Cosmology and Nongalactic Astrophysics
Pushing the Primordial Frontier: Exact Linear Solutions in Multifield Inflation
Javier Huenupi, Claudio Muñoz, Gonzalo A. Palma et al. (+1 more)
We present exact analytic solutions for the linear dynamics of a two-field inflationary system in which the
primordial curvature perturbation $\zeta$ is coupled to an isocurvature perturbation $\sigma$ of
entropy mass $\mu$. The solutions are valid for arbitrary values of $\mu$ and the dimensionless interaction strength $\lambda$. They therefore provide analytic control over the strong mixing regime in which $\zeta$ interacts with light isocurvature fields, commonly associated with rapid-turn inflation. As a first application, we derive the amplitude of the
primordial power spectrum in closed form, obtaining an expression that interpolates between the weakly mixed, strongly mixed, light-field, and heavy-field regimes. These results open the way to analytic studies of multifield observables beyond the power spectrum, including non-Gaussianity, particle production, and loop corrections.
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All authors Javier Huenupi, Claudio Muñoz, Gonzalo A. Palma, Spyros Sypsas
Astrophysics of Galaxies
PAHSPECS: Spatially Resolved PAH Spectroscopy at cosmic noon with JWST MIRI MRS
Fergus R. Donnan, Karin Sandstrom, Irene Shivaei et al. (+10 more)
We present spatially resolved spectroscopy with JWST/MIRI MRS of a representative sample of normal
star-forming
galaxies at $z\sim1.1$ as part of the PAHSPECS program. To extract emission from Polycyclic Aromatic Hydrocarbon (PAH) features, we forward model the data cubes with non-parametric spatial distributions, accounting for convolution with the PSF. With this method we are able to recover accurate spatial profiles of the 3.3 $\mu$m, 6.2 $\mu$m, 7.7 $\mu$m, 11.3 $\mu$m PAHs and [ArII] (6.98 $\mu$m) emission and produce PAH ratio maps at
cosmic noon. From the PAH ratio maps we find that PAHs become larger and more neutral with increasing galactocentric radius, which is the opposite of trends in local
galaxies, indicating radial ISM gradients in normal
star-forming
galaxies are different at
cosmic noon. Through spatially resolved SED fitting of HST and JWST photometry we measure the UV radiation field hardness through the intrinsic ratio of UV to optical flux and find the 3.3/11.3 PAH ratio to decrease with increasing hardness and the 11.3/7.7 to increase. This may suggest photo-destruction of small/ionized PAHs is driving the observed PAH ratio trends and may explain the overall lower 3.3/11.3 PAH at
cosmic noon compared to the local universe. This work demonstrates that PAH properties hold crucial information on the resolved ISM physics of
galaxies at
cosmic noon.
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All authors Fergus R. Donnan, Karin Sandstrom, Irene Shivaei, Leindert A. Boogaard, Tanio Díaz-Santos, Cristina M. Lofaro, Román Fernández Aranda, Manuel Aravena, Roberto Decarli, Hanae Inami, Pablo G. Pérez-González, Gergö Popping, Paul P. van der Werf
High Energy Astrophysical Phenomena
A Joint Optimal Search for Gravitational Waves from Resolved and Unresolved Supermassive Binary Black Holes with Pulsar Timing Arrays
Boris Goncharov, Gabriela Sato-Polito, Xiaoming Bi et al. (+1 more)
We introduce, from first principles, a joint model of the gravitational wave background (GWB) and brightest supermassive black hole binary (SMBHB) sources that may be individually resolvable in
Pulsar Timing Array (PTA) searches for gravitational waves. We propose the characteristic number of SMBHB sources, $N_{\rm c}$, as a detection statistic for the astrophysical origin of the GWB. We then demonstrate how the brightest SMBHBs assist in resolving $N_{\rm c}$. Applying our method to the simulated NANOGrav 15-year data, which replicates all aspects of real data's known noise, observations, and the inferred GWB power spectrum, we demonstrate direct astrophysical limits on the strain amplitude of individually resolvable SMBHBs. We find that 21 of 114 SMBHB candidates from active
galactic nuclei observations are in tension with the NANOGrav's observations. In contrast, only one candidate is in tension with the NANOGrav data based on the upper limits reported in the original analysis. Constraining the Poisson-specific characteristic number of SMBHBs, $N_{\rm c}$, at ${\rm yr}^{-1}$, we outline implications for the population properties of SMBHBs. Based on our new model applied to the simulated NANOGrav data, we calculate the probability of detecting GWs from isolated SMBHB in the 15-year data to be 2% at the ${\rm SNR}=5$ level. Our projection towards the expected NANOGrav 20-year data suggests an increase to 5%. With this, we estimate the probability of finding an outlier with an SNR of 2 in the NANOGrav 20-year data to be $40\%$.
