Instrumentation and Methods for Astrophysics · Cross-listed from cs.LG
Safe Domain Adaptation for Physics: Overcoming Nuisances, Label Shifts, and Simulation Priors
Ivan Kharuk
Domain adaptation is widely used to make neural networks trained on simulations applicable to experimental data. Its premise is that the two domains differ only in nuisances, and that the quantity of interest is distributed identically in both. In physics neither assumption holds: simulations can be wrong about the physics, and the distribution of the target quantity - an energy spectrum, a
redshift distribution - is often the measurement itself. We study the consequences of such mismatches on a toy air-shower
benchmark in which a detector-response nuisance, a physical simulation shift, and an energy-spectrum shift can be switched on separately or together. Standard
adversarial adaptation handles the conditional shifts, but once the two spectra differ it aligns them, replacing an uncontrolled bias by one anchored on the simulation prior. We present adaptive domain adaptation, which reweights the simulated events so as to focus domain adaptation on the genuine physical mismatch alone. Since the predicted spectrum depends on model training configuration, we provide a label-free model selection rule for selecting the near-the-best operation point.
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Materials Science
Breaking the mutual exclusivity between metallicity and ferroelectricity in a non-polar covalent semiconductor via orbital selective doping
Hui Li, Yunfan Yang, Junquan Huang et al. (+18 more)
The mutual exclusion of ferroelectricity and metallic conductivity is a long-standing tenet because itinerant electrons screen long-range Coulomb forces that stabilize the bulk polar order. Here, we break this paradigm by heavily doping a non-polar covalent
semiconductor of cubic silicon carbide (3C-SiC) with nitrogen. This introduces heavy electron doping, inducing
metallicity and driving a structural transition from the non-polar F-43m to the polar R3m
symmetry via the pseudo-Jahn-Teller effect. Remarkably, we provide direct, atomic-scale visualization of about 180{\deg} polarization reversal under an external voltage bias in a ferroelectric metal. The strongly directional character of antibonding orbitals occupied by conduction electrons prevents them from screening the local Si-C polarization, resulting in the coexistence of
metallicity and ferroelectricity. Ferroelectric tunnel junctions demonstrate nonvolatile
memory properties with a well-defined high-resistance state (HRS) and low-resistance state (LRS), an ultrahigh response speed (~50 ns), an ultralow operating voltage (1 V), an endurance exceeding 85927 cycles, and a projected retention time of 100 years. Our results provide a novel strategy for pioneering ferroelectricity in a metal, a new ferroelectric metal platform for exploring exotic properties, and a ferroelectric device with high performance that meets the requirements for low consumption and high-speed non-volatile devices.
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All authors Hui Li, Yunfan Yang, Junquan Huang, Yukun Feng, Guobin Wang, Qinci Wu, Jun Deng, Zhaolong Liu, Subi Du, Dongliang Gong, Zaihui Shen, Anmin Nie, Yang Xu, Junwei Yang, Zesheng Zhang, Huaping Song, Jiangang Guo, Wenjun Wang, Hailin Peng, Yongjun Tian, Xiaolong Chen
High Energy Physics (Theory)
Analytic Continuation of Conformal Integrals in Momentum Space
Jonathan Gräfe, Prashanth Raman and Denis Werth
Conformal
symmetry strongly constrains correlation functions. In momentum space, the conformal Ward identities are solved for three-point functions by integrals of a product of three Bessel functions ("triple-$K$ integrals"); more generally, integrals of this type ("multiple-$K$ integrals"), serve as the building blocks of a wide class of higher-point conformal correlators. These integrals belong to the class of generalised hypergeometric functions, and while their series representations are known in principle, none converges throughout the entire physical region of kinematic space selected by momentum conservation. In this work, we construct series representations adapted to exactly this physical domain. Using the method of brackets, which turns the evaluation of definite integrals into solving a linear system of algebraic equations, we derive various Lauricella-type series representations for multiple-$K$ integrals. For triple-$K$ integrals, conventionally expressed in terms of the Appell $F_4$ function whose series converges only outside the triangle-inequality region, we find instead a compact, two-branch series that converges throughout the entire physical region and for arbitrary scaling dimensions. We then extend this construction to general multiple-$K$ integrals: by introducing a new set of kinematic variables, we build an iterative series representation that converges for all physical kinematic configurations.
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Atomic Physics
Ultrafast and high resolution spatial light modulation for cold atoms
Alexander Dennisovich Deters, Yanfei Li, Alexander Douglas et al. (+2 more)
Programmable arrays of ultracold atoms are a leading platform for
quantum computation and simulation, enabling state-of-the-art implementations of
quantum error correction, and analog simulations of Hubbard models that address open problems in condensed matter physics. In these systems, all local control is mediated through precisely shaped optical fields, and so the challenge of managing many-body
quantum states becomes an exercise in optical design. In particular, one wishes for fast, flexible control with low disorder and heating, and access to large arrays with many atoms. An ideal optical system therefore must generate arbitrary patterns with high spatial resolution and low disorder, and alter these patterns on a timescale that is faster than the relevant atomic dynamics. Here, we present an optical system that is comparable to previous approaches in scale, while advancing all other axes. We demonstrate arbitrary pattern generation with $10^{-3}$ intensity resolution, a frame rate of $>84$ MFPS (megaframes per second), and a spatial resolution of $83 \times 52$ beam waists (with $11 \times 52$ waists accessible via a single $40$ GHz electro-optic modulator). These capabilities unlock a new class of experiments. We develop and numerically validate a scheme for fully programmable Hubbard models, with time-dependent control over local chemical potentials, tunneling amplitudes, on-site interactions, and patterns of artificial
magnetic flux. The same architecture performs fast, arbitrary permutations of tweezers in 2D, decoupling optical constraints from the design of high-rate error-correcting codes.
