arXiv:2603.17489v4 Announce Type: replace
Abstract: We present an approximation notion for NP-hard optimization problems. The notion is based on an *amortized relaxation*: the relaxed optimum of an input is the largest per-copy value attainable when many copies of the input are solved together. We prove that (assuming P != NP) the new notion is strictly stronger than FPTAS, but strictly weaker than having a polynomial-time algorithm. Our results introduce a new computational complexity class for optimization problems, which is a strict superset of P and a strict subset of FPTAS.
Science Journals
arXiv:2607.09197v1 Announce Type: new
Abstract: Routing policies for multi-model systems are evaluated almost exclusively on task accuracy and inference cost. We argue that two properties, orthogonal to performance, determine whether routing is meaningful. First, the society of actors must be behaviourally differentiated: if all actors respond identically, routing is vacuous. Second, the routing policy must be stable: surface-form variants of a query should be assigned to the same actor. High task accuracy is compatible with violating both properties, since a router can operate over a redundant society or assign queries inconsistently, preventing specialisation regardless of performance. We adapt Hierarchic Social Entropy (HSE) to language-model societies and introduce a perturbation-based robustness metric to diagnose these failure modes. Applied to EmbedLLM and RouterBench, we find that HSE exhibits strong diminishing returns, suggesting that a curated subset of fewer than ten agents recovers most available diversity in a large pool -- a practical coreset heuristic for society design. We further find that KNN routers gain accuracy from specialist societies but collapse in robustness under perturbation, while prompted routing remains stable across all perturbation types -- illustrating that accuracy and meaningfulness can sharply diverge.
arXiv:2607.08806v1 Announce Type: new
Abstract: Heterogeneous graph built on notes, lyric syllables, and accompaniment events is a natural representation of symbolic music score, providing a substrate for both philological analysis and computational tasks. Music features are therefore well-captured by graph geometry and its properties. This representation has proved effective for analytical tasks as cadence detection, voice separation, and stylistic classification. In the present work, the reduction of harmonic complexity of a music score on graph, by preserving task-relevant information, relation between notes, and graph structure is investigated. A compression scheme for the piano subgraph of vocal-pianistic scores, built on binary wedge partitioning trees, is proposed. The wedges are generated through a fully adaptive greedy algorithm that recursively minimizes the $L^2$-error within a six-dimensional Tonnetz embedding of musical notes. The partitioning process employs a splitting criterion based on harmonic distance, resulting in regions that accurately reflect the intrinsic harmonic relationships among notes. The reconstructed music scores obtained through piecewise-constant functions and the mean values of the notes inside each wedge are used as a new simplified scores human-readable and playable. Some experiments on a corpus of symbolic music scores of three different composers are performed to assess the proposed approach.
arXiv:2603.05361v2 Announce Type: replace
Abstract: 9-1-1 call-taking training requires mastery of over a thousand interdependent skills, covering diverse incident types and protocol-specific nuances. A nationwide labor shortage is already straining training capacity, but effective instruction still demands that trainers tailor objectives to each trainee's evolving competencies. This personalization burden is one that current practice cannot scale. Partnering with Metro Nashville Department of Emergency Communications (MNDEC), we propose PACE (Personalized Adaptive Curriculum Engine), a co-pilot system that augments trainer decision-making by (1) maintaining probabilistic beliefs over trainee skill states, (2) modeling individual learning and forgetting dynamics, and (3) recommending training scenarios that balance acquisition of new competencies with retention of existing ones. PACE propagates evidence over a structured skill graph to accelerate diagnostic coverage and applies contextual bandits to select scenarios that target gaps the trainee is prepared to address. Empirical results show that PACE achieves 19.50% faster time-to-competence and 10.95% higher terminal mastery compared to state-of-the-art frameworks. Co-pilot studies with practicing training officers further demonstrate a 95.45% alignment rate between PACE's and experts' pedagogical judgments on real-world cases. Under estimation, PACE cuts turnaround time to merely 34 seconds from 11.58 minutes, up to 95.08% reduction.
