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Lin Lin

Publications and source records attributed to Lin Lin.

At least 37 records · Page 2Linked to original sources
arXiv2026

Widespread Inflows Reveal Baryonic Cycling in Star-forming and Quiescent Galaxies

Cool-gas inflows, required to sustain star formation, have been fundamental in simulations yet remained observationally elusive. Using DESI spectroscopy of ~30,000 galaxies, we identify coherent inflowing gas (~100 km/s) in 20-50% of the sample, yielding a population-level census of gas flows. We uncover a striking inversion: inflows are detected in quiescent galaxies, whereas star-forming systems are dominated by gravitationally bound outflows. At fixed age, galaxies with inflows, outflows, or no/weak flows share similar masses, environments, and structures, indicating that these properties do not differentiate flow states. Instead, gas-flow state is linked to stellar population age and recent evolutionary history, consistent with age-dependent gas flows in two regimes. In some star-forming galaxies, elevated star formation surface densities drive outflows that recycle on ~0.5 Gyr timescales, consistent with a galactic fountain. In quiescent systems, low-level ``drizzling'' inflows persist, consistent with slowly cooling enriched halo gas and weak radio-mode nuclear activity. Broad gas-phase metallicity distributions---and absence of a pristine dilution signature---indicate that detected inflows are predominantly recycled or enriched. Detectability is modulated by dust, ionization, and geometry: in star-forming disks, inflowing gas lies near the disk plane and is obscured or ionized, while outflow hosts exhibit higher dust and metal content. As star formation declines, cold-outflow signatures weaken, and recycled or slowly cooling gas is more readily detected as inflow. Post-starburst galaxies provide snapshots of this transition. Our results resolve the scarcity of observed inflows, provide evidence for widespread gas accretion and recycling in present day galaxies, and establish an observational framework linking gas flows to star formation, chemical evolution, and galaxy structure.

astro-ph.GA↗
arXiv2026

Relaxation effects on Hartree-Fock ground states in twisted bilayer graphene at even integer fillings

A standard approach for studying magic angle twisted bilayer graphene (MATBG)'s correlated electronic phase diagram is to project the Coulomb interaction down to effective models only involving electrons in single-particle flat bands and some nearby remote bands. We provide a novel systematic derivation of a single-particle continuum model of MATBG's single-particle properties which incorporates structural relaxation while remaining in the Lagrangian frame. We project the Coulomb interactions down to electrons occupying the flat bands of this model and compute the Hartree-Fock many-body ground states at fillings $ν= \pm 2$. We find that incorporating relaxation effects drives the model into a semi-metallic phase at $- 2$ because of particle-hole asymmetry in the relaxed model's single-particle dispersion and because the flat band wavefunctions become more concentrated leading to an enhanced Hartree potential. Our results corroborate recent ab initio density functional theory studies which also found semi-metallic phases at $-2$. We discuss potential explanations for why such phases have not been seen in experiments.

cond-mat.mes-hall↗
arXiv2026

17 Yr of Magnetar Bursts Observed with the Fermi Gamma-ray Burst Monitor

The Fermi Gamma-ray Burst Monitor (GBM) has been in operation for over 17 years, during which it has observed more than a thousand bursts from soft gamma repeaters (SGRs), also known as magnetars. Serving as a laboratory for extreme physics, magnetars are a sub-family of neutron stars characterized by extreme magnetic field strength, observed through a combination of persistent and short transient emission across the electromagnetic spectrum. We present the comprehensive GBM catalog of SGR short bursts which supersedes the 5-year catalog of Collazzi et al. 2015. The new catalog contains 1254 SGR short bursts observed over 17 years, providing the longest uninterrupted, high-sensitivity all-sky monitoring of magnetar bursts with unprecedented spectral and temporal resolution. Our catalog contains bursts from 17 unique Galactic sources, with major contributions by bursts from SGR J1935+2154 and SGR J1550-5418. We present overall characteristics of these bursts, such as the durations, spectral parameters for various photon models, fluxes, as well as their comparison with recently published catalogs of other missions and the previous GBM magnetar catalog. The machine readable catalog, as well as burst spectra and response files are made publicly available for the community.

