arXiv subjects

Xiao Xue

Publications and source records attributed to Xiao Xue.

At least 19 recordsLinked to original sources

arXiv2026

Beyond Driving: Envisioning Activities in Future Autonomous Vehicles through Experience-Centered Design

Autonomous vehicles (AVs) are poised to fundamentally alter personal transportation, offering occupants the freedom to engage in various non-driving-related activities (NDRAs). However, our current understanding of how people might actually use this time in fully autonomous vehicles (FAVs) is limited. Traditional research methods often struggle to capture the influence of diverse travel circumstances and purposes when exploring future scenarios. This paper introduces experience-centered design (ECD) as an approach to investigate potential NDRAs within FAVs by examining the intricate connections between individuals' daily routines, specific travel contexts, and the activities they might undertake in transit. Through a multi-phase study employing participatory techniques that facilitated narrative construction and exploration, including diary studies, scenario scripting, and mixed reality (MR) enactments, we enabled participants to ground speculative future scenarios in their own lived experiences. This process yielded nuanced insights into preferences and behaviors concerning potential NDRAs, alongside the underlying subjective meanings and sociotechnical considerations. Our findings lead us to conceptualize NDRAs not as isolated instances of "travel time use," but as dynamic sequences of interrelated activities deeply shaped by pre- and post-journey contexts. The effectiveness of our ECD approach in bridging current lived experiences with future scenarios was crucial for uncovering these insights. Ultimately, this study reconceptualizes AVs as complex sociotechnical systems that actively mediate human activity and interaction, suggesting a fundamental shift in their role within the urban fabric.

cs.HC↗

arXiv2026

Discovering $μ$Hz gravitational waves and ultra-light dark matter with binary resonances

In the presence of a weak gravitational wave (GW) background, astrophysical binary systems act as high-quality resonators, with efficient transfer of energy and momentum between the orbit and a harmonic GW leading to potentially detectable orbital perturbations. In this work, we develop and apply a novel modeling and analysis framework that describes the imprints of GWs on binary systems in a fully time-resolved manner to study the sensitivity of lunar laser ranging, satellite laser ranging, and pulsar timing to both resonant and nonresonant GW backgrounds. We demonstrate that optimal data collection, modeling, and analysis lead to projected sensitivities which are orders of magnitude better than previously appreciated possible, opening up a new possibility for probing the physics-rich but notoriously challenging to access $μ\mathrm{Hz}$ frequency GWs. We also discuss improved prospects for the detection of the stochastic fluctuations of ultra-light dark matter, which may analogously perturb the binary orbits.

astro-ph.CO↗

arXiv2026

Prospects for gravitational wave and ultra-light dark matter detection with binary resonances beyond the secular approximation

Precision observations of orbital systems have recently emerged as a promising new means of detecting gravitational waves and ultra-light dark matter, offering sensitivity in new regimes with significant discovery potential. These searches rely critically on precise modeling of the dynamical effects of these signals on the observed system; however, previous analyses have mainly only relied on the secularly-averaged part of the response. We introduce here a fundamentally different approach that allows for a fully time-resolved description of the effects of oscillatory metric perturbations on orbital dynamics. We find that gravitational waves and ultra-light dark matter can induce large oscillations in the orbital parameters of realistic binaries, enhancing the sensitivity to such signals by orders of magnitude compared to previous estimates.