arXiv:2607.08930v1 Announce Type: new
Abstract: Efficient serving of diffusion large language models (dLLMs) is hindered by convergence heterogeneity: when batching multiple requests, different sequences converge at different rates, causing faster requests to stall behind slower stragglers and introducing compute bubbles and tail latency. We present BlockServe, a continuous batching framework that integrates block-grained scheduling -- immediately evicting completed requests at block boundaries -- with mixed-state execution that extends dual cache and parallel decoding to heterogeneous batches via gather-scatter indexing. Furthermore, a compute-aware admission controller expands effective batch capacity through token-budgeted refill. On Dream and LLaDA across five benchmarks, BlockServe achieves 1.9--10.6$\times$ throughput over Fast-dLLM with comparable generation quality, establishing block-grained scheduling as a foundation for high-throughput offline dLLM inference.
arXiv:2607.08837v1 Announce Type: new
Abstract: Exploration is essential to RL since a policy cannot improve by repeatedly sampling the behaviors it already prefers. Standard methods inject stochasticity in the action space, but such jitter only yields rollouts close to the original. Escaping a weak policy often requires global perturbations that action noise cannot produce. Large language models (LLMs) and vision-language-action (VLA) models offer a pathway: they condition the policy on a natural language prompt, and since the rollout follows from it, modifying the prompt induces global changes. The challenge is finding prompts that induce useful global changes. With a weak policy that rarely succeeds, reward is too sparse to select on. Our idea is to refine prompts from the rollouts themselves: a vision-language model (VLM) reasons over the rollout video, diagnoses how the policy responded, and rewrites the prompt to elicit better behavior next time. This procedure realizes posterior sampling, a classical RL exploration framework, at the level of prompts: the VLM maintains an implicit distribution over useful prompts and updates it from observed rollouts. We call this strategy Prompt-Driven Exploration (PDE). Across manipulation and reasoning tasks, PDE enables RL to learn successful policies even from zero-reward starts, and improves sample efficiency more broadly. Our website is available at https://xinyunsunshine.github.io/prompt-rl.
arXiv:2603.12939v2 Announce Type: replace
Abstract: Enabling reliable long-horizon robotic manipulation is a crucial step toward open-world embodied intelligence. However, VLM-based planners treat each step as an isolated observation-to-action mapping, forcing them to reinfer scene geometry from raw pixels at every decision point while remaining unaware of how prior actions have reshaped the environment. Despite strong short-horizon performance, these systems lack the spatio-temporal reasoning required for persistent geometric anchoring and memory of action-triggered state transitions. Without persistent state tracking, perceptual errors accumulate across the execution horizon, temporarily occluded objects are catastrophically forgotten, and these compounding failures lead to precondition violations that cascade through subsequent steps. In contrast, humans maintain a persistent mental model that continuously tracks spatial relations and action consequences across interactions rather than reconstructing them at each instant. Inspired by this human capacity for causal spatio-temporal reasoning with persistent memory, we propose RoboStream, a training-free framework that achieves geometric anchoring through Spatio-Temporal Fusion Tokens (STF-Tokens), which bind visual evidence to 3D geometric attributes for persistent object grounding, and maintains causal continuity via a Causal Spatio-Temporal Graph (CSTG) that records action-triggered state transitions across steps. This design enables the planner to trace causal chains and preserve object permanence under occlusion without additional training or fine-tuning. RoboStream achieves 90.5% on long-horizon RLBench and 44.4% on challenging real-world block-building tasks, where both SoFar and VoxPoser score 11.1%, demonstrating that spatio-temporal reasoning and causal memory are critical missing components for reliable long-horizon manipulation.
arXiv:2607.09249v1 Announce Type: new
Abstract: As power systems transition toward inverter-based resource (IBR)-dominated grids, traditional system strength definitions and metrics are becoming increasingly inadequate to characterize upcoming stability challenges. Emerging definitions characterize system strength in terms of "voltage source behind impedance (VSBI)" characteristics. Similarly, Grid-ForMing (GFM) IBRs are expected to contribute voltage stiffness by exhibiting near-constant VSBI characteristics in the (sub-)transient time frame. To quantify VSBI characteristics as a measure of system strength or grid-forming capability, this paper proposes the Jacobian Voltage Stiffness Metric (JVSM), derived from the frequency-domain Jacobian. JVSM provides a measure of both small-signal voltage magnitude and phase-angle stiffness. JVSM is demonstrated to serve as a compliance criterion for evaluating the VSBI characteristics of GFM IBRs. When applied for grid strength assessment, it more effectively identifies small-signal stability problems than state-of-the-art strength metrics. The proposed JVSM is validated through electromagnetic transient simulation case studies using the National Laboratory of the Rockies (NLR, formerly NREL) and WECC-approved industry-standard GFM IBR models and on a modified IEEE 39-bus system.
