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Tian Li

Publications and source records attributed to Tian Li.

At least 19 recordsLinked to original sources
arXiv2026

Associating binary black holes with galactic centres using lensed gravitational waves

Binary black holes (BBHs) have been theorised to form through various different channels, but current LIGO-Virgo-KAGRA (LVK) sky locations make the direct study of formation channels a challenge. Association of strongly lensed BBHs to their hosts has been shown to be possible with current detector networks at upgraded sensitivity and survey telescopes like Euclid, with localisations that can reach sub-galactic scales in the source plane. With Hubble Space Telescope-like high-resolution imaging, we report that strong-lensing localisation of BBHs allows us to confidently associate the BBH with specific structures within their host galaxies, specifically, galactic centres (GCs). We demonstrate that a BBH localised to the GC of the galaxy can reject light-tracing BBH populations ($p < 5\%$) in a single observation, and in most cases demonstrate high confidence rejection ($p < 1\%$), providing a conservative basis to exclude light-tracing spatial distributions associated with field-like or non-central formation channels. Such association therefore directly supports formation channels confined to GCs, such as active galactic nuclei and nuclear stellar clusters.

astro-ph.HE↗
arXiv2026

Harnessing stellar kinematics to constrain dark energy with the double-source-plane gravitational lens SDSS J0946+1006

SDSS J0946+1006 is an attractive target for measuring cosmological parameters through lens modelling. It is the best-studied galaxy-scale strong gravitational lens with multiple sources whose redshift separations are well-suited to constraining the dark energy equation of state. However, multi-plane lens models with free cosmological parameters risk a multi-plane mass-sheet degeneracy, although this can be lifted by a non-lensing deflector density profile tracer. We simultaneously reconstruct near-infrared and near-ultraviolet HST imaging whilst including a velocity dispersion measurement from VLT-MUSE to constrain the foreground deflector. Imaging is reconstructed faithfully regardless of whether the inferred kinematics are realistic, though we find the kinematic constraint essential for shifting the preferred cosmology into a region not in significant tension with other dark energy probes. Deflector density profile perturbations, via substructure and multipolar halo shape deformations, have only a modest effect on inferred cosmology. Combining our fiducial model with Planck CMB data yields $w=-1.01^{+0.08}_{-0.13}$; or, combined with Pantheon SNe Ia, $w=-0.99^{+0.13}_{-0.15}$. We further show that this system proves a remarkably valuable complementary probe in the $w_{0}$-$w_{a}$ plane of an evolving dark energy model, and that the DESI BAO tension with $\Lambda$CDM seen when combined with other datasets is not reproduced when combined with this lens, yielding $(w_{0}, w_{a})=(-0.87^{+0.10}_{-0.11}, -0.22^{+0.28}_{-0.25})$. The system's third source, visible with MUSE, only weakly constrains $w$CDM but may strengthen $w_{0}w_{a}$CDM constraints, though further mass-model complexity along its line of sight is required. Overall, kinematics-informed multi-plane lens modelling is a robust route to competitive dark energy constraints, even with a single system.

astro-ph.CO↗
arXiv2026

Phase Independent Measurement of Weak Coherent Optical Signals

We develop a quantum sensing framework for the phase independent detection of weak coherent optical displacements based on SU(1,1) interferometry. Unlike conventional quantum measurement protocols that require prior knowledge of the signal phase and coherent homodyne detection, the proposed approach estimates the displacement magnitude independently of its phase. We show that, under ideal lossless conditions, a conventional SU(1,1) interferometer employing only total intensity detection saturates the quantum Cramer Rao bound for displacement magnitude estimation. We further derive the analytical expression of the quantum Cramer Rao bound and the sensitivity of the conventional SU(1,1) interferometer with total intensity detection and systematically investigate its performance in the presence of optical loss. The proposed phase-independent intensity detection scheme achieves comparable performance over experimentally relevant operating regimes while eliminating the need for local oscillators, phase locking, and quadrature tracking. These results establish SU(1,1) based intensity detection as a practical platform for phase independent quantum sensing.

quant-ph↗
arXiv2026

Disentangling the dark and stellar mass through precise lens modelling of the JWST observation of lensed quasar WFI2033--4723