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All authors Alexander Dennisovich Deters, Yanfei Li, Alexander Douglas, Markus Greiner, Aaron W. Young
Quantum Physics
Kean Chen and Qisheng Wang
In this paper, we provide
estimators for
quantum Rényi and Tsallis
entropies with nearly optimal sample complexity. Specifically, for order $\alpha$, dimension $d$, and additive error $\varepsilon$, 1. For $0 < \alpha < 1$, the sample complexity is $O(d^{1+1/\alpha}/\varepsilon^{1/\alpha} + d^{1/\alpha-1}/\varepsilon^{2})$ for Rényi
entropy and $O(d^{1+1/\alpha}/\varepsilon^{1/\alpha} + d^{2-2\alpha}/\varepsilon^2)$ for Tsallis
entropy. In particular, for $0 < \alpha \leq 1/2$, the sample complexity for both
entropies is $O(d^{1+1/\alpha}/\varepsilon^{1/\alpha})$. 2. For non-integer $\alpha > 1$, the sample complexity is $O(d^2/\varepsilon^{1/\alpha} + d^{1-1/\alpha}/\varepsilon^2)$ for Rényi
entropy. Our upper bounds improve the
quantum Rényi
entropy estimators due to Acharya, Issa, Shende, and Wagner (2017) and the
quantum Tsallis
entropy estimators due to Chen, Liu, and Wang (2026), and match the lower bounds recently established by Wang (2026).
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Disordered Systems and Neural Networks
Quantum Geometric Tensor Preconditioning for Stable Training of Recurrent Neural Quantum States
Adil Attar, Amine M. Aboussalah and Mohamed Hibat-Allah
Neural
Quantum States (NQS) provide a powerful neural network-based variational framework for representing many-body wave functions and solving for ground states. Recurrent Neural Networks (RNNs) are particularly promising owing to their relatively low computational cost and their autoregressive property, which enables perfect sampling. Recently, RNNs have been reported to be unstable under
curvature-based optimizers such as the minimum-step
stochastic reconfiguration (minSR) method. In this paper, we address this perceived limitation and show that minSR can be stabilized through simple regularization techniques, enabling robust training of RNN-based NQS with only a few samples. Our approach outperforms the Adam optimizer on the one-dimensional transverse-field Ising model and the one-dimensional cluster state, and provides competitive results on the two-dimensional Heisenberg and $J_1-J_2$ models. This work offers a promising pathway for using modern optimization techniques with autoregressive NQS to address open questions in
quantum simulation.
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Applied Physics
Achieving Long Retention in Area-Dependent Resistive Memory with Phase-Separated Amorphous Tantalum Oxide
Sangyong Lee, Anton V. Ievlev, Dongjae Shin et al. (+2 more)
Resistive random-access
memory (ReRAM) is a promising future nonvolatile
memory technology. Most ReRAM exhibit a fundamental tradeoff: filament-type ReRAM provides long data retention but suffers from poor uniformity and high switching current, whereas nonfilamentary ReRAM shows lower-current, area-dependent switching but generally poor retention. No two-terminal device has been able to overcome this tradeoff. In this work, we present a two-terminal Ta2O5/ TaOX resistive
memory cell that achieves both nonfilamentary switching and long retention. Electrical measurements and composition depth profile show that the switching is not confined to a single filament but is instead uniform across the entire switching region. Despite the nonfilamentary switching, this device can retain information for over 22 hours at 190 {\deg}C, which is comparable to the best filamentary devices. We propose that this long retention arises from composition phase separation in amorphous tantalum
oxide. Our work shows that the fundamental tradeoff between information retention and switching uniformity can be overcome, and thereby provides a pathway toward more uniform and reliable
oxide memory devices.
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All authors Sangyong Lee, Anton V. Ievlev, Dongjae Shin, Jingxian Li, Yiyang Li
Medical Physics · Cross-listed from eess.IV
Primitive Representation Learning for Unsupervised Dynamic Contrast Enhanced MRI Reconstruction
Veronika Spieker, Wenqi Huang, Cemre Ariyurek et al. (+5 more)
Reliable quantitative analysis of dynamic contrast-enhanced MRI requires high-quality spatiotemporal reconstructions at high undersampling rates. Scan-specific reconstructions using Gaussian and Gabor primitives have shown promising results without the need for large training
datasets, but have not addressed the additional dimension of dynamic contrast. We propose a multi-dimensional, primitive based framework for dynamic contrast-enhanced MRI reconstruction that disentangles the underlying anatomy, the dynamic contrast enhancement, and residual motion into separate temporal basis functions, thereby enabling a geometrical interpretation of the representation. We show that this architecture achieves performance competitive with conventional reconstruction methods, both in reconstruction quality and in the accuracy of extracted aorta and kidney enhancement curves. The modular tier design extends naturally to additional dynamic factors and higher acceleration rates. Code available at https://github.com/compai-lab/2026-GaborDCE-spieker.