gr-qc↗

arXiv2026

A New Probe of Dark Matter Subhalos: Stellar Aberration with TESS

Small-scale dark matter (DM) structure encodes key information about the particle nature of DM and therefore provides a sensitive test of competing models. Yet, it remains hidden from electromagnetic surveys and is instead inferred through its gravitational effects. Stellar aberration, the apparent shift in a light source's position induced by the observer's motion, offers a largely unexplored channel to access such signatures. DM subhalos can perturb the observer's motion, imprinting characteristic, spatially correlated shifts in stellar positions across the sky. We show that the Transiting Exoplanet Survey Satellite (TESS), with its long temporal baseline, wide sky coverage, and high-cadence observations, is well suited to search for these aberration signals. We derive Fisher-matrix-based sensitivity estimates for constant observer accelerations, forecasting a sensitivity down to $6.3\times 10^{-9}\,\mathrm{m/s^2}$ from the combined sample of TESS stars with magnitude $\mathrm{Tmag}\leq 10$. This sensitivity allows TESS to probe concentrated DM subhalos over a broad parameter space, from $\gtrsim 10^{-6}\,\mathrm{M_{\odot}}$ at AU-scale distances to $\gtrsim 10^{7}\,\mathrm{M_{\odot}}$ at $\mathcal{O}(10\,\mathrm{pc})$. TESS's sector-based observing strategy further provides intrinsic temporal resolution of potential DM-induced aberration signals. Moreover, we briefly discuss challenges for future data analysis, including the modeling of instrumental systematics and stellar astrometric foregrounds, such as parallax and proper motion. Our results establish stellar aberration as a novel probe of DM substructure, paving the way for dedicated searches in TESS and next-generation wide-field surveys.

astro-ph.CO↗

arXiv2026

Controllable interaction between photons and distant spins via vacuum Rabi oscillations

Vacuum Rabi oscillations between a single photon and a single spin demonstrate the capability of harnessing light-matter interaction at the level of a single quantum of energy. Since the observation of strong spin-photon coupling in gate-defined quantum dots, probing this interaction in the time-domain has been a major objective. Here, we carefully engineer a device composed of two spatially separated double quantum dots hosting single electron spin qubits and a superconducting cavity to accommodate microwave photons. We observe multiple vacuum Rabi oscillations between each spin qubit and the cavity. By concatenating vacuum Rabi oscillations involving the two spins, an energy excitation in one qubit can be emitted as a photon and then transferred to the other qubit. When a single photon is emitted, the cavity is prepared in a Fock state, leading to an accelerated vacuum Rabi frequency. These results serve as building blocks not only in exploring light-matter interactions, but also in interfacing semiconductor spin qubits to photonic links.

cond-mat.mes-hall↗

arXiv2026

Practical Quantum Advantage before Fault Tolerance via Quantum-Informed Machine Learning

Early quantum devices can deliver a practical advantage before fault tolerance. The role we identify is a statistical module within a classical scientific workflow: a compressed memory with a collective two-copy readout, evaluated against a verifiable definition of practical quantum advantage. We develop this mechanism in quantum-informed machine learning for chaotic dynamical systems. A family of $k$-indexed higher-order quantum statistical priors (Q-Priors) hosts the $k$-point marginal of the invariant measure on $n_q = kq$ qubits. We prove a two-stage advantage. In the representation stage, superposition and entanglement compactly store non-factorisable spatial correlations of the invariant measure on $n_q$ qubits. In the extraction stage, joint Bell measurements estimate any \emph{post hoc} Pauli functional with a copy-pair count independent of $n_q$, whereas any adaptive single-copy protocol for the corresponding full-Pauli read-out requires $Ω(2^{n_q})$ copies; this is a provable quantum-classical separation in copy-measurement complexity. The two-copy read-out is realised in simulation and on superconducting processors. Two case studies instantiate the mechanism in workflows of scientific value. In a turbulent channel-flow study, the readout yields the velocity-direction coherence as a named non-diagonal correlator, and the $k = 2$ Q-Prior recovers invariant-measure statistics that the unregularised baseline loses. In a medium-range weather forecasting workflow on the ECMWF ERA5 reanalysis, the diagonal $k \leq 2$ Q-Prior steers a Koopman rollout, improves anomaly correlation skill and stabilises long-horizon rollouts against collapse onto a static mean field. Together, the mechanism and these two case studies satisfy our practical-advantage definition, identifying a candidate route to practical quantum advantage before fault-tolerant hardware.