astro-ph.HE↗
arXiv2026

Constrained minimax approximation for quantum signal processing

Quantum signal processing (QSP) provides a simple and efficient framework for implementing polynomial transformations using quantum circuits. Its classical design stage leads to a constrained minimax approximation problem: find a polynomial of prescribed parity that approximates a target function uniformly on a fitting set while remaining bounded in magnitude by one on the domain $[0,1]$, which can be viewed as a semi-infinite constraint. Discretization converts the problem into a linear program, but feasibility at a set of finitely many sampled points does not ensure feasibility on the whole domain, especially when an optimal approximant reaches the boundary of the feasible set. We investigate two approaches to address this difficulty. A Remez exchange method combined with active-set constraint enforcement is efficient on many tested instances, but its stability depends on the target and problem geometry. We then introduce nonlinear Fourier retraction, which uses QSP completion and phase synthesis to turn a nearly feasible polynomial into phase factors for a feasible QSP polynomial without increasing the degree. Across representative problems, retraction largely preserves approximation accuracy and remains effective on instances where the Remez heuristic is unstable. The resulting workflow connects classical minimax approximation and semi-infinite optimization with nonlinear Fourier analysis, and is implemented in the qsppack software package.

quant-ph↗
arXiv2026

Mathematical and numerical analysis of quantum signal processing

Quantum signal processing (QSP) provides a representation of scalar polynomials of degree $d$ as products of matrices in $\mathrm{SU}(2)$, parameterized by $(d+1)$ real numbers known as phase factors. QSP is the mathematical foundation of quantum singular value transformation (QSVT), which is often regarded as one of the most important quantum algorithms of the past decade, with a wide range of applications in scientific computing, from Hamiltonian simulation to solving linear systems of equations and eigenvalue problems. In this article we survey recent advances in the mathematical and numerical analysis of QSP. In particular, we focus on its generalization beyond polynomials, the computational complexity of algorithms for phase factor evaluation, and the numerical stability of such algorithms. The resolution to some of these problems relies on an unexpected interplay between QSP, nonlinear Fourier analysis on $\mathrm{SU}(2)$, fast polynomial multiplications, and Gaussian elimination for matrices with displacement structure.

quant-ph↗
arXiv2026

Simple and efficient end-to-end quantum thermal and ground state preparation

Quantum computing algorithms for many-body physics, chemistry and materials science typically require the preparation of thermal or ground states for a given Hamiltonian. We propose quantum algorithms based on system-bath interactions to prepare thermal and ground states for a range of physically relevant Hamiltonians. These algorithms require only forward evolution under a system-bath Hamiltonian in which the bath is a single reusable ancilla qubit, making them especially well-suited for early fault-tolerant quantum devices. By carefully designing the bath and interaction Hamiltonians, we prove that the fixed point of the dynamics accurately approximates the desired quantum state. Furthermore, we establish theoretical guarantees on the mixing time, and thereby provide a rigorous justification for the end-to-end efficiency of system-bath interaction models in thermal and ground state preparation for the physically relevant models we consider.

quant-ph↗
arXiv2026

AuricularWorld: Hierarchical Action-Guided World Modeling for Fine-Grained Auricular Structure Segmentation from CT Scans

Fine-grained segmentation of auricular structures in CT is challenging because the ear occupies a small image region, cartilage boundaries are highly irregular, and interfaces between cartilage and surrounding soft tissues are often ambiguous. Clinical annotations may also include both composite structures containing cartilage and adjacent skin and their corresponding cartilage-only regions, producing nested and overlapping labels. We propose a world-model-based segmentation framework that enables iterative anatomical reasoning beyond conventional feed-forward prediction. Built on an encoder-decoder architecture, the framework introduces a deterministic recurrent state-space model into the intermediate latent space. Multi-scale encoder features and partially decoded representations are fused to form a structural observation that initializes the latent dynamics. During inference, the model performs a three-step latent rollout without ground-truth guidance. Hierarchical anatomical actions update the recurrent state and progressively refine the latent representation. The resulting latent trajectory is projected back into the decoder and combined with high-resolution features to produce the final segmentation. To learn reliable latent transitions, we introduce a balanced hierarchical action objective that addresses foreground sparsity, missing anatomical groups, and imbalance between add and remove operations. Extensive experiments show that the proposed framework consistently improves segmentation accuracy and reduces HD95 by more than 43% for small, irregular, and overlapping auricular structures in CT. These results demonstrate the effectiveness of latent world-model reasoning for challenging medical image segmentation.