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All authors Veronika Spieker, Wenqi Huang, Cemre Ariyurek, Liam Timms, Daniel Rueckert, Onur Afacan, Julia A. Schnabel, Sila Kurugol
Quantum Physics
A Complete Classification of Complex Hadamard Matrices of Order Six
Mateo Cárdenes Wuttig and Joseph Tindall
Complex Hadamard
matrices encode perfectly balanced
unitary transformations. Their classification is complete through order five, but order six – the first dimension in which several continuous families coexist with an isolated solution – has remained open for decades. Here, we give a complete and exact finite-incidence classification of order-six complex Hadamard
matrices up to standard equivalence. We supply the global step missing from Szöllősi's dilation method, which allows us to prove an even stronger version of his
conjecture: every complex Hadamard
matrix of order six can be recovered algebraically from a suitable, dephased $3 \times 3$ corner defined by four initial phases. We then describe the geometry of the reconstruction from these phases and show that, except for Tao's isolated
matrix and a single explicit Karlsson
matrix, every class admits a representative obtained by solving one quadratic and one cubic equation in both the horizontal and vertical directions. Our work resolves the classification problem and provides a rigorous framework for further investigating order-six Hadamards, with applications to balanced six-mode interferometers and the study of mutually unbiased bases.
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Mesoscale and Nanoscale Physics
Electronic Reconstruction at the Quasicrystal-Moiré Crossover in Twisted Bilayer Graphene
Kuo-En Chang, Aitor Garcia-Ruiz, Ta-Lei Chou et al. (+11 more)
Large twist angles in twisted
bilayer graphene are widely expected to be electronically trivial, with negligible interlayer coupling and no electronic reconstruction, in contrast to the rich moiré-driven band reconstruction and correlated physics that emerge at small twist angles. Here, we show that this paradigm breaks down near a twist angle of 29{\deg}, where the system crosses over between quasicrystalline and commensurate order. Atomic-resolution transmission electron microscopy directly reveals the coexistence of near-dodecagonal quasicrystalline
symmetry and emerging moiré periodicity, indicating an intermediate, nonperiodic structural regime. Magnetotransport measurements uncover strong interlayer hybridization mediated by Umklapp scattering, manifested by magneto-intersubband oscillations and a highly unconventional Landau-level spectrum. Remarkably, the Landau-level degeneracy evolves from 4- to 12-fold with increasing temperature, a behavior incompatible with two decoupled
graphene monolayers. These findings establish large-angle twisted
bilayer graphene as a platform where quasiperiodic
symmetry fundamentally reshapes low-energy electronic states beyond the conventional moiré framework.
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All authors Kuo-En Chang, Aitor Garcia-Ruiz, Ta-Lei Chou, Yen-Ting Liu, Sheng-Chin Ho, Yu-Chiang Hsieh, Ching-Hua Kao, Chiu-Hua Huang, Ying-Mei Yang, Kenji Watanabe, Takashi Taniguchi, Ming-Wen Chu, Ming-Hao Liu, Tse-Ming Chen
Instrumentation and Methods for Astrophysics
Variable Star Polarimetry with PICSARR-2
Daniel V. Cotton, Jeremy Bailey, Logan Barrett et al. (+3 more)
We describe the upgraded Polarimeter using Imaging CMOS Sensor and Rotating Retarder 2 (PICSARR-2), describe its applications, and characterize its performance for
stellar polarimetry on a 36-inch and 14-inch
telescope. On the larger
telescope in the SDSS $g^\prime$, $r^\prime$ and $i^\prime$ filters a precision of $\sigma_p=$ 5.7 ppm on bright
stars is recorded using a fast modulation rate corresponding to frame exposures of 12 ms; accounting for the internal errors in the individual observations gives a limiting precision of $e_p=$ 1.3 ppm. Longer frame rates are required for
stars with $m > 5$, but the recorded errors only underperform a
photon shot noise derived extrapolation for $m > 8$, when even longer frame exposures are required.
Stars as faint as $m = 11$ were observed. The position angle precision is measured as 0.0845 degrees, and there is very good agreement between observations made by both the PICSARR-2 and HIPPI-2 polarimeters. On the smaller
telescope the instrument's performance approaches similar levels, and there is good cross-platform stability in the observation of standard
stars. PICSARR-2 is therefore an excellent instrument to explore
stellar variability due to a range of phenomena; examples from our ongoing pulsating
star campaign and other variable
star programs are presented.
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All authors Daniel V. Cotton, Jeremy Bailey, Logan Barrett, Glenn Henderson, Kim Sumagang, Eric C. Haase
Mesoscale and Nanoscale Physics
Long-time fermionic quantum transport with controlled full-state error using an adaptive reservoir-mode window
Mikhail Umanskii, Nataliya Arefyeva, Georgy Sultanov et al. (+2 more)
Real-time simulations of interacting nanostructures coupled to fermionic reservoirs can require a growing number of environmental degrees of freedom to retain long-lived correlations. We introduce tape-recorder coarse graining, which reorganizes each noninteracting lead into incoming, active, and outgoing modes. The device is propagated with the active modes, while outgoing modes are stochastically sampled and removed once their remaining integrated coupling falls below a prescribed threshold. For each outgoing-mode truncation, we derive a nonperturbative upper bound on the infidelity between the exact and truncated full device-reservoir states over any prescribed finite interval. The bound depends on the mode's remaining coupling weight and finite-interval response factors. Numerically, the active-mode count saturates in time at fixed relative threshold and grows logarithmically as the threshold is reduced. We
benchmark the method on a two-site
quantum point contact at zero temperature and maximal bias. For Lorentzian reservoirs, the dynamics agrees with converged HEOM calculations and the steady-state current with the Landauer-Büttiker result. For flat-band reservoirs with algebraically decaying correlations, it agrees with direct Schrödinger
evolution before finite-size recurrences and reproduces the Landauer-Büttiker stationary current, while finite exponential HEOM decompositions remain unconverged. For interacting contacts, the method yields Coulomb-blockade peak splitting. In the noninteracting driven limit, it agrees with an exact Floquet Green-function calculation and reproduces coherent current suppression under periodic driving, which persists at finite Coulomb repulsion. Together, these
benchmarks show that tape-recorder coarse graining enables practical long-time simulations of the full device-reservoir state in interacting fermionic transport.