We use high-resolution JWST/NIRCam imaging and measured time delays to model the quadruply imaged quasar WFI2033--4723 with a composite stellar plus dark-matter mass model. We first construct an elliptical power-law baseline model and recover Fermat-potential differences (fpd) consistent with previous HST-based and JWST-based analyses, providing a reference scale for composite modelling. We then replace the total mass profile with a physically motivated decomposition in which the stellar mass follows a multi-Gaussian expansion of the lens light, with a free radial mass-to-light gradient, and the dark matter is described by a generalized Navarro--Frenk--White (gNFW) halo. Using two external cosmological priors, Planck+DESI and Pantheon+SH0ES, the measured time delays constrain the mass-sheet-transformation freedom that would otherwise damage the stellar--dark-matter decomposition. In both cosmological cases, the stellar normalization lies between the expectations for Chabrier and Salpeter initial mass functions, while the radial mass-to-light gradient is not strongly required by the data (mildly positive). The dark matter halo has an inner slope $\gamma_{\rm in}\simeq1.3$, steeper than a standard NFW cusp, and the main astrophysical conclusions are insensitive to the adopted cosmological prior. This work shows that composite time-delay lens modelling can effectively separate baryons from dark matter. As a qualitative check, we reverse the logic and use our composite lens model without kinematic information to infer the cosmology instead. However, the strong degeneracy between $H_0$ and the halo scale radius $R_s$ prevents a robust standalone constraint.

astro-ph.GA↗
arXiv2026

Arachne: Orchestrating Cascades for Efficient Text-to-Video Model Training

The rising demand for AI-generated videos is fueled by advances in large-scale Text-to-Video (T2V) models, trained on extensive datasets of video clips spanning diverse resolutions and durations. To address this data heterogeneity, current training methods often use a bucketing strategy that groups samples into discrete buckets for efficiency. However, this approach struggles to scale with compute and data volumes under static parallelism schemes, such as data and sequence parallelism, leading to significant workload imbalances and hardware under-utilization. In this paper, we present Arachne, a novel training framework for efficient T2V model training at scale. Arachne decomposes the training process into fine-grained computational units, called \textit{cascades}, orchestrating their distributed execution and synchronization across the cluster through coordinated spatial and temporal optimization. Our comprehensive evaluation demonstrates that Arachne reduces iteration time by up to 65\% over leading frameworks, exhibiting a positive scaling trend where its performance advantages amplify as training scale grows.

cs.DC↗
arXiv2026

Strong Lensing Tomography: Double and pseudo multi-source plane strong gravitational lensing to constrain dark energy

Tomographic measurements of gravitational lensing with different lens and source redshift distributions contain crucial information about the universe's relative expansion rate, and hence dark energy. While this technique is well-established in weak lensing, its application to strong lensing has traditionally focused on Double Source Plane Lenses (DSPLs). However, DSPLs are exceedingly rare and fundamentally limited by the Mass-Sheet Degeneracy (MSD), a systematic uncertainty underexplored in previous literature. To overcome these challenges, we introduce Pseudo Double-Source Plane Lenses (PDSPLs): pairs of independent single-source plane lenses with self-similar deflectors. This generalizes the DSPL formalism to the $\sim 10^5$ galaxy-galaxy lenses expected from upcoming surveys like LSST, Euclid, and Roman. Unlike true DSPLs, PDSPLs are free from the intermediate source mass problem by construction, eliminating the associated secondary MSD and the need for multi-plane ray tracing. We incorporate the deflector galaxy's MSD into a hierarchical forecasting framework, demonstrating that this degeneracy severely degrades constraints from small DSPL samples, thus motivating our PDSPL statistical approach. We forecast constraints on the dark energy equation of state under a Flat $w_0w_a$CDM cosmology. The LSST 10-year photometric sample alone achieves $\sigma(w_0) \sim 0.45$, while simultaneously constraining the MSD parameter and deflector power-law slope to $\sim 2\%$. Adding a prior $\mathcal{N}(0.3, 0.05)$ on $\Omega_{\rm m}$ -- simulating combination with external probes like CMB, BAO, or SNe Ia -- tightens this to $\sigma(w_0) \sim 0.29$, competitive with current Stage III weak lensing analyses. Notably, this massive photometric sample outperforms smaller subsets with precise spectroscopic follow-up (e.g., from 4MOST), confirming statistical volume dominates over per-pair precision.

astro-ph.CO↗
arXiv2026

Gaussian processes on ray-guided transformed uniform grids for fast, flexible, and auto-differentiable adaptive source reconstruction in lens modelling

