospin-selective source excitation launches counterpropagating pseudospin branches along arbitrary prescribed axes. Our work establishes
quantum geometric polarity as a versatile tool for reconfigurable wave routing, sensing, and high-capacity
quantum applications.
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All authors Qinhui Jiang, Jidong Song, Qingyang Mo, Bo Li, Dongyi Wang, Shuang Zhang, Mengyao Li