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All authors Boris Goncharov, Gabriela Sato-Polito, Xiaoming Bi, Matias Zaldarriaga
Quantum Physics
Impulse Decoding of Quantum LDPC Codes: Equivalence of Degeneracy and Code-Shortening
Shobhit Bhatnagar, Michele Pacenti, Nithin Raveendran et al. (+2 more)
Quantum error correction is essential for building scalable
quantum computers. Within the stabilizer formalism, the Calderbank-Shor-Steane framework constructs
quantum codes from pairs of classical linear codes. A distinctive feature in this setting is degeneracy, where multiple equivalent error estimates exist-a phenomenon that has no classical counterpart, and the lack of a meaningful classical coding-theoretic interpretation of which has remained a gap in the literature. In this paper, we demonstrate that degeneracy is closely related to the classical operation of shortening of a linear block code. Interestingly, the shortening here takes place at the decoder rather than at the encoder. Leveraging this insight, we present a parallel decoding scheme for
quantum low-density parity-check codes, which we term impulse decoding, that significantly outperforms belief propagation with ordered statistics decoding, as well as several other existing techniques, under both code-capacity and
circuit-level noise, with significantly lesser complexity. We then present another
algorithm based on decoding of residual errors, which when combined with impulse decoding achieves further performance improvement under
circuit-level noise.
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All authors Shobhit Bhatnagar, Michele Pacenti, Nithin Raveendran, David Declercq, Bane Vasić
High Energy Physics (Phenomenology)
Twist-3 contributions to $\gamma\gamma\to\pi^0\pi^0,\,K_S^0K_S^0$ in $k_T$ factorization
Jun-Kang He and Cong Wang
We compute the cross sections for the two-
photon processes $\gamma\gamma\to\pi^0\pi^0$ and $\gamma\gamma\to K_S^0K_S^0$ in $k_T$ factorization, including the chirally enhanced two-parton twist-3 light-cone distribution amplitudes. For these charge-suppressed neutral channels the twist-3 cross sections exceed the twist-2 ones by close to an order of magnitude in the intermediate-energy region, bringing the predictions much closer to the Belle data, the residual underestimate being plausibly attributable to higher-order QCD corrections. The calculation reproduces the measured angular distributions and the energy dependence of the charged channels and the neutral pion, though not the steeper fall of the neutral kaon. The neutral-to-charged ratios are the most discriminating observables. They depend strongly on energy in the data, whereas our calculation, like other approaches in the literature, yields a nearly flat ratio. Finally, in a phenomenological discussion, we combine our contribution with the soft handbag contribution and largely reproduce the observed energy dependence, suggesting that the hard and soft contributions are comparably important in the few-GeV region.
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Fluid Dynamics
Quartic Lyapunov functions for global fluid stability
David Darrow, Elizabeth Carlson and David Goluskin
A fluid system is 'globally stable' if all initial conditions eventually converge to the same state. Since Reynolds (1895) and Orr (1907), the standard way to show global stability has been the energy method, which uses the fluctuation energy as a Lyapunov function. However, the energy method fails whenever transient energy growth is possible, so it often yields overly strict stability criteria. The first broadly applicable alternative has recently been introduced (Goulart & Chernyshenko 2012; Fuentes et al. 2022), using
polynomial optimization to construct non-quadratic Lyapunov functions. Unlike the energy method, however, this approach is highly technical, computationally expensive, and hard to interpret physically. Moreover, it treats only one set of parameters at a time; in particular, if it verifies global stability at a certain Reynolds number, it does not imply the same for smaller values. The present work makes progress by connecting this numerical program with new analytical and physical insights. We show how to exploit
symmetries of shear flows via convenient complex variable representations, greatly reducing the problem size. We then refine key inequalities, replace several expensive computational steps with simpler analytical alternatives, and show how to prove global stability over a range of Reynolds numbers. Our analysis identifies the simplest class of non-quadratic Lyapunov functions for two-dimensional parallel shear flows: a three-parameter family of quartic
polynomials. Using these Lyapunov functions, we verify global stability of 2-D plane Couette flow and plane Poiseuille flow up to higher Reynolds numbers than possible with the energy method. Our work takes a step towards an analytical theory of global fluid stability beyond the energy method, and offers structural insights that should significantly improve future numerical investigations of global stability.
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Astrophysics of Galaxies
PAHSPECS: Polycyclic aromatic hydrocarbon properties at cosmic noon with JWST/MIRI MRS
Cristina Maria Lofaro, Tanio Díaz Santos, Irene Shivaei et al. (+13 more)
Context.
Cosmic noon (z ~ 1-3) marks the peak of the
cosmic star-formation rate density, when
dust-obscured
star formation dominated
galaxy growth. Mid-infrared spectroscopy probes the interstellar medium through PAH emission, whose band ratios trace PAH charge, size, and local radiation-field conditions. Aims. We characterize the PAH properties of five z ~ 1.1
star-forming
galaxies from the PAHSPECS survey and investigate how their PAH luminosities and band ratios relate to global
galaxy properties. We compare them with local luminous infrared
galaxies (LIRGs) to assess whether PAH emission at
cosmic noon differs from that nearby. Methods. We analyze JWST/MIRI MRS observations of five ASPECS
galaxies in the HUDF. Integrated spectra are extracted with wavelength-dependent apertures and modeled with CAFE, including ancillary photometry to constrain the
dust emission.
Stellar masses and SFRs are derived with Prospector. Results. Compared to local LIRGs, most PAHSPECS sources show higher 6.2/7.7 and lower 11.3/7.7 ratios, suggesting an ionized PAH component weighted toward smaller grains. The 3.3/11.3 ratio is less constrained, since the 3.3 micron feature is detected in only two sources. Within the sample, 11.3/7.7 increases with sSFR and
star-formation surface density, while 6.2/7.7 decreases with sSFR, consistent with preferential processing of small ionized PAH carriers. ASPECS-15, the AGN-hosting source, has the lowest 6.2/7.7 ratio and highest sSFR, suggesting a reduced contribution from small PAHs, potentially due to AGN activity. The 7.7 micron luminosity follows the local L7.7-SFR relation, supporting its use as a
star-formation tracer at z ~ 1. Conclusions. PAH emission at
cosmic noon appears shaped by different ISM conditions than in nearby starburst
galaxies, likely reflecting more intense radiation fields, while the 7.7 micron feature remains a robust SFR tracer.