Strong gravitational lensing constrains cosmology and dark matter, but robust inference requires accurate source reconstruction. The achievable source resolution is highly position-dependent. Adaptive meshes can place resolution where needed, but typically rely on discontinuous operations, such as Delaunay tessellations or Voronoi binning, which can restrict regularization choices and break differentiability. In this paper, we present a novel approach for modelling the source on a ray-guided transformed uniform grid (RTU grid), that is adaptive to the lens mass model, auto-differentiable and flexible with respect to the regularization by allowing for an arbitrary choice of power spectrum. We achieve this by defining the source as a Gaussian process on a uniform grid, which is then transformed based on the cumulative distributions of rays traced back to the source plane. This approach ensures that source pixels contain a more uniform number of rays. The approach is fast by leveraging the fast Fourier transform to describe the Gaussian process in Fourier space. We apply this new approach to mock data and show that it achieves comparable fit quality with fewer source pixels, typically corresponding to about a factor of two fewer pixels per dimension, and increases Evidence Lower Bounds (ELBOs) for the same number of pixels. Using the RTU grid only mildly affects the difference in ELBO for models with and without substructures within lens galaxies. A fast, flexible, and auto-differentiable source reconstruction can greatly benefit the analysis of large samples of lens systems, e.g. those found within the Euclid survey

astro-ph.IM↗
arXiv2026

Manifold Constrained Tabular Deep Neural Networks

Tabular classification is often governed by local, condition-triggered rules rather than smooth global patterns. However, tabular deep neural networks (DNNs) are typically built upon Euclidean representations that favor smooth variations and semantic locality. This potential geometric mismatch can make it challenging for tabular DNNs to efficiently represent the discrete, rule-partitioned structures often underlying tabular classification. To address this issue, we propose HDE-Net, a manifold-constrained DNN that enables hierarchical decision modeling in hyperbolic space. We first abstract heterogeneous features into unified Latent Decision Nodes (LDNs) and embed them in the Poincar\'e ball, forming a continuous representation that resembles tree-structured reasoning. For numerical features, we introduce a Soft Decision Routing mechanism that approximates range-based local rules in a differentiable manner, bringing their LDN semantics closer to those of categorical features. An entropy-aware capacity allocation algorithm further adapts the number of LDNs per numerical feature to balance expressiveness and complexity. On the TALENT-tiny-core classification benchmark (30 datasets), HDE-Net achieves the \textit{best average rank}, outperforming both industrial GBDTs and recent tabular DNNs while maintaining high efficiency.

cs.LG↗
arXiv2026

Link-Free Multi-Node Timing Synchronization for Scalable Quantum Networking

Precise timing synchronization is essential for distributed quantum networking, enabling entanglement distribution, quantum teleportation, and entanglement swapping across remote nodes. Existing synchronization architectures rely on dedicated timing-distribution infrastructure, most notably White Rabbit networks, which constrain topology, scalability, and deployment in free-space and satellite environments. Here we demonstrate link-free synchronization of quantum network nodes using independently operating miniature rubidium atomic clocks and computational post-processing. We validate the approach on a deployed metropolitan-scale telecom fiber network spanning three geographically separated nodes. Following drift correction, atomic-clock-based synchronization achieves timing performance approaching that of a White Rabbit benchmark and remains stable over continuous 8-hour operation. As a stringent test of quantum-network functionality, we observe Hong-Ou-Mandel interference across spatially separated nodes with visibility exceeding 70%, statistically equivalent to that obtained using dedicated White Rabbit timing links. To the best of our knowledge, this represents the first observation of quantum interference across a deployed metropolitan-scale telecom fiber network synchronized entirely without dedicated timing-transfer infrastructure. These results establish atomic-clock-based synchronization as a scalable, topology-independent alternative to conventional timing-distribution architectures and a practical pathway toward terrestrial, airborne, and space-based quantum networks where dedicated timing links are unavailable.

quant-ph↗
arXiv2026

Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation

Multi-window CT imaging captures complementary pathological information across anatomical structures of differing densities, yet existing deep learning methods fuse representations only at later stages, missing cross-density interactions. We propose a cross-window knowledge distillation framework in which student encoders learn latent clinical priors from a teacher trained on the most informative window. Evaluated retrospectively on three cohorts - COPD-CT-DF (n=719), RSNA PE (n=1,433), and an in-house CTEPD dataset (n=161) - distillation improved per-window AUC by 10.1-16.5 percentage points on COPD-CT-DF (0.75-0.81 to 0.90-0.94; all P<0.001), with ensemble AUC reaching 0.9960. Similar gains were observed on RSNA PE (0.80-0.83 to 0.90-0.92) and CTEPD (AUC 0.7481 vs. 0.6264). Cross-window distillation internalises pathological signatures invisible to supervised approaches, offering a generalisable solution for multi-window pulmonary CT analysis.