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All authors Mikhail Umanskii, Nataliya Arefyeva, Georgy Sultanov, Alexey Rubtsov, Evgeny Polyakov
High Energy Astrophysical Phenomena
Generalized Non-linear Bayesian Pulsar Timing with Enterprise
Andrew R. Kaiser, Jeffrey S. Hazboun, Maura A. McLaughlin et al. (+56 more)
In this study, we use the
Bayesian methods in the Enterprise package to examine the fully general parameterization of
pulsar timing models in tandem with noise. We investigate four
pulsars, PSR J1600$-$3053, PSR J2043+1711, PSR J0740+6620, and PSR J1640+2224, through the lens of
Bayesian timing. These four are selected as they are well-studied, but exhibit interesting characteristics under the lens of
Bayesian timing. Our new
pulsar mass constraints (medians and 68% confidence intervals) for our fully general non-linear
Bayesian timing models are $m_{\mathrm{p}}=1.6(1)~\mathrm{M}_{\odot}$ for PSR J2043+1711 and $m_{\mathrm{p}}=2.3^{+0.9}_{-0.7}~\mathrm{M}_{\odot}$ for PSR J1600$-$3053 both using the NANOGrav 12.5-yr data release, and $m_{\mathrm{p}}=2.06(6)~\mathrm{M}_{\odot}$ for PSR J0740+6620 using the data from Fonseca, et al., 2021. We investigate the effects on placing physical priors on timing model parameters, including restricting the upper limit on the
pulsar mass for PSR J1640+2224, which has a mass often estimated to be greater than $3~\mathrm{M}_{\odot}$. We find \ark{that restricting the allowed sampling space of the
pulsar mass for PSR J1640+2224 to} $m_{\mathrm{p}}<3~\mathrm{M}_{\odot}$ results in a
pulsar mass of $m_{\mathrm{p}}=2.2(5)~\mathrm{M}_{\odot}$ for PSR J1640+2224 using the NANOGrav 12.5-yr data release. For the first time, we find evidence for intrinsic red noise in PSR J2043+1711. We show how fully general
Bayesian timing can better model the interplay of the intrinsic noise and the timing parameters.
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All authors Andrew R. Kaiser, Jeffrey S. Hazboun, Maura A. McLaughlin, H. Thankful Cromartie, Emmanuel Fonseca, Joseph Simon, Stephen R. Taylor, Michele Vallisneri, Sarah J. Vigeland, Zaven Arzoumanian, Paul T. Baker, Harsha Blumer, Paul R. Brook, Ismael Cognard, Megan E. DeCesar, Paul B. Demorest, Timothy Dolch, F. Adam Dong, Justin A. Ellis, Robert D. Ferdman, Elizabeth C. Ferrara, William Fiore, Nate Garver-Daniels, Peter A. Gentile, Deborah C. Good, Lucas Guillemot, Ross J. Jennings, Megan L. Jones, David L. Kaplan, Victoria M. Kaspi, Matthew Kerr, Aida Yu. Kirichenko, Michael T. Lam, Duncan R. Lorimer, Jing Luo, Ryan S. Lynch, Alexander McEwen, James W. McKee, Natasha McMann, Bradley W. Meyers, Arun Naidu, Cherry Ng, David J. Nice, Aditya Parthasarathy, Timothy T. Pennucci, Benetge B. P. Perera, Nihan S. Pol, Henri A. Radovan, Scott M. Ransom, Paul S. Ray, Brent J. Shapiro-Albert, Renée Spiewak, Ingrid H. Stairs, Kevin Stovall, Joseph K. Swiggum, Chia Min Tan, Shriharsh P. Tendulkar, Haley M. Wahl, WeiWei Zhu
Mathematical Physics · Cross-listed from math.PR
Supercritical sharpness for the random cluster representation of real-valued spin models
Trishen S. Gunaratnam, Dmitry Krachun, Christoforos Panagiotis et al. (+2 more)
We study the supercritical regime $\beta>\beta_c$ of a large family of real-valued
spin models on $\mathbb Z^d$, including the Blume-Capel and $P(\varphi)$ models. We consider their random cluster representations and prove that they are well-behaved, in the sense that local uniqueness of macroscopic clusters occurs with high probability, uniformly in the boundary conditions. This implies, among other things, a surface-order exponential bound for the (lower) large deviations of the empirical magnetisation. These results were previously known only in the cases of the Ising and $\varphi^4$ models.
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All authors Trishen S. Gunaratnam, Dmitry Krachun, Christoforos Panagiotis, Romain Panis, Franco Severo
Quantum Physics
CLOPS: Benchmarking System Speed at Utility Scale
Andrew Wack
As
quantum processors scale to hundreds of
qubits, execution speed is a critical performance dimension alongside scale and quality. While substantial progress has been made in benchmarking
circuit fidelity, existing speed metrics often fail to reflect the sustained, end-to-end throughput experienced by users running utility-scale workloads. This shortfall is especially pronounced for layered, parameterized
circuits executed repeatedly within classical-
quantum workflows, such as variational
algorithms and error-mitigated simulations. In this work, we formalize CLOPS_h (
Circuit Layer Operations Per Second) as a holistic speed
benchmark defined over layered, hardware-aware
circuits. CLOPS_h measures the sustained rate at which the system executes physical layers, parallel slices of
qubit-disjoint two-
qubit gates separated by synchronization barriers. Because each such layer is one time slice of an N-
qubit circuit, this rate maps directly to the execution rate of layered $N$-
qubit circuits, connecting CLOPS_h to published device capability claims, and to the device-level throughput ceiling we formalize as Max
Circuits Per Second (MCPS). CLOPS_h is obtained under layer-fidelity operating conditions, binding the speed measurement to an independently verified quality envelope, and it shares its layer decomposition with scalable layer-fidelity (LF) quality
benchmarks, enabling coherent interpretation of speed and quality without conflating the two.