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All authors Cristina Maria Lofaro, Tanio Díaz Santos, Irene Shivaei, Leindert A. Boogaard, Karin Sandstrom, Fergus R. Donnan, Manuel Aravena, Pablo G. Pérez-González, Román Fernández Aranda, Roberto Decarli, Frédéric Galliano, Hanae Inami, Göran Östlin, Gergö Popping, Paul van der Werf, Fabian Walter
Astrophysics of Galaxies
An extreme ram-pressure stripping event in a protocluster at redshift 4.3
Dazhi Zhou, Scott C. Chapman, Manuel Aravena et al. (+16 more)
In the nearby Universe, the environment plays a crucial role in suppressing
star formation in dense regions. In particular, ram-pressure stripping (RPS) is a major mechanism for removing gas from
galaxies in clusters, occurring when
galaxies travel through a dense hot atmosphere and leave trailing gaseous wakes. By depleting the cold gas reservoir, RPS can drive outside-in quenching and is therefore thought to be an important route for transforming cluster
galaxies. At earlier times, however, the hot atmosphere in protoclusters is expected to be immature, so environmental effects are commonly assumed to be dominated by gravitational interactions. Here we report ALMA and JWST observations of SPT2349$-$56-C26 (hereafter C26), a massive
galaxy experiencing an extreme RPS event in the SPT2349$-$56 protocluster at $z\,{=}\,4.3$. More than half of the [CII]-traced cold gas lies outside its
stellar body, with the emission peak offset by 6 kpc. These observations show that RPS can remove most of the cold gas from massive
galaxies in dense protocluster cores as early as $z\,{=}\,4.3$, providing a direct hydrodynamic pathway for environmental quenching at $z\,{>}\,4$.
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All authors Dazhi Zhou, Scott C. Chapman, Manuel Aravena, Roger Deane, Anthony H. Gonzalez, Ryley Hill, Nicholas LeVar, Matthew A. Malkan, Adam Muzzin, Nan Zhang, Kedar A. Phadke, Vismaya R. Pillai, Manuel Solimano, Justin S. Spilker, Nikolaus Sulzenauer, Joaquin D. Vieira, David Vizgan, George C. P. Wang, Axel Weiss
Cosmology and Nongalactic Astrophysics
Field-level vs summaries: convergence of information in non-Gaussian density fields
Ivana Nikolac, Fabian Schmidt and Beatriz Tucci
We elucidate the sources of information gain in weakly non-Gaussian cosmological fields at the field- vs. summary-statistic-level in a controlled setting. Specifically, we compare field-level inference (FLI) with the standard power spectrum plus bispectrum (P${+}$B), and a family of composite-operator correlators (OCs) built from auto- and cross-spectra of local powers of the
galaxy density field. The forward model is a linear density field with a single local quadratic coupling $\lambda$ and Gaussian noise; this minimal nonlinear setup interpolates between a purely Gaussian
dataset ($\lambda=0$) and a non-Gaussian one ($\lambda\sim 1$), while keeping the analytical structure tractable. FLI is performed by jointly sampling the initial conditions, bias and noise parameters via MCMC; the summary posteriors are obtained with simulation-based inference (SBI) as well as Fisher estimates. In the Gaussian limit, the P${+}$B, OCs and FLI yield equivalent constraints, in agreement with the perturbative expectation. As the nonlinear coupling $\lambda$ increases, the summary-based uncertainties on the model parameters grow faster than the FLI ones, leading to an increasing information loss for a fixed set of summaries. This loss is largely, but not completely, recovered by adding OCs corresponding to up to the 6-point function. The information loss over FLI becomes even more pronounced for lower-noise data, where summaries corresponding to up to the 6-point function still capture significantly less information than the field.
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Physics and Society
Strategies for preventing and reversing polarized online discourse
Leon Klingborg, Kenneth Mavor and Alexander J. Stewart
Political polarization poses a variety of challenges for modern democracies. Entrenched disagreements on policy can prevent constructive discourse and compromise, and high levels of affective polarization threaten to undermine social cohesion and support for institutions. Finding ways to promote constructive discourse while maintaining free expression has proved a challenge for social media platforms, media outlets and policy makers alike. Here we develop a computational model – based in psychology – of online discourse and opinion dynamics under complex individual identities, which we use to assess the capacity of realistic interventions to prevent or reverse polarization. We show that changes to the range of acceptable opinions in a society – i.e. the Overton window – have a limited impact on polarization, and that attempts to “optimize” the Overton window can even trigger the onset of polarization. In contrast, interventions that shift
attention towards under-discussed topics, or increase the costs of violating existing norms, are often effective at preventing polarization, but are less successful at reversing it. Most strikingly, increasing the salience of influential individuals, who model non-polarized discourse, can be highly effective at both preventing and reversing polarization. However we also find that once polarization has set in, even the most successful interventions result in latent extremism when identities are complex. Our work suggests that restricting
speech by shrinking the range of acceptable discourse is an ineffective way to tackle polarization, whereas enforcement of existing norms,
attention nudges and the presence of elites who model good behavior can be highly effective.