eess.IV↗
arXiv2026

PRTS: A Primitive Reasoning and Tasking System via Contrastive Representations

Vision-Language-Action (VLA) models advance robotic control via strong visual-linguistic priors. However, existing VLAs predominantly frame pretraining as supervised behavior cloning, overlooking the fundamental nature of robot learning as a goal-reaching process that requires understanding temporal task progress. We present \textbf{PRTS} (\textbf{P}rimitive \textbf{R}easoning and \textbf{T}asking \textbf{S}ystem), a VLA foundation model that reformulates pretraining through Goal-Conditioned Reinforcement Learning. By treating language instructions as goals and employing contrastive reinforcement learning, PRTS learns a unified embedding space where the inner product of state-action and goal embeddings approximates the log-discounted goal occupancy, the probability of reaching the language-specified goal from the current state-action, quantitatively assessing physical feasibility beyond static semantic matching. PRTS draws this dense goal-reachability supervision directly from offline trajectories without reward annotations, and folds it into the VLM backbone via a role-aware causal mask, incurring negligible overhead over vanilla behavior cloning. This paradigm endows the high-level reasoning system with intrinsic goal reachability awareness, bridging semantic reasoning and temporal task progress, and further benefits goal-conditioned action prediction. Pretrained on 167B tokens of diverse manipulation and embodied-reasoning data, PRTS reaches state-of-the-art performance on LIBERO, LIBERO-Pro, LIBERO-Plus, SimplerEnv, and a real-world suite of 14 complex tasks, with particularly substantial gains on long-horizon, contact-rich, and zero-shot novel-instruction settings, confirming that injecting goal-reachability awareness significantly improves both execution success and long-horizon planning of general-purpose robotic foundation policies.

cs.AI↗
arXiv2026

Differentially Private Model Merging

In machine learning, privacy requirements at inference or deployment time often evolve due to changing policies, regulations, or user preferences. In this work, we aim to construct a magnitude of models to satisfy any target differential privacy (DP) requirement without additional training, given a set of existing models trained on the same dataset with different privacy/utility tradeoffs. We propose two post-processing techniques, namely random selection and linear combination, to generate final private models satisfying any target privacy parameter. We provide privacy accounting of these approaches from the lens of R'enyi DP and privacy loss distributions on general problems, as well as on private mean estimation, where we precisely characterize the privacy/utility tradeoffs and compare the two mechanisms. Empirically, we demonstrate the effectiveness of our approaches and validate our analyses on several models and both synthetic and real-world datasets.

cs.LG↗
arXiv2026

High-flux sub-Poissonian twin fields generation from warm atomic vapor

We demonstrate the generation of sub-Poissonian twin fields via near-degenerate spontaneous four-wave mixing (SFWM) in warm $^{85}\mathrm{Rb}$ vapor at 795~nm. When seeded with a weak coherent field, the generated twin beams exhibit approximately $5.5~\mathrm{dB}$ of intensity-difference squeezing in free space and retain about $3~\mathrm{dB}$ after coupling into polarization-maintaining (PM) fibers. Under vacuum seeding, time-resolved photon-counting measurements yield Mandel parameters of $Q\approx-0.7$ for each individual field, demonstrating strong photon-number squeezing. To explain these observations, we develop a finite-resource saturation model in which occupation-dependent SFWM gain, arising from competition for a finite nonlinear gain resource, suppresses large photon-number fluctuations within an effective collective mode selected by the PM-fiber spatial projection, thereby producing the observed negative Mandel-$Q$ parameters. The temporal cross-correlation between the twin photons exhibits a distinctive flat-topped profile resulting from the interplay of multiple $\chi^{(3)}$ processes in the atomic medium and is in excellent agreement with the theoretical model. Combining high photon flux, near-resonant operation, robust sub-Poissonian photon statistics, and fiber compatibility, this source provides a promising platform for scalable quantum-enhanced sensing and quantum information processing.