quant-ph↗

arXiv2026

Autoregressive One-Step Generative Modeling for Dynamical System Forecasting

Fast surrogate modeling for high-dimensional physical dynamics requires more than low short-term error: useful models must roll out efficiently while preserving the statistical structure of long trajectories. Neural operators provide inexpensive autoregressive forecasts but can drift in turbulent regimes, whereas rolling diffusion and latent generative surrogates can represent stochastic transitions at the cost of multi-step denoising, noise-schedule design, or auxiliary compression models. We propose MeanFlow Long-term Invariant Spatiotemporal Consistency Autoregressive Models (MeLISA), a latent-free autoregressive generative surrogate built on pixel-space MeanFlow. MeLISA defines a blockwise stochastic transition kernel that generates each forecast block with a single model evaluation, avoiding latent encoders and iterative diffusion solvers at inference time. To stabilize long-horizon rollouts, MeLISA combines a Window-Consistency MeanFlow objective that learns conditional spatiotemporal generation from partially observed temporal windows with a Time Increment Consistency loss that constrains multi-lag finite increments and targets temporal-correlation structure. We evaluate MeLISA with compact UNet and scalable DiT backbones on two high-resolution benchmarks, extended 2D Kolmogorov flow at $256 \times 256$ and turbulent channel-flow slice at $192 \times 192$. MeLISA outperforms neural-operator baselines on short-term forecasting accuracy and long-horizon statistical metrics, including energy spectra, turbulent kinetic energy, and mixing-rate-related dynamics, while achieving inference speeds comparable to, and in some cases faster than, neural operators. To our knowledge, this is the first method for high-resolution one-step generation for physical dynamical systems with performance comparable to state-of-the-art deterministic surrogates.

cs.LG↗

arXiv2026

Probing Fundamental Constant Oscillation in the Galactic Center with S-Star Spectroscopy

Astrophysical spectroscopy provides a powerful probe of spacetime variations of fundamental constants, as atomic and ionic emission and absorption lines depend sensitively on the fine-structure constant. In particular, coherent temporal oscillations induced by an ultralight scalar background produce characteristic, time-resolved signatures that can be robustly disentangled from intrinsic variability. In the Galactic Center, such scalar backgrounds can be substantially enhanced, either through the formation of dense scalar clouds powered by black hole rotational energy extraction or as ultralight scalar dark matter forming a soliton-like core. These scalar configurations generically induce oscillations of the fine-structure constant, with periods set by the scalar mass and spatial profiles determined by the scalar wavefunction and its coupling to the electromagnetic sector. We show that precise, time-resolved spectroscopy of S-stars orbiting the supermassive black hole Sgr A$^*$ provides a sensitive test of these effects, enabling constraints on quadratic scalar-photon couplings in the exceptionally high boson-density environment of the Galactic Center.

hep-ph↗

arXiv2026

Explainable quantum-compressed machine learning for complex fluid flows

Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear flows, whereas expressive deep surrogates match high-fidelity simulations only through massive parameterisations that turn the learned dynamics into a black box. Here, we introduce quantum-compressed machine learning (QCML), which resolves this tension by compressing the latent propagator of a flow surrogate from $524{,}288$ trainable parameters to no more than $8$. This parameter reduction brings the learned dynamical law to the parameter scale of a physical constitutive relation rather than a black-box neural network, making the surrogate directly interpretable and controllable without sacrificing expressivity. The compression is realised by a structured quantum circuit whose unitary propagator constrains the latent spectrum to the unit circle exactly and by construction, replacing exponential error growth with linear accumulation over autoregressive rollouts. Classical regularisation only approximates this constraint: even a quantum-inspired classical baseline penalised towards unitarity collapses within one Lyapunov time on turbulent channel flow, whereas QCML remains stable over the full rollout. Shared phase and coupling angles parameterising the circuit correspond directly to modal frequencies and inter-mode interactions, giving the learned dynamics a physical interpretation in spectral space. On two patient-specific cardiovascular benchmarks, the structured QCML propagator matches the predictive accuracy of its classical counterpart on surface pressure spectra, pressure drop, and wall shear stress. These results establish QCML as a working component of scientific machine learning and a concrete contribution towards practical quantum advantage in real-world prediction.