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Mesoscale and Nanoscale Physics
Coherent effects in quantum transport models and their classical counterparts
N.S. Maslova, V.N. Mantsevich, I.M. Sokolov et al. (+1 more)
We analyze the transport properties of quasiparticles locally excited at an initial time moment in several exactly solvable
quantum models. It is revealed that, in the investigated
quantum systems, the time-dependent probability distribution function (PDF) exhibits behavior similar to that of classical continuous-time random walk (CTRW) models, such as Lévy walks or diffusing diffusivity.
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All authors N.S. Maslova, V.N. Mantsevich, I.M. Sokolov, P.I. Arseyev
Earth and Planetary Astrophysics
Ice Giants Revisited: Uranus and Neptune as Magma Ocean Worlds
Edward D. Young, Sarah P. Marcum, Aaron Werlen et al. (+1 more)
Uranus and Neptune are commonly interpreted as volatile-rich "ice
giants", an assumption that underpins most interior models. Here we show that their observed radii, bulk densities, gravitational harmonics, normalized moments of inertia, intrinsic luminosities, and key features of their atmospheric compositions are consistent with interiors comprising supercritical, hydrogen-rich magma oceans overlain by H2-rich envelopes. Our results, based on three fit parameters for each
planet, provide a parsimonious explanation for the structures, thermal states, and atmospheric chemistries of Uranus and Neptune. We find that the
Solar System's ice
giants are better understood as magma-ocean
giants, with origins parallel to those of sub-Neptune gas-
dwarf planets. A continuum among gas
dwarf planets permits Neptune and Uranus to serve as accessible, data-driven test cases for structure models and material properties used to understand sub-Neptunes.
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All authors Edward D. Young, Sarah P. Marcum, Aaron Werlen, Paula N. Wulff
Materials Science
Anisotropic Short-Range Order Modulates Ferroelectric Switching in Wurtzite ScAlN Alloys
Shunda Chen, Xianchao Dong, Xiaochen Jin et al. (+1 more)
Ferroelectric switching in wurtzite
alloys is typically understood in terms of composition, strain, defects, and interfaces, while local chemical order is often neglected or treated as a secondary perturbation. Here we show that short-range order (SRO) is a previously overlooked microscopic variable that substantially influences the intrinsic switching barrier. Using first-principles canonical sampling, we find that wurtzite ScAlN develops a robust, highly anisotropic SRO that challenges the conventional random-
alloy picture. This ordering suppresses in-plane Sc–N–Sc motifs while enhancing columnar mixed-cation chains along the polar $c$ axis, reflecting the
symmetry-distinct polar and basal directions of the wurtzite
lattice and reorganizing its polar connectivity. Relative to random-
alloy structures, SRO systematically increases the intrinsic switching barrier across a broad composition range. Motif-resolved analysis further identifies the population of columnar Sc–N–Al–N–Sc motifs as the primary structural descriptor underlying switching-barrier variations among configurations with different local order. These results establish anisotropic SRO as an independent degree of freedom for tuning ferroelectric switching. More broadly, they reveal how local chemical order can couple to the
symmetry-distinct directions of a polar
semiconductor lattice to modify functional behavior. Our findings lay a foundation for SRO engineering as a route to tailoring switching barriers without changing
alloy composition.
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All authors Shunda Chen, Xianchao Dong, Xiaochen Jin, Tianshu Li
Quantum Physics
Einstein-Podolsky-Rosen correlations between mechanical oscillators revealed through SU(1,1) interferometry
Max-Emanuel Kern, Stefano Marti, Raquel Garcia-Belles et al. (+5 more)
Quantum correlations are essential for achieving
quantum advantage in computing, communication and sensing. Moreover, their observation challenges and constrains our fundamental understanding of nature. Mechanical oscillators in the
quantum regime provide an appealing platform for preparing and investigating
quantum correlations at macroscopic scales. Despite substantial progress, however, continuous-variable
quantum correlations stronger than
entanglement have not yet been observed in this macroscopic regime. Here, we report the experimental observation of continuous-variable Einstein-Podolsky-Rosen correlations between two spatially-separated mechanical oscillators with an effective mass of $\sim 16 \,\mu g$ each. This is achieved by coupling them to a superconducting
qubit which allows for engineering a two-mode squeezing interaction when parametrically driven. Crucially, we show that this interaction can be used to witness
quantum correlations through the realization of a mechanical SU(1,1) interferometer. Our results expand the toolbox of operations in
circuit quantum acoustodynamics and demonstrate that
quantum correlations stronger than
entanglement can also be observed in macroscopic systems, thereby shedding light on the boundary between
quantum and classical regimes.
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All authors Max-Emanuel Kern, Stefano Marti, Raquel Garcia-Belles, Andraz Omahen, Igor Kladaric, Arianne Brooks, Yiwen Chu, Matteo Fadel
Computational Physics · Cross-listed from cs.MS
PDECheck: A Diagnostic Software Suite for Auditing Learned PDE Simulators
Lennon J. Shikhman
PDECheck audits learned PDE simulators through callable propagators or saved predictions. It evaluates relative $L^2$ error, temporal composition, finite-difference generators, and equation-specific physical residuals, retaining horizons and missing prerequisites. Analytic and manufactured defects verify the kernels. A heat-equation sweep quantifies generator sensitivity to timestep, frequency, grid, and precision. A three-equation, five-seed FNO study compares supervised, PINO-style, and composition-regularized training. An accuracy-matched advection comparison has state errors below 0.3% and a
semigroup-error ratio of 1.48 (95% interval 1.34-1.62). The composition penalty lowers
semigroup error but increases composed-state and longer-rollout prediction error. A time-reparameterization check produces identical low-error endpoints with different compositions. Applications to five external equation families exercise the readers and physical contracts on coarse grids. The release includes reproducibility artifacts and measured CPU/CUDA costs.