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Mesoscale and Nanoscale Physics
Dynamics of Majorana tetron qubits under quasiparticle poisoning
Sauri Bhattacharyya and Bernard van Heck
We study the dissipative dynamics of a Majorana tetron
qubit in the presence of extrinsic quasiparticle poisoning due to the coupling to external leads. From the Bloch-Redfield equation describing a finite-size topological
superconductor hosting four Majorana zero modes and tunnel-coupled to fermionic reservoirs, we recover analytical expressions for the decoherence rate of a Majorana
qubit at arbitrary values of the charging energy. The analysis shows that the exponential suppression of the decoherence rate is gradually removed by the energy splitting of the
qubit states. These results can be useful to understand time-domain experimental data in Majorana
qubit prototypes.
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Nuclear Theory
Confining density functional approach to the QCD phase diagram at low temperatures and thermal twin stars
David Blaschke and Oleksii Ivanytskyi
We present a density functional-based equation of state for warm, dense
nuclear matter with a transition to deconfined
quark matter for applications to simulations of
supernova explosions and
neutron star mergers, but also for the cosmological
evolution of Q-balls. For the
quark matter equation of state, we employ a recently developed confining density functional approach while
nuclear matter is described within a relativistic density functional model of the DD2 class. The phase transition is obtained by a Maxwell construction at constant
entropy per
baryon. We discuss the solutions of TOV equations for isentropic hybrid
stars for two hybrid equation of state models: DDf-SFM without and DD2-$\chi$CDF with color superconductivity. We find that at finite temperatures above a critical value of
entropy per
baryon sequences of disconnected third family branches ("thermal twin
stars") may appear for the DDf-SFM model, while they are absent for the color superconducting model and at $T=0$. We discuss the relation of this critical
entropy per
baryon to the Seidov criterion of gravitational instability for $T=0$ and find that it is a good guide. We suggest that the presence of thermal twin
stars may be regarded as an indicator for the core-collapse
supernova explodability of massive blue supergiant
stars with
solar metallicity and thus serve as a new criterion for the reliability of hybrid equation of state models. By this argument, strong color superconductivity shall be excluded and it remains to be shown whether models with moderate diquark pairing could fulfill the thermal twin constraint. For the case of symmetric matter, we compare the resulting hybrid EOS with the flow constraint by Danielewicz et al. and find a a sensitivity of the onset density for deconfinement to the presence or absence of color superconductivity.
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Solar and Stellar Astrophysics
The instantaneous mass accretion rate of novae in quiescence: - an archival ultraviolet optical spectral analysis
Patrick Godon, Edward M. Sion, Tim Naylor et al. (+1 more)
Based on archival spectra, we derive the quiescent instantaneous mass transfer rates in novae using synthetic disk spectra generated with tlusty,
Gaia parallax-derived distances, and updated color excess values. Our results for nine novae, based on ultraviolet spectra and on a number of optical spectra, yield mass
accretion rates that are higher than those derived from simple integration of the UV and optical luminosity. HR Del, 20 years after its eruption, has a mass transfer rate of $4\times 10^{-7}M_\odot$/yr, and is likely burning the accreting H-rich material. V842 Cen, thought to be a low mass transfer system, has a comparable mass
accretion rate. For both novae, the enhanced mass transfer must be self-sustained by a feedback loop. RR Pic, with $\dot{M}\approx 3\times 10^{-8}M_\odot$/yr, is better fitted with an
accretion disk where the outer region is heated up to 12,000 K, in agreement with H and He emission lines coming from the outer disk and a large emission region on the leading side of the disk. Such a heated disk also gives a good fit to the spectra of CP Lac, and DI Lac with $\dot{M}\sim4.5$ and $9\times 10^{-9}M_\odot$/yr. V1974 Cyg and V533 Her, with an
accretion rate of $\sim 3\times 10^{-9}M_\odot$/yr, have a rather flat spectrum. V446 Her and BK Lyn, caught in a state of low
accretion, have the lowest mass
accretion rates with $\dot{M} \sim 10^{-9}$ and $\sim 10^{-10}M_\odot$/yr respectively. The higher mass transfer rate systems, with $\dot{M}\approx \sim 10^{-7}M_\odot$/yr, agree with the standard disk model; the remaining systems are better fitted when the outer disk is heated to $\sim 12,000$~K. We suggest that irradiation from the heated WD, inner disk, together with
tidal interaction, the bright spot, and material overflowing the disk edge, can increase the temperature of the outer disk.