arXiv:2607.09582v1 Announce Type: new
Abstract: We present a physics-constrained machine learning framework for accelerating the direct numerical simulation (DNS) of turbulent reacting flows. The model replaces the direct evaluation of detailed chemical source terms with a surrogate that predicts reaction rates from a reduced thermochemical state. To improve physical consistency, the second law of thermodynamics is incorporated as a training constraint by enforcing non-negative entropy generation, which restricts the evolution of the thermochemical state to physically admissible directions and improves stability during time integration. The approach is demonstrated on DNS of a two-dimensional planar lean premixed methane-air flame interacting with a turbulent flow field. The model reproduces detailed-chemistry results with high fidelity while achieving more than an order-of-magnitude reduction in computational cost. Furthermore, a residual-based synthetic data augmentation strategy enables parametric exploration by constructing new training data from the original dataset, allowing accurate simulation at new inlet conditions without additional detailed-chemistry CFD runs. These results demonstrate that thermodynamically constrained machine learning can provide reliable and computationally efficient surrogates for detailed chemistry in high-fidelity combustion simulations.
arXiv:2607.08843v1 Announce Type: new
Abstract: In artificial and biological neural networks, concepts are often encoded as consistent linear directions in representation space. In deep learning, this idea is known as the linear representation hypothesis and underpins many interpretability and control methods based on linear probes, from concept detection to activation steering. Yet while prior work has studied whether such directions should exist $\textit{after}$ training, the dynamics of how they emerge $\textit{during}$ training remain poorly understood. Here, we develop a framework to study the alignment of concept directions during training - a process we call "abstraction". In a minimal linear network setting, we obtain exact solutions for the full trajectory of abstraction. These solutions reveal key analytic principles governing abstraction: (i) data and target geometry jointly determine abstraction at the end-of-learning, (ii) abstraction improves with network depth, and (iii) initialization scale controls the maximum abstraction reached during training. Extending our theory to nonlinear networks, we analyze how the choice of nonlinearity affects abstraction dynamics: erf networks approximate the linear theory, while abstraction in ReLU networks depends less on target geometry and more on input geometry. Across both, we prove a striking attenuation law: both nonlinearities weaken abstraction in activations relative to preactivations. We find evidence for this law in open models (DINOv3, Gemma 4) and apply our theory to improve linear probe generalization in LLMs. Together, our results provide a dynamical theory of abstraction with implications for interpretability and control.
arXiv:2607.09024v1 Announce Type: new
Abstract: Driven by next-token prediction, NLP shifted from task-specific models into powerful generalist foundation models. What, then, is the equivalent catalyst needed to achieve a general-purpose model in computer vision? In this paper, we contend that large-scale text-to-video generation serves as a strong pre-training paradigm for computer vision, providing the necessary spatiotemporal priors, vision-language alignment, and scalability required for general visual intelligence. We introduce GenCeption, which leverages a pre-trained video generative diffusion backbone to define a feed-forward perception model, capable of performing various vision tasks steered by text instructions. Empirical results demonstrate that GenCeption achieves state-of-the-art performance across a diverse suite of tasks, including depth, surface normal, and camera pose estimation, expression-referring segmentation, and 3D keypoint prediction, often matching or surpassing specialized models (e.g. DepthAnything3, SAM3, D4RT, VGGT-Omega, Sapiens, David, Genmo, and Lotus-2). Furthermore, the video generative pretrained backbone outperforms alternative pretraining paradigms (e.g., V-JEPA, and Video MAE) under comparable settings. Importantly, GenCeption exhibits preliminary data and model scaling properties along with exceptional data efficiency, where it achieves comparable performance with leading models like D4RT and VGGT-Omega with 7 to 500 less training data. Finally, GenCeption also exhibits intriguing emergent behaviors: a model trained exclusively on synthetic human videos generalizes to real-world footage and out-of-distribution object categories (e.g., animals and robots). These findings suggest that video generation is not merely a synthesis tool, but a foundational path toward generalist vision intelligence for the physical world. Project page: https://genception.github.io
arXiv:2607.08848v1 Announce Type: new
Abstract: One of the most effective means to precondition the electric field integral equation (EFIE) discretized with Rao-Wilton-Glisson (RWG) functions is the multiplicative Calder\'on preconditioner employing Buffa-Christiansen (BC) functions as a basis dual to the RWG basis. It results in a formulation that is free from the dense-discretization and the low-frequency breakdown. To generalize the multiplicative Calder\'on preconditioner from the low-order BC and RWG basis to higher orders, we utilize B-spline-based basis functions and establish the first explicit high-order dual basis. It can be regarded as a generalization of the BC functions to arbitrary polynomial degrees and constitutes a fundamental building block for other approaches that rely on a dual basis. Numerical results for the obtained preconditioner demonstrate a low and constant number of generalized minimum residual (GMRES) iterations independent of the number of unknonws and the polynomial degree for canonical and realistic perfectly electrically conducting (PEC) scatterers; a key to enable the full potential of higher-order bases.