cs.CV↗
arXiv2026

Retraction-Free Optimization over the Stiefel Manifold for the LoRA Fine-Tuning

Optimization over the Stiefel manifold plays a significant role in various machine learning tasks. Existing methods either use the retraction operators, requiring costly orthonormalization for large-scale matrices, or employ landing methods that rely on careful step size selection and penalty parameter tuning. To address these challenges, we propose a retraction-free and penalty parameter-free algorithm that directly lands on the manifold. By leveraging the strongly-convex-like property of the quadratic penalty function and the proximal smoothness of the Stiefel manifold, we establish global convergence guarantees with the best-known iteration complexities under both constant and diminishing step sizes. Then, we reformulate the low-rank adaptation (LoRA) fine-tuning problem for large language models as a manifold optimization problem, introducing Manifold-LoRA for geometry-accelerated adaptation. This approach employs the proposed landing technique and a carefully designed step size strategy to accelerate the training process. Numerical experiments on benchmark datasets demonstrate the efficiency and strong downstream performance of the proposed method.

cs.LG↗
arXiv2026

Spectral Gap of the Davies Generator for the Mean-Field Heisenberg Model

The mean-field Heisenberg ferromagnet is a quantum spin model on the complete graph with isotropic spin-1/2 interactions. This non-commuting Hamiltonian is permutation and $\mathsf{SU}(2)$ invariant, and its Gibbs states undergo an $\mathsf{SU}(2)$ symmetry breaking phase transition at inverse temperature $β=2$. We consider the associated Davies generator, a canonical model of open-system thermalization, and prove tight asymptotic estimates for its spectral gap at all noncritical temperatures. For fixed $β<2$, the gap as a function of number of qubits $n$ is $Θ(1)$, while for fixed $β>2$ the gap is $Θ(n^{-1})$. The matching upper bound of the spectral gap is witnessed by the total magnetization order parameter, suggesting that the low-temperature ($β>2$) slowdown is associated with broken continuous symmetry. Two key ingredients in our approach are a comparison argument, which introduces auxiliary generators to bound dissipation on nontrivial representations of the symmetry groups $\mathsf{SU}(2)$ and $\mathsf{S}_n$, and a decomposition of the space of observables into spherical tensor operators to reveal a form of monotonicity.

quant-ph↗
arXiv2026

Dissipative phase decision without ground-state preparation

We propose a dynamical approach to identifying ground-state quantum phases through short-time dissipative cooling. Rather than determining the phase by preparing highly accurate approximations to ground states, we prepare a representative state of a candidate phase and monitor the early-time response of phase-sensitive observables under cooling dynamics tailored to the target Hamiltonian. For a class of phase-decision problems in which the relevant observables can be inferred from the low-energy manifold, and with jump operators implementable using only short-time Hamiltonian simulation, the dissipative evolution rapidly suppresses high-energy components and drives the system into a low-energy manifold whose observables already reveal the underlying ground-state phase, well before mixing to the steady state. We demonstrate this strategy for the frustrated $J_1$--$J_2$ Heisenberg chain, the Kitaev honeycomb model, and the XXZ chain, including Berezinskii--Kosterlitz--Thouless and topological phase transitions. In particular, coarse filter resolutions and short evolution times suffice to recover phase-sensitive quantities such as the Luttinger parameter and topological diagnostics. We further provide theoretical justification that cooling dynamics with such jump operators can rigorously prepare low-energy manifolds for free-fermionic and free-bosonic systems, and investigate this mechanism for interacting fermionic systems. Our results suggest that phase decision is a plausible target for future utility-scale studies on early fault-tolerant quantum devices.

quant-ph↗
arXiv2026

Canonical Variates in Wasserstein Metric Space

In this paper, we address the classification of instances represented by distributions on a vector space rather than single points. We consider classification algorithms based on pairwise distances, specifically, the Wasserstein metric between distributions. Central to our investigation is dimension reduction within the Wasserstein metric space to enhance classification accuracy. We introduce a novel approach grounded in the principle of maximizing Fisher's ratio, defined as the quotient of between-class variation to within-class variation. The directions in which this ratio is maximized are termed discriminant coordinates or canonical variates axes. In practice, both between-class and within-class variations are defined as the average squared Wasserstein distances between pairs of distributions, with the pairs either belonging to the same class or to different classes. This ratio optimization is achieved through an iterative algorithm, which alternates between optimal transport and maximization steps within the vector space. Empirical studies are conducted to assess the algorithm's convergence; and experimental results demonstrate that the dimension reduction technique substantially enhances classification performance. Moreover, the new method outperforms well-established algorithms that operate on vector representations derived from distributional data. It also exhibits robustness to variations in how instances are summarized by distributions, such as the number of components in a Gaussian mixture model (GMM) representation.

stat.ML↗
arXiv2026

The Narrow-Line Seyfert 1 Phenomenon: Accretion State Versus Host Galaxy Properties