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All authors Patrick Godon, Edward M. Sion, Tim Naylor, Frederick A. Ringwald
Medical Physics · Cross-listed from eess.IV
Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors
Mahdi Saberi, Yaşar Utku Alçalar, Merve Gülle et al. (+2 more)
MRI reconstruction methods for undersampled k-space data naturally utilize complex-valued measurements. Parallel developments in sparse phase
retrieval have shown that magnitude-only measurements may provide complementary information for signal recovery. However, their use in MRI reconstruction remains largely unexplored, due to lack of practical settings where informative magnitude measurements can be obtained without additional scan time. In this work, we investigate the use of auxiliary k-space magnitude information for accelerated steady-state dynamic MRI reconstruction, and demonstrate strong consistency of k-space magnitudes across time-frames. Building on this observation, we propose $\mathbb{C}+\text{Mag}$, a magnitude-informed physics-driven deep learning reconstruction method. The proposed method employs an ADMM-based unrolling framework with a novel magnitude-aware data-fidelity formulation, where quadratically smoothed optimization and momentum-based updates are introduced to address the non-differentiability and non-convexity of the magnitude constraints. Experiments on retrospectively undersampled cine MRI and phase-contrast flow MRI
datasets, as well as prospectively undersampled real-time cine MRI acquisitions, demonstrate improved artifact suppression, sharper anatomical recovery, and better preservation of phase information compared to conventional PD-DL methods, which is further supported through blinded expert reader evaluations.
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All authors Mahdi Saberi, Yaşar Utku Alçalar, Merve Gülle, Chetan Shenoy, Mehmet Akçakaya
Cosmology and Nongalactic Astrophysics
Imprints of Mass Accretion History on Galaxy Cluster Morphology
Kabelo Tsiane, Camille Avestruz, Elena Rasia et al. (+6 more)
Variations in dynamical states of
galaxy clusters can introduce biases and scatter in observable-mass relations. The dynamical state of a cluster is an emergent feature of its mass
accretion history (MAH), it is therefore useful to constrain the MAH of the cluster. In this work, we characterize 305 massive clusters from The300 project by connecting features from their projected
stellar distributions to their mass
accretion histories (MAH). As a baseline, we first correlate host dark matter
halo dynamical state indicators at $z=0$ with their MAH via the Spearman rank correlation coefficient $\rho_{\mathrm{sp}}$. Both substructure mass fraction and center-of-mass offset measurements correlate strongly with the MAH measured between $0.1\lesssim z\lesssim 1$. We repeat this exercise with morphological measurements of projected
stellar density maps, many of which exhibit moderate correlation strength with different times in the MAH. Broadly, core morphological measurements ($r \leq 30\,\mathrm{kpc}$) correlate better with early-time MAH. Core-excised ($50\,\mathrm{kpc} \leq r \leq 1\,\mathrm{Mpc}$) morphological measurements correlate better with late-time MAH. We further quantify the MAH prediction power of both traditional dynamical state indicators and morphological parameters using Multivariable Conditional Abundance Matching (MultiCAM). MultiCAM employs simple rank-ordering operations, making it straightforward to translate to observed
datasets. We find reasonable ($\rho_{\mathrm{sp}} \geq 0.6$) performance for predictions of the mass fraction between $1\lesssim z\lesssim 0.1$, though with notable information loss when using projected quantities. In one example application of our methodology, we use the coefficients of the MultiCAM models to select subsamples of
galaxy clusters that have accreted more (or less) of their $z = 0$ mass budget over a given time frame.
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All authors Kabelo Tsiane, Camille Avestruz, Elena Rasia, Roan Haggar, Jesse B. Golden-Marx, Guillaume Mahler, Elizaveta Sazonova, James Taylor, Massimo Meneghetti
Quantum Physics
Three-qubit entanglement in the Bethe-Heitler process
Haotian Cao, Yuxun Guo, Yoshitaka Hatta et al. (+1 more)
The familiar Bethe-Heitler process on the
proton target $e+p\to e+p+\gamma$ is transformed into a laboratory for studying multiparticle
entanglement. We discuss how bipartite and genuine tripartite
entanglement between the final state electron,
proton and
photon are built up by successive $1\to 2$ and $2\to 2$ elementary interactions. We validate our argument by simulating events. Below 5 GeV center-of-mass energy, we identify more than 900 Greenberger-Horne-Zeilinger (GHZ) states and 1200 W states, each with a fidelty exceeding 99%.
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All authors Haotian Cao, Yuxun Guo, Yoshitaka Hatta, Jakob Schoenleber
High Energy Physics (Phenomenology)
Can $\gamma\gamma$ collisions rival $e^-e^+$ in probing doubly charged Higgs bosons?
Abdesslam Arhrib, Rachid Benbrik, Mohammed Boukidi et al. (+3 more)
High-energy $\gamma\gamma$ collisions, realizable as an operational mode of future
lepton linear
colliders such as the ILC and CLIC, provide a promising environment to probe extended
Higgs sectors. We investigate the sensitivity of such
colliders to doubly charged
Higgs bosons within the 2-
Higgs Doublet Model with type-II seesaw (2HDMcT). Focusing on the three-body production channels $\gamma\gamma \to H^{\pm\pm}H_1^{\mp}H_1^{\mp}$ and $\gamma\gamma \to H^{\pm\pm}H_1^{\mp}W^{\mp}$, we perform a parameter space scan consistent with theoretical constraints as well as current
collider,
flavor, and electroweak precision observables (EWPOs). We show that $\gamma\gamma$ collisions can rival the discovery potential of the conventional $e^+e^-$ mode for probing doubly charged
Higgs bosons through a $4\ell+E_T^{miss}$ signature ($\ell=e,\mu$). Despite the reduced effective luminosity resulting from the
photon spectrum, the significantly enhanced production cross sections, exceeding those in electron-positron collisions by more than one order of magnitude, compensate for this limitation. By performing a detailed signal-to-background analysis at center-of-mass energies of $\sqrt{s}=830$ and $1245$ GeV, we demonstrate that a discovery significance at the $5\sigma$ level can be achieved for viable
benchmark points (BPs).