physics.flu-dyn↗

arXiv2026

GUEST: Gravitational Universe Exploration with Satellite Tracking. A passive satellite laser-ranging mission for the dark gravitational Universe

GUEST is a space mission concept whose central objective is the detection of gravitational waves (GWs) in the microhertz band -- a physics-rich frequency window that no other present or planned detector can reach at a significant level. The concept is simple: two dense, passive spheres, covered with cube-corner retroreflectors, deployed in {highly eccentric} Earth orbits ($e \gtrsim 0.7$, period $P \gtrsim 33$ h), tracked continuously by the global network of satellite laser-ranging stations over a minimum observation time of 10 years, with an expected total duration of 30 years. The orbits themselves act as resonant detectors of the oscillating gravitational perturbations, with the microhertz sensitivity emerging from the selected orbital parameters. From the same data stream, GUEST delivers a programme of fundamental and applied science that cuts across particle physics, gravitational-wave astronomy, cosmology, astrophysics, and geodesy: the first coherent search for GWs from supermassive black-hole binaries in the $μ$Hz band, the exploration of primordial GW backgrounds in the unexplored energy-scale gap between pulsar-timing arrays and LISA, a dedicated probe of ultra-light dark matter in a parameter region untouched by any other experiment, a new way to search for ultra-light bosons, order-of-magnitude-improved tests of new gravitational interactions at astronomical ranges, and a step change in the absolute determination of $GM_\oplus$ that underpins the Global Geodetic Observing System and future navigation and Earth-observation missions. This white paper presents the motivation, scientific reach, and mission concept of GUEST.

astro-ph.CO↗

arXiv2026

Population statistics of nanohertz gravitational wave sources

The recent detection of a nanohertz gravitational wave (GW) background by pulsar timing arrays (PTA) has sparked extensive discussions regarding its origin-whether it arises from astrophysical supermassive black hole binaries (SMBHBs) or from primordial GWs generated by various early universe processes. Previous studies suggest that a key discriminant between these two origins is the non-Gaussianity of the GW background prior to the detection of any individual source. In this Letter, we introduce a hierarchical Bayesian inference framework for inferring population properties of GW sources. This approach enables not only the measurement of evidence for different GW origins using PTA data but also the inference of population properties of astrophysical SMBHBs, by optimally leveraging non-Gaussian information in individual bright sources and in power spectrum fluctuations of the GW background.

astro-ph.HE↗

arXiv2026

The SKAO Pulsar Timing Array

Pulsar timing arrays (PTAs) are ensembles of millisecond pulsars observed for years to decades. The primary goal of PTAs is to study gravitational-wave astronomy at nanohertz frequencies, with secondary goals of undertaking other fundamental tests of physics and astronomy. Recently, compelling evidence has emerged in established PTA experiments for the presence of a gravitational-wave background. To accelerate a confident detection of such a signal and then study gravitational-wave emitting sources, it is necessary to observe a larger number of millisecond pulsars to greater timing precision. The SKAO telescopes, which will be a factor of three to four greater in sensitivity compared to any other southern hemisphere facility, are poised to make such an impact. In this chapter, we motivate an SKAO pulsar timing array (SKAO PTA) experiment. We discuss the classes of gravitational waves present in PTA observations and how an SKAO PTA can detect and study them. We then describe the sources that can produce these signals. We discuss the astrophysical noise sources that must be mitigated to undertake the most sensitive searches. We then describe a realistic PTA experiment implemented with the SKA and place it in context alongside other PTA experiments likely ongoing in the 2030s. We describe the techniques necessary to search for gravitational waves in the SKAO PTA and motivate how very long baseline interferometry can improve the sensitivity of an SKAO PTA. The SKAO PTA will provide a view of the Universe complementary to those of the other large facilities of the 2030s.