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Quantum Physics
Learning Arbitrary Lindbladians with Quantum Error Correction
Nikita Romanov, Petr Ivashkov, Weiyuan Gong et al. (+4 more)
We study ansatz-free Lindbladian learning, the problem of reconstructing the generator of an open
quantum system without prior knowledge of its
Hamiltonian or dissipator structures. This problem exhibits two fundamental precision limits.
Hamiltonian components not obscured by dissipation are Heisenberg-limited, while full Lindbladian reconstruction is subject to the quadratically worse standard
quantum limit. This creates an apparent algorithmic impasse: achieving the Heisenberg-limited
Hamiltonian learning requires suppressing unknown dissipation, which seemingly demands prior reconstruction of the noise and thereby incurs the standard-
quantum-limit cost. In this work, we resolve this obstruction by giving an
algorithm that learns the
Hamiltonian disjoint from dissipator (HDD) at the Heisenberg limit without prior knowledge of either the
Hamiltonian or dissipator supports. Our main technical ingredient is a randomized recursive stabilizer-code construction that progressively identifies and suppresses the dominant dissipative terms without reconstructing the full dissipator. Building on this framework, we also introduce an efficient end-to-end
algorithm that learns the entire sparse Lindbladian at the standard
quantum limit. Finally, we prove that in the ansatz-free setting, the HDD terms constitute the maximal set of
Hamiltonian coefficients uniformly learnable at the Heisenberg limit. We show that every term outside HDD is fundamentally standard-
quantum limited, extending prior no-go results to the ansatz-free setting. Together, our protocols provide a scalable framework for characterizing open
quantum systems, with
quantum error correction serving as a key learning primitive.
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All authors Nikita Romanov, Petr Ivashkov, Weiyuan Gong, Ishaan Kannan, Andi Gu, Hong-Ye Hu, Susanne F. Yelin
General Relativity and Quantum Cosmology
Beyond Plane Waves: Coherent Network Response to Collimated Gravitational-Wave Wavepackets
S. D. Campos
We present a paraxial wavepacket model for structured, collimated gravitational-wave bursts and derive the coherent response of detector networks to these signals. For current terrestrial baselines such as LIGO-Virgo, analytic mismatch estimates confirm that the paraxial wavepacket model waveforms are effectively indistinguishable from standard sine-Gaussian bursts, validating the robustness of the plane-wave approximation in this regime. However, we identify a physical scaling regime relevant to third-generation networks and
galactic-scale
Pulsar Timing Arrays in which finite transverse structure-motivated by wave-optics
lensing or ultra-relativistic beaming induces non-negligible
geometric phase shifts. A toy event-level Monte Carlo study compares a standard burst-search ranking with a paraxial wavepacket model-constrained statistic that penalizes
geometric inconsistencies across detectors. In this controlled setup, the model prior yields an illustrative factor of $\sim 3$-$4$ gain in detection efficiency at a fixed false-alarm rate, while maintaining performance on plane-wave-like signals. These results suggest that paraxial corrections may provide a necessary metrological framework for signal discrimination and unbiased parameter estimation in future
cosmic-scale observations.
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Instrumentation and Methods for Astrophysics
Resolving the smallest scales of massive star formation: A case for next-generation thermal-infrared interferometers
Emma Bordier, Evgenia Koumpia, Lucas Labadie et al. (+3 more)
This white paper was submitted to the European Southern Observatory (ESO) as part of the "Expanding horizons: transforming astronomy in the 2040s" call. Understanding how massive
stars assemble their mass is a major astrophysical challenge, primarily because the critical
accretion, ejection, and fragmentation processes occurring within the innermost 100 au remain largely inaccessible. Current facilities, such as ALMA and near-infrared interferometers, either lack the resolution to probe the compact high-mass
star-disk interaction zone or are severely hindered by high extinction and marginal spectral resolution. To bridge this observational gap, this white paper advocates for next-generation thermal-infrared interferometers operating in the L, M, N, and Q bands to directly observe warm
dust, hot gas, and embedded protostars at sub-milliarcsecond scales. These advanced capabilities will provide unprecedented access to a rich molecular inventory and spatially resolved spectroscopy, which are crucial for disentangling
accretion streams, disk winds, and fragmentation mechanisms on dynamical timescales. We propose that developing heterodyne-based, long-baseline interferometry at high, dry sites represents the most transformative and realistic pathway to finally unveil the complete picture of massive
star formation.
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All authors Emma Bordier, Evgenia Koumpia, Lucas Labadie, Joel Sanchez-Bermudez, Alvaro Sanchez-Monge, Jean-Philippe Berger
History and Philosophy of Physics
Perturbative QCD as a quantitative tool in the years 1976-2000
R Keith Ellis
This paper traces the development of precision QCD in the years 1976-2000.This is after the discovery of asymptotic freedom, and after the exploration of the simplest processes based on the operator product expansion. The new theoretical tools of factorization, infra-red safety andresummation, needed to make predictions for the colliding beam machines of this era, are described. The role of computer
algebra and modern spinor techniques for the calculation of amplitudes and cross sections are briefly reviewed. A selection of important processes calculated at next-to-leading order (or in limited cases beyond next-to-leading order) is presented.