The physical origin of the narrow-line Seyfert 1 (NLS1) and broad-line Seyfert 1 (BLS1) dichotomy remains debated, with competing scenarios invoking host-galaxy evolution or intrinsic accretion physics. We analysed host-galaxy properties and AGN luminosities obtained from CIGALE spectral energy distribution fitting for $\sim$12,000 Type 1 AGNs from the Sloan Digital Sky Survey, of which 29\% are NLS1s. Globally, NLS1s have lower virial black hole masses, higher inferred Eddington ratios, lower stellar masses, and higher specific star formation rates than BLS1s. In the FWHM(\Hb)--$L_{\rm AGN}$ plane, the conventional 2000 km s$^{-1}$ boundary is better viewed as an empirical division within a continuous parameter space rather than a physical threshold, with Fe~II tracing the high-accretion end. In a host-matched subsample of 767 NLS1--BLS1 pairs with statistically indistinguishable stellar mass, black hole mass, and redshift, NLS1s still show higher Eddington ratios, stronger Fe~II emission, and bluer optical continua, together with elevated SFR and dust attenuation, suggesting that the NLS1 phenomenon is most naturally associated with a high-accretion state within the continuous distribution of Type 1 AGNs, while host-galaxy gas supply may also play a role in modulating its strength. In this picture, NLS1 and BLS1 classifications reflect different locations within a continuous accretion sequence of the same underlying population rather than two physically disjoint classes.

astro-ph.GA↗
arXiv2026

A Sketch-and-Project Analysis of Subsampled Natural Gradient Algorithms

Subsampled natural gradient descent (SNG) has been used to enable high-precision scientific machine learning, but standard analyses based on stochastic preconditioning fail to provide insight into realistic small-sample settings. We overcome this limitation by instead analyzing SNG as a sketch-and-project method. Motivated by this lens, we discard the usual theoretical proxy which decouples gradients and preconditioners using two independent mini-batches, and we replace it with a new proxy based on squared volume sampling. Under this new proxy we show that the expectation of the SNG direction becomes equal to a preconditioned gradient descent step even in the presence of coupling, leading to (i) global convergence guarantees when using a single mini-batch of any size, and (ii) an explicit characterization of the convergence rate in terms of quantities related to the sketch-and-project structure. These findings in turn yield new insights into small-sample settings, for example by suggesting that the advantage of SNG over SGD is that it can more effectively exploit spectral decay in the model Jacobian. We also extend these ideas to explain a popular structured momentum scheme for SNG, known as SPRING, by showing that it arises naturally from accelerated sketch-and-project methods.

cs.LG↗
arXiv2026

One for All: A Non-Linear Transformer can Enable Cross-Domain Generalization for In-Context Reinforcement Learning

A central challenge in reinforcement learning (RL) is to learn models that generalize beyond the tasks on which they are trained, a goal traditionally pursued through multi-task and meta RL. Recently, transformer architectures have emerged as a promising approach, enabling adaptation to new tasks via in-context learning without explicit parameter updates. From a functional perspective, a transformer can be viewed as a functional operator that maps a context to a task-specific function. It is thus fundamental to understand and design this operator to support stronger generalization in RL. In this work, we address this resulting question of generalization from a kernel-based perspective by establishing a connection between non-linear transformers and kernel-based temporal difference learning. By interpreting the transformer as performing regression in a Reproducing Kernel Hilbert Space (RKHS), we show that value functions from different domains can be represented using a shared set of weights, provided they lie within the same RKHS. Experiments on multiple MetaWorld domains support this interpretation, demonstrating convergence of the temporal-difference objective.

cs.LG↗
arXiv2026

Quantum-Enhanced Dark Matter Search Using Cat States

Quantum metrology has recently emerged as a powerful approach for dark matter (DM) searches, particularly using nonclassical bosonic states in microwave cavities that are sensitive to weak signals. Nonclassical cat states - macroscopic superpositions of coherent states featuring sub-Planck interference structures - offer promising advantages for high-precision measurements. However, their practical utility in DM search remains unexplored. Here, we report the first experimental application of four-component cat states within a high-quality superconducting microwave cavity to search for dark photons, a potential DM candidate. We demonstrate an 8.1-fold enhancement in the signal photon rate and constrain the dark photon kinetic mixing angle to an unprecedented $ε< 7.32 \times 10^{-16}$ near 6.44~GHz (26.6~$μ$eV). By employing a parametric sideband drive to actively tune the cavity frequency, we achieve dark photon searches and background subtraction across multiple frequency bins, yielding a sensitivity at the $10^{-16}$ level within a 100~kHz bandwidth. Our cat-assisted DM (CaD) search and frequency-scanning techniques demonstrate substantial improvements over previous results, promising potential implications in quantum-enhanced searches for new physics.