astro-ph.IM↗

arXiv2026

MENO: MeanFlow-Enhanced Neural Operators for Dynamical Systems

Neural operators have emerged as powerful surrogates for dynamical systems due to their grid-invariant properties and computational efficiency. However, Fourier-based variants inherently truncate high-frequency components in spectral space, resulting in the loss of small-scale structures and degraded prediction quality at high resolutions when trained on low-resolution data. While diffusion-based enhancement methods can recover multi-scale features, they introduce substantial inference overhead that undermines the efficiency advantage of neural operators. In this work, we introduce MeanFlow-Enhanced Neural Operators (MENO), a novel framework that achieves accurate all-scale predictions with minimal inference cost. By leveraging the improved MeanFlow method, MENO restores both small-scale details and large-scale dynamics with superior physical fidelity and statistical accuracy. We evaluate MENO on three challenging dynamical systems, including phase-field dynamics, 2D Kolmogorov flow, and active matter dynamics, at resolutions up to 256$\times$256. Across all benchmarks, MENO improves the power spectrum density accuracy by up to a factor of 2 compared to baseline neural operators while achieving up to $14\times$ faster inference than the state-of-the-art Denoising Diffusion Implicit Model (DDIM)-enhanced counterparts, effectively bridging the gap between accuracy and efficiency. The flexibility and efficiency of MENO position it as an efficient surrogate model for scientific machine learning applications where both statistical integrity and computational efficiency are paramount.

cs.LG↗

arXiv2026

The MeerKAT Thousand-Pulsar Polarisation Array II: Searches for Ultralight Axion-Like Dark Matter

We construct Pulsar Polarisation Arrays (PPA), using regular pulsars monitored in MeerKAT's Thousand Pulsar Array (TPA) Programme, to search for Axion-like Dark Matter (ALDM) within Milky Way. Specifically, from a catalogue of 1237 regular pulsars, we select the 50 ones with the highest signal-to-noise ratio and set upper limits on the ALDM Chern-Simons coupling. We find no signals with statistical significance over the mass range of $[10^{-23},10^{-20}]\,{\rm eV}$ in the six-year MeerKAT's data. By combining the high-quality TPA pulsars and the accurate ionospheric subtraction of spinifex, we establish the most sensitive upper limits to the date on the ALDM Chern-Simons coupling, namely $\lesssim 10^{-14} - 3\times 10^{-13}\,{\rm GeV}^{-1}$, for the mass range of $[10^{-23},10^{-21}]\,{\rm eV}$ except at $m_a \sim 1.3 \times 10^{-22}\,$eV. This study underscores the great potential of constructing regular-pulsar PPAs for scientific tasks.

astro-ph.HE↗

arXiv2026

The MeerKAT Thousand-Pulsar Polarization Array I: Properties of the Polarization and Rotation Measure Time Series Data

The polarimetry of recent pulsar observations has provided a wealth of observational data with which to test physical theories of emission mechanisms, radiative transfer and even theories that extend beyond the Standard Model. In this work, we have outlined the data analysis of the polarisation time series data of a population of 513 pulsars from the Thousand Pulsar Array observing programme, laying the foundation for building the MeerKAT Thousand-Pulsar Polarization Array as a probe for ultralight Axion-Like Dark Matter (ALDM). From this large dataset we have focused on the temporal trends in the observed polarisation angle (PA) through a measure we call the PA offset, and characterised the trends due to the effects of Faraday Rotation within the interstellar medium and the Earth's ionosphere, as well as generic white and red noise models that are estimated within a Bayesian MCMC analysis. Then, motivated by potential extra contributions to the rotation of the PA that may not be Faraday-like, arising from the proposed ALDM field, we have investigated a derived time dependence for the rotation measure (RM) required to explain the observed PA offset. Comparison of these estimates to RM values that are measured in typical pulsar studies, through a technique known as RM Synthesis, provides a probe of any wavelength-independent contribution to the rotation of the PA. Although we find no evidence for oscillatory behaviour within our dataset's observation timespan, we do find cases of deviation from the usual RM values in several `pulsars of interest', as well as long-term linear trends in the time evolution of Faraday rotation that have been presented in the literature before.