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Computational Physics · Cross-listed from math.NA
A Convex Quasilinearization Method for Solving Nonlinear PDEs with Physics-Informed Neural Networks
Gbenga T. Awojinrin, Abdul-Akeem Olawoyin and Rami M. Younis
We present a numerical method for the forward solution of nonlinear partial differential equations (PDEs) in which Bellman-Kalaba quasilinearization reduces the nonlinear problem to a sequence of linear subproblems, each discretized by collocation onto a trial space that is linear in its parameters and solved by a single direct linear least-squares QR factorization. The trial space, which we term Linear-in-Learnables (LiL), comprises representations whose trainable parameters enter linearly, including random-feature extreme learning machines, spectral
polynomial bases, and trigonometric expansions, each implemented as a physics-informed neural network. The method thus replaces the nonconvex gradient-based training that limits standard PINNs with a convex per-step solve. We establish local Newton-Kantorovich convergence of the outer iteration to a residual-limited neighborhood under an explicit smallness condition, with the limiting accuracy governed by the best-approximation residual of the trial space rather than by an optimization tolerance. The method, denoted LiL-Q, is assessed on seven
benchmarks spanning scalar nonlinear PDEs (Bratu, viscous Burgers, Buckley-Leverett), coupled systems (plane-strain elasticity and the incompressible Navier-Stokes equations in two and three spatial dimensions), and steady-state Darcy flow with heterogeneous permeability. Across these problems, LiL-Q converges in single-digit outer iterations in most cases, even at the coarsest basis sizes and independent of the parameter count. When the exact solution lies in the span of the trial space, the method recovers it to machine precision in a single solve. On the Navier-Stokes
benchmarks, it matches or exceeds published PINN solvers with up to two orders of magnitude fewer trainable parameters, without gradient-based optimization.
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Quantum Physics
Optimal Probe State for Phase Estimation Under Covariant Measurement
Qipeng Qian and Christos N. Gagatsos
We study the optimization of input states for phase estimation under covariant measurements. Building on Holevo's framework, which provides the optimal covariant measurement for a fixed input state, we further optimize over the input state itself. For a general even $2\pi$-periodic cost function with non-negative
Fourier coefficients, we derive a necessary and sufficient condition for the optimal input state: Its Fock coefficients are determined, up to arbitrary phases, by the eigenvector corresponding to the largest
eigenvalue of a Toeplitz
matrix defined by the cost function. This characterization yields an explicit expression for the attainable lower bound of the average cost under optimal covariant measurements and shows that this bound asymptotically approaches zero in the infinite-energy limit. For the specific cost function $W(\theta,\tilde{\theta})=4\sin^2[(\theta-\tilde{\theta})/2]$, we obtain the optimal input state and the corresponding minimum average cost in closed form, demonstrating Heisenberg scaling with respect to the mean
photon number.
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Quantum Physics
Optimal Calibration of Quantum Network Links
Vinay Kumar, Claudio Cicconetti, Marco Conti et al. (+1 more)
The reliable distribution of
entanglement is essential for the effective operation of
quantum networks. Due to fundamental differences between
quantum and classical communication systems, it is necessary to develop specialised
algorithms and protocols that also account for
quantum-specific constraints. In this work, we focus on the issue of recalibration. As suggested by recent experimental studies, the process of local
entanglement generation in a
quantum link degrades over time due to environmental changes that have to be estimated and compensated via a fixed-duration calibration operation, during which the link is not available. Therefore, in such a
quantum network, every link alternates between an activation period, during which it operates normally, and a calibration period, during which it cannot participate in the end-to-end
entanglement distribution, thereby creating a trade-off between link quality (the initial fidelity of generated pairs, which decays during activation) and availability (the fraction of time the link is usable, which calibration reduces). We develop analytically a protocol for optimally assigning activation periods to each link in linear
quantum repeater chains, subject to any general end-to-end fidelity requirements and local initial fidelity thresholds. Building on this foundation, we extend to general
quantum networks, where multiple paths may cross at common links. To this, we propose a heuristic approach, evaluate it through simulations, and compare its performance with a
benchmark, a numerical approach and theoretical bounds, confirming the analytical ordering of their performance.
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All authors Vinay Kumar, Claudio Cicconetti, Marco Conti, Andrea Passarella
Nuclear Experiment
Observation of a dominant $\boldsymbol{0f_{7/2}}$ neutron configuration in the $\boldsymbol{^{32}}$Si $\boldsymbol{J^{\pi}=5^-}$ isomeric state
C. R. Hoffman, G. L. Wilson, J. Chen et al. (+8 more)
An yrast, $J^{\pi}=5^-$,
spin-trap isomer has been previously identified in $^{32}$Si. The isomeric state decays predominantly via a hindered $E3$ transition [B($E3$) = 0.0841(10)~W.u.], bypassing a nearby $E2$ decay path to the first excited $3^-$ level. The single-
neutron aspects of these negative parity levels were investigated via the $^{31}$Si$(d$,$p)^{32}$Si reaction at 9.6~MeV/$u$ using HELIOS and the ATLAS in-flight facility. The $5^-$ state appears as a dominant $\ell=3$ transfer with a relatively large spectroscopic factor, confirming its single-particle $\nu0f_{7/2}$ character. The yrast $3^-$ level had a reduced $\ell=3$ spectroscopic factor of $\approx$ 0.44 compared to that of the $5^-_1$ level. This is similar to the situation observed in nearby $^{34}$S which by contrast has a measured B($E2, 5^-\rightarrow 3^-$) transition strength closer to 1~W.u.. It has been concluded that the hinderance of the $5^-_1\rightarrow 3^-_1$ transition in $^{32}$Si is not primarily due to the differing overlaps in the
neutron structure. Instead, the lack of participation by both the
protons and the
neutrons in the transition is proposed as the transition-strength reduction mechanism.