quant-ph↗
arXiv2026

Quantum Filtering and Analysis of Multiplicities in Eigenvalue Spectra

Fine-grained spectral properties of quantum Hamiltonians, including both eigenvalues and their multiplicities, provide useful information for characterizing many-body quantum systems as well as for understanding phenomena such as topological order. Extracting such information with small additive error is $\#\textsf{BQP}$-complete in the worst case. In this work, we introduce QFAMES (Quantum Filtering and Analysis of Multiplicities in Eigenvalue Spectra), a quantum algorithm that efficiently identifies clusters of closely spaced dominant eigenvalues and determines their multiplicities under physically motivated assumptions, which allows us to bypass worst-case complexity barriers. QFAMES also enables the estimation of observable expectation values within targeted energy clusters, providing a powerful tool for studying quantum phase transitions and other physical properties. We validate the effectiveness of QFAMES through numerical demonstrations, including its applications to characterizing quantum phases in the transverse-field Ising model and estimating the ground-state degeneracy of a topologically ordered phase in the two-dimensional toric code model. We also generalize QFAMES to the setting of mixed initial states. Our approach offers rigorous theoretical guarantees and significant advantages over existing subspace-based quantum spectral analysis methods, particularly in terms of the sample complexity and the ability to resolve degeneracies.

quant-ph↗
arXiv2026

FAST Polarization Catalog of FRB 20240114A

Polarization measurements of fast radio bursts (FRBs) probe the magnetized plasma surrounding their central engines. FRB~20240114A is an exceptionally active repeating source, with 17,356 bursts detected between 2024 January 28 and 2025 May 30 by FAST, enabling time-resolved polarimetric studies. In this work, we present a polarimetric catalog of 6,131 bright bursts (with a signal-to-noise ratio S/N $\geq$ 20, 35.3% of the total sample), including arrival time (MJD$_{\text{topo}}$), dispersion measure (DM), burst width (W$_{\text{eff}}$), bandwidth, Faraday rotation measure (RM), linear and circular polarization degrees (DOL, DOC), and intrinsic polarization angle (PA$_0$). We detect a clear temporal evolution of RM: after an initial stable phase, it decreases linearly by $\sim$200 $\rm rad\ m^{-2}$ over 200 days, forming a bimodal distribution, whereas DM remains stable at 528.9 $\rm pc\ cm^{-3}$. The linear polarization fraction is generally high, with the 3$σ$ lower bound around 76%, while circular polarization is low, with 1,157 of 17,356 bursts (6.67%) having DOC $\geq$10%. We perform a power-law fit between $|\textrm{V}|$/I and $|\textrm{RM}|$, which yields an index of $-2.98 \pm 0.80$. It is found that the combined 2D distribution of L/I versus V/I remains stable, implying that the emission mechanism is largely invariant. Our PA$_0$ measurements show a broad, non-uniform distribution, implying a complex emission geometry. These results suggest that FRB~20240114A resides in a dynamically evolving magneto-ionic environment. This catalog provides a foundation for studies of repeating FRB progenitors and their environments.

astro-ph.HE↗
arXiv2026

Resolving the 2024 Outburst of Magnetar 1E 1841-045 from its host Supernova Remnant with EP-FXT

The magnetar 1E 1841-045 exhibited a new active episode starting on August 20, 2024, marked by X-ray bursts and enhanced persistent emission. Using data from the Einstein Probe (EP), we report on the timing and spectral results following the onset of this outburst. The pulse profile displays a multi-peaked structure, with notable phase shifts in the secondary peak. Energy-resolved pulse profile analysis indicates a transition in the dominant peak of the pulse profile above 5.8 keV. The 0.5-10 keV X-ray spectrum is well-modeled by a combined blackbody and power-law (BB+PL) model, showing a $\sim 20\%$ flux increase following the outburst. Phase-resolved spectroscopy indicates a correlation between BB temperature and pulse profile intensity, along with spectral hardening at a specific pulse phase. The high spatial resolution of EP enables effective separation of the supernova remnant emission, which is crucial for measuring the intrinsic pulse emission of the source. These findings underscore the intricate relationship between magnetar outbursts, pulse profile evolution, and spectral characteristics.

astro-ph.HE↗