astro-ph.HE↗

arXiv2026

Accelerating Redshift-Conditioned Galaxy Image Synthesis with One-step Generative Modeling

Understanding galaxy morphology evolution across cosmic time requires models that can generate realistic galaxy populations conditioned on redshift. In this work, we study efficient redshift-conditioned generative modeling for astrophysical image synthesis using diffusion models and pixel-MeanFlow. We first review the connections between score-based diffusion models, Flow Matching, one-step generative models, and modern diffusion samplers. We then evaluate DDPM, DDIM, DEIS-AB2, DPM++2M, and one-step pixel-MeanFlow on the GalaxiesML-64 dataset using morphology-based metrics, including ellipticity, semi-major axis, Sérsic index, and isophotal area. Our results show a clear accuracy-efficiency trade-off: standard DDPM sampling achieves the best distributional fidelity but requires high computational cost, while second-order samplers substantially improve efficiency over DDIM. Pixel-MeanFlow enables single-step generation and achieves competitive performance on several morphology statistics, though it remains weaker than many-step DDPM for fine-grained structure. Our results demonstrate that one-step generative models can recover key galaxy morphology statistics at orders-of-magnitude lower computational cost, opening a path toward efficient conditional simulators for large cosmological surveys and simulation-based scientific inference.

astro-ph.IM↗

arXiv2026

Physical Fidelity Reconstruction via Improved Consistency-Distilled Flow Matching for Dynamical Systems

Reconstructing high-fidelity flow fields from low-fidelity observations is a central problem in scientific machine learning, yet recent diffusion and flow-matching models typically rely on iterative sampling, making them costly for latency-sensitive workflows such as ensemble forecasting, real-time visualization, and simulation-in-the-loop inference. We study whether a high-fidelity flow-matching generative model can be compressed into a compact one-step model for fast scientific flow reconstruction. Our approach distills an optimal-transport flow-matching teacher into a one-step consistency model. Low-fidelity observations are incorporated at inference by initializing the generative trajectory from a noised observation along the transport path, allowing an unconditional high-fidelity flow model to perform conditional reconstruction without retraining the teacher. We evaluate this distillation strategy on three fluid benchmarks, Smoke Buoyancy, Turbulent Channel Flow, and Kolmogorov Flow, using coarse-to-fine reconstruction as a controlled testbed at field sizes up to $256 \times 256$. Across these settings, the distilled student retains similar performance of the teacher's model on spectrum metrics, while using roughly half as many parameters and achieving a $12\times$ inference speedup over the flow-matching teacher. Under the same training budget, the distilled student also outperforms a one-step consistency model trained directly from scratch by $23.1\%$ in SSIM, showing that teacher distillation improves training efficiency rather than merely accelerating sampling. These results suggest a promising route for turning future high-capacity scientific generative models into compact reconstruction models that are faster to train, cheaper to run, and easier to deploy.

cs.LG↗

arXiv2026

CAMO: An Agentic Framework for Automated Causal Discovery from Micro Behaviors to Macro Emergence in LLM Agent Simulations

LLM-empowered agent simulations are increasingly used to study social emergence, yet the micro-to-macro causal mechanisms behind macro outcomes often remain unclear. This is challenging because emergence arises from intertwined agent interactions and meso-level feedback and nonlinearity, making generative mechanisms hard to disentangle. To this end, we introduce \textbf{\textsc{CAMO}}, an automated \textbf{Ca}usal discovery framework from \textbf{M}icr\textbf{o} behaviors to \textbf{M}acr\textbf{o} Emergence in LLM agent simulations. \textsc{CAMO} converts mechanistic hypotheses into computable factors grounded in simulation records and learns a compact causal representation centered on an emergent target $Y$. \textsc{CAMO} outputs a computable Markov boundary and a minimal upstream explanatory subgraph, yielding interpretable causal chains and actionable intervention levers. It also uses simulator-internal counterfactual probing to orient ambiguous edges and revise hypotheses when evidence contradicts the current view. Experiments across four emergent settings demonstrate the promise of \textsc{CAMO}.

cs.AI↗