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All authors Abdesslam Arhrib, Rachid Benbrik, Mohammed Boukidi, Mohamed Chabab, Khalid Goure, Stefano Moretti
Statistical Mechanics
Critical behavior and crossover scaling in the Light-Heavy model
Shilpa Prakash, Mustansir Barma and Kabir Ramola
The Light-Heavy (LH) model involves two species of particles (light and heavy) coupled with a fluctuating surface (described by tilts). The dynamics include the inherent
diffusion of the particles (or tilts) as well as the drive provided by the tilts (or particles). When the two are of similar magnitude, the system lies in the unscaled (uLH) regime, while a significantly weaker drive leads to the scaled (sLH) regime. In the unscaled limit, the model exhibits an order-disorder transition characterized by the fluctuation-dominated phase ordering (FDPO). In this state, interestingly the dynamics is driven by multiple modes, giving rise to dynamic clusters. Away from the critical regime the disordered phase retains vestiges of FDPO behavior on length scales smaller than the correlation length. We examine this local FDPO-like behavior by using a scaling function that links the off-critical and critical regimes. We next turn to the scaled model and show that the multi-mode dynamics present in the unscaled regime is replaced by dynamics that is effectively controlled by a single dominant mode in the scaled regime. Concurrently, the two-point correlations change from the $\mathcal{O}(1)$ FDPO form to an anomalous long-range form that decays as $1/\sqrt{L}$. Drawing on the analogy with the sABC model, where similar anomalous correlations appear at criticality, we derive an analytical expression for the two-point correlation function using the same approach used for that model.
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Materials Science
Energy-efficient, Reconfigurable Optoelectronic Artificial Synapses Based on MoWS$_2$ Alloy for Pattern Recognition and Color Image Filtering Applications
Deepak Kumar Sahu, Santu Kumar Ghosh, Sagarneel Ghoshal et al. (+2 more)
Two-dimensional transition-metal dichalcogenide
alloys are potential candidates for advanced optoelectronic and neuromorphic applications due to their strong light-matter interactions and controllable defect properties. However, large-area growth of such
alloys remains challenging, while the correlation between their physical and neuromorphic properties remains largely unclear. In this work, we present an innovative microcavity chemical vapor deposition (CVD) reactor pathway to grow uniform, and large-area MoWS$_2$ mono- and few-layer
alloy films for demonstrating optoelectronic synaptic functionalities. Driven by growth-induced intrinsic sulfur vacancies, as confirmed by XPS, KPFM, and STEM measurements, our optoelectronic synaptic device (OSD) successfully emulates essential biological synaptic features, such as excitatory postsynaptic currents (EPSC), paired-pulse facilitation (PPF~170%), and stimulus-dependent short- and long-term plasticities (STP & LTP). With picojoule-order energy consumption per synaptic event and nanoampere-order dark current, the device enables low-power neuromorphic learning, including emulation of Pavlovian associative learning. Furthermore, the experimentally measured conductance weight-update characteristics enabled an artificial neural network (ANN) simulation to achieve 92.43% recognition accuracy on the MNIST handwritten digit
dataset. Finally, we demonstrate advanced neuromorphic visual processing by executing color image filtering based on the device's wavelength-selective photoresponse characteristics. This simple, yet multifunctional device architecture provides a promising path toward energy-efficient, spectral-selective neuromorphic
vision applications.
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All authors Deepak Kumar Sahu, Santu Kumar Ghosh, Sagarneel Ghoshal, Saranya Das, Samit K. Ray
Quantum Physics
Gisin's Argument and the Limits of Causal Explanations in Relativistic Spacetime
Felix J. Rutzinger
Gisin has provided an argument for the conclusion that no covariant nonlocal model can reproduce the operational statistics observed in Bell experiments. Gisin's argument applies only to deterministic models and we argue that proposed generalizations of the argument beyond determinism based solely on statistical notions are unconvincing, as they lack the resources to formulate a satisfactory notion of no-retrocausality. We introduce the framework of frame-indexed causal models in order to extend Gisin's argument to the genuinely probabilistic case. Frame-indexed causal models associate potentially different causal models with different reference frames while requiring corresponding operational variables to agree runwise. Within this framework, we establish two no-go results based on Gisin's argument. First, we show that no empirically adequate frame-indexed causal model can jointly satisfy Independent Settings, No-Retrocausality, and Causal Lorentz Invariance. Second, it is established that this result continues to hold when Causal Lorentz Invariance is weakened to Lorentz Invariance of Causal Connections, which requires only the skeleton of the causal structure to remain invariant, thereby allowing the direction of causal influence to depend on the choice of reference frame. These results are discussed in the context of relativistic interpretations of
quantum theory and nonclassical causal inference.
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Optics
Conservation of Pseudoangular Momentum in the Radiative Emission and Optical Excitation of Valley-Polarized Surface Lattice Resonances
Siamak Khorasani, Marc R. Bourgeois and David J. Masiello
Surface
lattice resonances (SLRs) are collective polaritonic excitations in nanoparticle arrays, with band-edge states enabling
symmetry-based control of radiation including scattering, photoluminescence, conventional and polariton lasing, and condensation. Here, we show that the integer pseudoangular momentum (PAM) of valley-polarized SLRs in parity-broken hexagonal arrays of honeycomb and kagome arrangements is directly encoded in the phase and polarization structure of their emitted electromagnetic fields. The effects of parity-
symmetry breaking on SLR PAM and the localized surface plasmon angular momentum associated with the individual nanoparticles composing the array are investigated, and an explicit link between these quantities is established. Leveraging electromagnetic reciprocity, we further propose a structured optical field possessing the same array
symmetries and integer PAM as the underlying SLRs and demonstrate theoretically and numerically that it selectively couples to valley-polarized SLRs with matching quantized PAM. Our results establish PAM-resolved light-matter interactions involving
lattice resonances as providing new opportunities for
symmetry-selective nanophotonic control,
chiral light generation, and angular-momentum-engineered metasurfaces.