quant-ph↗
arXiv2026

Federation over Text: Insight Sharing for Multi-Agent Reasoning

We propose a federated learning-like framework, Federation over Text (FoT), that enables multiple clients solving different tasks to collectively generate a shared library of metacognitive insights by iteratively federating their local reasoning processes without sharing actual problem instances or task instructions. Instead of federation over gradients (e.g., as in distributed training), FoT operates at the semantic level without any gradient optimization or supervision signal. Iteratively, each client runs an LLM agent that does local thinking and self-improvement on their specific tasks independently, and shares reasoning traces with a central server, which aggregates and distills them into a cross-task (and cross-domain) insight library that existing and future agents can leverage to improve performance on related tasks. Experiments show that FoT improves reasoning effectiveness and efficiency across a wide range of challenging applications, including mathematical problem solving, cross-domain collaboration, real-world daily tasks, and machine learning research insight discovery. Specifically, it improves average performance scores by 25% while reducing the reasoning tokens by 4% across the first three applications. In the research insight discovery application, FoT is able to generate insights that cover over 80% of the major contributions in the subsequent papers.

cs.LG↗
arXiv2026

The Stellar IMF and Dark Matter Halo of ESO0286: Constraints from Strong Lensing and Dynamics

The internal mass structure of elliptical galaxies offers critical insights into galaxy formation, yet disentangling stellar mass from dark matter and determining the stellar initial mass function (IMF) remains challenging. We present a detailed analysis of ESO0286-G022 ($z=0.0312$), a rare nearby strong-lens system with a fast-rotating elliptical galaxy, combining high-resolution Hubble Space Telescope (HST) imaging with VLT/MUSE integral-field stellar kinematics. We construct axisymmetric and triaxial Schwarzschild orbit-superposition models to reconstruct its intrinsic shape and mass distribution. Despite being a fast rotator, ESO0286 exhibits clear kinematic signatures of intrinsic triaxiality, characterized by rotation along both the major and minor axes, making it only the second such confirmed case. By incorporating the mass enclosed within the Einstein radius from strong lensing as a complementary constraint, we tightly anchor the total mass at large radii. This significantly reduces the uncertainty on the outer mass profile and orbital structure, demonstrating that only models with strong radial anisotropy beyond the IFU field of view are compatible with the data. In the inner regions, we robustly constrain an upper limit for the stellar mass around $r \sim 0.7$ kpc, ruling out an IMF more bottom-heavy than Kroupa, though a gentle gradient toward a slightly heavier central IMF is permitted. This aligns with recent dynamical studies of local massive early-type galaxies but contrasts with heavier IMFs reported for lenses at $z>0.1$. Our work demonstrates the power of combining lensing and dynamical modeling to resolve the detailed inner structure of massive galaxies.

astro-ph.GA↗
arXiv2026

SLSim: a strong lensing population simulation package

Gravitational lensing offers unique insights into cosmology by bending light around massive objects. Strong gravitational lensing, in particular, produces magnified and often multiple images of distant sources, crucial for precise cosmological measurements and understanding the distribution of dark matter in the universe. Current studies are limited by the number of strong gravitational lenses. From upcoming cosmological surveys, we anticipate observing a several orders of magnitude increase in the number of lenses, for both static and transient phenomena. However, detecting and analyzing these events from vast surveys like Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) presents significant challenges. To prepare for these challenges, we introduce SLSim, a versatile simulation tool tailored for the Vera C. Rubin Observatory. SLSim integrates advanced astrophysical models with computational efficiency to generate synthetic strong lens populations under realistic observational conditions. SLSim simulates static and variable lensing scenarios, essential for cosmological studies, training and testing lens search and data analysis pipelines. This paper details SLSim,'s design and implementation, emphasizing its modularity and capabilities across various astrophysical regimes. Validation against observational data and existing simulations confirms SLSim's accuracy in reproducing observed lensing phenomena. SLSim is publicly available at https://github.com/LSST-strong-lensing/slsim, and we anticipate continued development and expansion of its capabilities. Users are encouraged to check the repository for updates and to contribute to ongoing community efforts in strong lensing simulations.

astro-ph.CO↗
arXiv2026

Joint Bayesian Source and Lens Reconstruction for Multi-messenger Binary Black Holes

If a gravitational wave event is lensed by a cluster or galaxy in our line-of-sight, it is expected that its host galaxy would also be lensed. Therefore, connecting lensed gravitational wave events even without direct optical counterpart could be feasible by identifying matching lenses in electromagnetic data and surveys. Seminal work has demonstrated the potential of this approach in LVK, Euclid, HST, JWST, and CSST mock data, motivating the need for a dedicated software package to perform such analyses in practice. Here, we present the alpha-version of silmarel, the first software package designed to bridge these cosmic signals and enable us analysis of real LVK gravitational-wave binaries together with telescope observations from instruments like \textit{Euclid} or \textit{Hubble} Space Telescope, and the future of multimessenger binary black hole lensing.

astro-ph.HE↗