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All authors C. R. Hoffman, G. L. Wilson, J. Chen, B. P. Kay, T. L. Tang, S. R. Carmichael, M. Gott, S. Lesher, M. S. Martin, G. E. Morgan, J. Wu
Disordered Systems and Neural Networks
Learning Dynamics of Chain-of-Thought State Tracking in a Solvable Transformer Model
Niklas Forner, Marcel Kühn, Matthias Thamm et al. (+1 more)
Chain-of-thought generation can turn a multi-step computation into a sequence of locally checkable state updates, but the training dynamics by which
transformers acquire such updates remain poorly understood. We study this question in a solvable setting: a simplified one-block
transformer trained by supervised next-
token prediction on state sequences generated by composing permutations. The architecture separates fixed-lag action
retrieval, learned by RoPE
attention, from a specialized MLP logic module that applies the retrieved permutation to the current state. Using a statistical-physics mean-field description, we derive dynamics for three order parameters measuring
attention retrieval, teacher-
matrix alignment, and off-target logic overlap. These equations quantitatively match simulations for the order parameters and, combined with a logit-distribution approximation, qualitatively predict the sharp transition in final rollout accuracy. The analysis reveals staged learning: the logic module first learns a mixed heuristic;
attention then locks onto the relevant action, enabling efficient MLP
alignment. Together, these results provide a controlled mechanistic account of how
attention-based
retrieval and MLP-based logic co-develop during chain-of-thought state tracking.
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All authors Niklas Forner, Marcel Kühn, Matthias Thamm, Bernd Rosenow
Soft Condensed Matter
Dynamical properties of ab initio water from machine-learning potentials
P. Montero de Hijes, L. Neubeck, G. Kresse et al. (+1 more)
We assess the dynamical properties of
liquid water predicted by several density functionals using machine-learning interatomic potentials. MACE models were trained for SCAN, RPBE-D3/zd, revPBE-D3/zd, revPBE0-D3/BJ, PBE0-D3/zd, and PBE0-D3/BJ using previously reported ab initio
datasets. We compare translational, rotational, and viscous dynamics through time-correlation functions, which resolve relaxation processes across different timescales, and through the corresponding long-time kinetic coefficients. The
diffusion coefficient, second-rank orientational relaxation time, and shear viscosity reveal systematic differences among functionals. Part of these differences can be rationalized as shifts along the phase diagram, as comparisons relative to each functionals melting temperature reduce the spread in the dynamical observables. Among the functionals considered, RPBE-D3/zd provides the best overall agreement with experiment. We therefore perform a broader validation of RPBE-D3/zd using a Behler–Parrinello neural-network potential over a wide range of temperatures, densities, and pressures. The model reproduces the magnitude and anomalous pressure dependence of the
diffusion coefficient, gives generally good viscosities, and captures the temperature dependence of the rotational relaxation time.
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All authors P. Montero de Hijes, L. Neubeck, G. Kresse, C. Dellago
Quantum Physics
Closest Accessible Symmetry reduction: a tool for Hamiltonian interpolation analysis
Ana Palacios, Artur Garcia-Saez, Arnau Riera et al. (+1 more)
We introduce a framework for analysing the spectrum of
Hamiltonian interpolations without heavily relying on discretising the interpolation parameter. The method is based on the concept of accessible
symmetries: a problem-class-dependent family of certifiable reflections that induce bipartitions of the
Hilbert space. At each step, the interpolation
Hamiltonian is projected onto the sectors of the accessible
symmetry that is closest to being satisfied, yielding a hierarchy of weakly coupled pseudo-eigenspaces together with explicit residual couplings between them. We show that this representation captures qualitative signatures of
quantum phase transitions, provides estimates of their location, and offers insights into their nature. The quality of the approximation is controlled by the compatibility between the accessible
symmetry family and the problem instance. Although motivated in spirit by adiabatic
quantum computation, our approach applies more broadly to the study of
Hamiltonian phase diagrams, providing a new perspective on the spectral reorganisation of many-body
quantum systems.