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Computational Physics · Cross-listed from cs.LG
Composing Flow-Matching Energies with Known Physics: Generation, OOD Detection, and Inversion on PDE Fields
Yixuan Sun, Anirban Samaddar and Sandeep Madireddy
Probabilistic modeling of physical fields benefits from both a data-driven prior and known physical structure such as the governing equations. Energy-based models (EBMs) are a natural fit since energies compose additively, which enables augmenting physics information during inference. However, EBMs have been difficult to train and sample from due to the intractable partition function. We show in this work that flow matching models with a potential-induced velocity yield an explicit scalar energy at all transport times, whose gradient is exactly the converted learned score and which recovers the marginal negative log-density at the population optimum. The time-dependent energy functions are obtained purely from the matching regression objective on an independent linear Gaussian interpolation, without a variational form or additional MCMC steps, and the sampling retains the flow ODE. Access to the energy function from a trained model serves three roles: energy-corrected data generation, energy as a scoring function for out-of-distribution (OOD) detection, and energy compositional posterior sampling for inverse problems. In particular, we show the explicit energy permits general MCMC samplers in the predictor-corrector sampling framework, reducing PDE residual and spectral distance compared to the flow ODE baseline. Furthermore, we demonstrate utilizing the data energy and physics-based energy (e.g., PDE residuals) as complementary mechanisms to improve detection accuracy for OOD tasks. In addition, we explore the connection to MCMC-based inference for inverse problems by composing the energy with a quadratic observational likelihood that yields a posterior energy, used as an explicitly chosen family of inference-time targets.
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Atmospheric and Oceanic Physics
How Do AI Climate Models Respond to Warming Across Climate Zones?
Charlotte C. Merchant, Milan Klöwer, Bradley Stanley-Clamp et al. (+4 more)
Regional
climate zones are expected to shift under global warming. Whether AI
climate models have learned to generalize
climate-zone distributions under warming in a physically meaningful way affects their suitability for
climate projection. We address this question by applying a Köppen-Geiger
climate-zone decomposition to AIMIP Phase 1 models under prescribed +4K SST forcing and comparing their responses to physics-based AMIP models. Using this diagnostic, we compare baseline classification skill, per-zone responses in temperature, precipitation, and near-surface specific humidity, and the spatial structure of departures from physics-based models. All AI models considered reproduce the 1979-2014 ERA5 climatology within the physics-based models' range, but only the hybrid physics-AI model NeuralGCM-HRD reorganizes zones in agreement with established thermodynamic and hydrological scaling relations. The remaining emulators have distinct failure modes traceable to their architectural treatment of land
cells. A physically consistent
climate-zone response is therefore necessary for AI models intended for
climate projection.
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All authors Charlotte C. Merchant, Milan Klöwer, Bradley Stanley-Clamp, Maren Höver, Simon L. L. Michel, Edward Groot, Hannah M. Christensen
Instrumentation and Methods for Astrophysics
The Roman Coronagraph Community Participation Program: data reduction pipeline astrometric calibration
Amanda Chavez, Jason Wang, Vanessa P. Bailey et al. (+5 more)
The Nancy Grace Roman Space
Telescope will be equipped with a Technology Demonstration Coronagraph Instrument that will push the current limits of high contrast imaging for
exoplanets ($10^{-9}$ contrast). The Roman Coronagraph Community Participation Program has developed corgidrp, a python-based data reduction pipeline for the Roman Coronagraph Instrument that will perform essential data processing and calibration steps for coronagraphic observations. The astrometric calibration function within corgidrp allows us to understand the on-sky angular size and distance scale of science observations by characterizing essential detector parameters: boresight, plate scale, north angle, and optical distortion. Measuring these astrometric calibration products not only helps us understand the science output of our data, but it is what allows us to point the Roman coronagraph accurately at our science target in the first place. Here, we describe the techniques used within the astrometric calibration and demonstrate that our
algorithm meets Technology Demonstration Threshold Requirements: (1) compute the on-sky location of the center of CGI EXCAM detector to better than 30 [mas] and (2) compute the on-sky position angles of the camera axes to within 0.3 [deg].
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All authors Amanda Chavez, Jason Wang, Vanessa P. Bailey, Maxwell Millar-Blanchaer, Julien Girard, John Krist, Julia Milton, Marie Ygouf
Quantum Physics
High-Harmonicity Planar Penning Traps for Single-Electron Qubits
Kedar Mal, A. N. Agnihotri, Sugam Kumar et al. (+2 more)
We present a detailed account of the design choices required for planar Penning traps that feature a highly harmonic confining potential. High harmonicity is indispensable for a number of applications, particularly for confinement of single electrons as
qubits in
quantum-information processing. The present work extends previous studies [D. Goldmann and G. Gabrielse, Phys. Rev. A 81, 052335 (2010)] by a fully analytic treatment of finite electrode gaps and their relevance for small traps on the millimetre size scale and below, when relative gap sizes are non-negligible. We derive the overall trap potential with a particular focus on different models of finite-gap potentials and show how to find the optimum trap geometry and electrode voltages to minimize anharmonicities. The analytic calculations are compared with detailed finite-element simulations.
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All authors Kedar Mal, A. N. Agnihotri, Sugam Kumar, Wolfgang Quint, Manuel Vogel