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All authors Ana Palacios, Artur Garcia-Saez, Arnau Riera, Marta P. Estarellas
High Energy Astrophysical Phenomena
X-ray eclipse tomography: Resolving the extent of X-ray-emitting region(s) in the Seyfert galaxy ESO362-G18
Laura Rueda-Nieto, Beatriz Agís-González and Giovanni Miniutti
X-ray spectral variability in active
galactic nuclei (AGNs) can be either intrinsic or caused by external processes, such as variable absorption. Several transient occultation events from individual clouds or X-ray eclipses have been identified in a number of AGNs in the past two decades. We report results from the analysis of two Swift monitoring campaigns of the Seyfert
galaxy ESO 362-G18, a changing-look AGN that showed transitions between optical spectral types 1.5 and 1.9 in the past, most likely induced by variable absorption. We identified two X-ray eclipses during the first Swift campaign, one of which we were able to follow almost entirely from ingress to egress. We used time-resolved spectroscopy to follow the
evolution of the X-ray spectrum with time in order to derive the properties of the absorbing cloud and the size of the main X-ray-emitting region. The cloud is located towards the innermost
dust sublimation zone, intermediate between the
dust-free broad-line region and the dusty
torus. The X-ray-emitting region is confined within 35 gravitational radii (Rg) from the centre. A deeper analysis indicated that the eclipsing cloud likely comprises a denser core and a more tenuous atmosphere. We were also able to estimate the size of the soft excess and hot corona X-ray-emitting regions separately. These are two of the most relevant continuum components in the X-ray spectra of unobscured AGNs. Our analysis suggests that the soft-excess-emitting region is 50% more extended than the hot corona one. However, it appears to co-exist with and is not replaced by the hot corona one in the innermost 30 Rg. Our results highlight the potential of X-ray eclipse
tomography for providing a clearer view of the innermost
accretion flow geometry and of the AGN surroundings out to the
torus scale.
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Fluid Dynamics
How Sparse and How Noisy? Systematic Benchmarking of Inverse Physics-Informed Neural Networks for Manning Friction Estimation in Shallow Water Equations
Soheil Radfar
Physics-informed neural networks (PINNs) offer a promising framework for inverse hydrodynamic modeling by combining sparse observations with governing physical constraints. However, their reliability for estimating hydraulic parameters under data limitations remains insufficiently characterized. This study
benchmarks inverse PINN recovery of the Manning friction coefficient in the shallow water equations under controlled variations in observation sparsity, noise, and observed variable type. Two cases are considered: a one-dimensional MacDonald subcritical channel with an analytical steady reference solution, and a two-dimensional sloped channel with a
parabolic transverse bed generated using a balanced finite-volume solver. The Manning coefficient is treated as a trainable positive scalar and recovered jointly with the flow field using a two-phase strategy that first fits observations and then incorporates the physics residual. Results show that the two-dimensional case achieves robust friction recovery, with errors below 5% when at least 10 depth and velocity observations are available and noise is at or below 10% of the field standard deviation. Recovery remains stable up to 20% noise with 50 observations, but becomes unreliable with only five observations. In contrast, the one-dimensional case shows a persistent positive bias of about 15% that is largely insensitive to observation count and noise, indicating a structural identifiability limitation rather than a data-density limitation. Observation-type ablation shows that recovery degrades substantially when only depth or velocity is observed, demonstrating that joint depth-velocity information is essential for reliable inverse identification. Overall, the results provide a reproducible
benchmark for assessing when inverse PINNs can and cannot reliably estimate Manning friction from sparse and noisy shallow-water observations.
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Strongly Correlated Electrons
High-temperature ferromagnetism and antiferromagnetism in monolayer \ce{CrTe2}: Roles of strong spin-lattice coupling and charge doping
Anupama S and Mukul Kabir
The interplay of structural, electronic, and
magnetic degrees of freedom governs phase stability and critical temperatures in two-dimensional magnets. Controlling this coupling is essential for advancing fundamental understanding and spintronic applications. Combining first-principles calculations with Heisenberg Monte Carlo simulations, we reveal a rich
magnetic phase diagram governed by the interplay of
lattice strain and carrier density. These results provide a unified framework that reconciles diverse experimental reports on epitaxial layers and predicts a novel double-stripe
antiferromagnetic phase, further stabilized by electron doping. Moreover, structural and electronic perturbations enable room-temperature ferromagnetism and antiferromagnetism. This
magnetic evolution arises from competing, highly tunable direct and ligand-mediated exchange interactions in the presence of Ruderman-Kittel-Kasuya-Yosida coupling. By disentangling their individual contributions, we elucidate the underlying microscopic mechanisms, which transcends the conventional conduction electron picture. Finally, we quantify the colossal magnetoelastic response and identify zone-folded Raman modes that serve as unique experimental fingerprints for phase identification. Together, these results establish \ce{CrTe2} as a versatile platform for two-dimensional spintronics, where
magnetic order and transition temperatures are tailorable via structural and electrical engineering.
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Quantum Physics
A polynomial-time approximation scheme for minimum-weight decoding of topological codes
Shouzhen Gu, Lily Wang and Aleksander Kubica
Two-dimensional topological translationally invariant (2D TTI) stabilizer codes lie at the heart of fault-tolerant
quantum computation, but using them requires solving the decoding problem. Minimum-weight decoding of these codes was recently shown to be NP-hard, even in basic settings, such as the color code with Pauli $Z$ errors and the toric code with Pauli $X$, $Y$ and $Z$ errors. Here, we prove that minimum-weight decoding of 2D TTI codes nonetheless admits a
polynomial-time approximation scheme (PTAS), i.e., for any constant $\varepsilon>0$, a recovery operator of weight within a multiplicative factor of $1+\varepsilon$ of the minimum can be found in
polynomial time. Our approach builds on Arora's PTAS for Euclidean problems, such as the traveling salesman problem, and applies when decoding can be cast in terms of point-like excitations connected by string-like errors. It therefore extends beyond two dimensions, covering certain higher-dimensional topological codes and
quantum memories, including the toric code with phenomenological or
circuit-level noise.
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