arXiv:2607.09284v1 Announce Type: new
Abstract: Generally, monocular methods capture rich contextual priors but lack geometric precision, whereas stereo methods are geometrically accurate yet struggle in textureless and occluded regions. Several approaches attempt to combine their strengths to enhance the generalization of stereo matching (SM) by aligning monocular depth with stereo information. However, establishing a stable and generalizable alignment is challenging, and unreliable monocular cues can substantially degrade performance. This paper rethinks monocular depth embedding. First, to prevent shortcut learning, we reduce branch coupling instead of expanding network width. Second, we construct soft constraints instead of hard ones from monocular depth to improve tolerance to monocular depth errors. Based on the principles, we integrate monocular information into both feature extraction and GRU iterations. Specifically, the monocular depth map is fused with the RGB image to sharpen depth boundary perception and suppress matching ambiguities. The fused image is then used for feature extraction, allowing the contextual features to encode global geometric information. Furthermore, the monocular depth gradient feature is employed to guide disparity updates, helping to escape local oscillations. Finally, to address the boundary blurring of supervised disparity caused by data augmentation, we propose an edge confidence estimation method and an edge-aware loss function. Our method achieves state-of-the-art (SOTA) performance on multiple standard benchmarks, demonstrating excellent generalization while improving accuracy. The code is available at https://github.com/linliboabc-maker/stereo-matching-digital.
arXiv:2607.09382v1 Announce Type: new
Abstract: This work aims to develop a fast and physically consistent surrogate model for real-time structural health monitoring of fractured elastic domains. We propose a physics-informed DeepONet framework that predicts displacement fields from both boundary conditions and fracture geometry, using a dedicated encoding strategy for the latter and without relying on finite-element-generated training data. The traction-free condition on the fracture boundary is imposed weakly through a localized penalty term. The presented numerical example focuses on one representative fracture geometry, demonstrating the feasibility of the formulation and laying the groundwork for extensions to surrogate modeling across diverse fracture geometries.
arXiv:2607.09600v1 Announce Type: new
Abstract: Enhancing the reasoning capabilities of large language model (LLM) agents requires effective orchestration of diverse expert models and tools. However, existing frameworks typically call APIs based on coarse-grained matching between tasks and the functions of expert models or tools, while overlooking critical factors such as performance variability and cost efficiency among functionally similar alternatives. To address this, we propose Agora, a framework that introduces an incentive-compatible auction mechanism for dynamically allocating tasks to expert models and tools. By treating reasoning steps as tradeable items, Agora enables agents to bid based on their rectified competence-ensuring that critical logic is routed to the most capable solver rather than the most overconfident one. Evaluations across five benchmarks show that Agora improves over matched single-model, routing, and cascade baselines under comparable candidate pools, while exposing a controllable cost-quality trade-off through a single auction parameter.
arXiv:2604.05661v2 Announce Type: replace
Abstract: We provide improved space-time tradeoffs for permutation problems over additively idempotent semi-rings. In particular, there is an algorithm for the Traveling Salesperson Problem that solves $N$-vertex instances using space $S$ and time $T$ where $S\cdot T \leq 3.1861^{N}$. This improves a previous work by Koivisto and Parviainen [SODA'10] where $S\cdot T \leq 3.9271^N$, and overcomes a barrier they identified, as their bound was shown to be optimal within their framework.
To get our results, we introduce a new parameter of a set system that we call the chain efficiency. This relates the number of maximal chains contained in the set system with the cardinality of the system. We show that set systems of high efficiency imply efficient space-time tradeoffs for permutation problems, and give constructions of set systems with high chain efficiency, disproving a conjecture by Johnson, Leader and Russel [Comb. Probab. Comput.'15].
Proof-of-Continuity: A Temporal Model for Authority Propagation in Distributed Systems and AI Agents
arXiv:2607.08906v1 Announce Type: new
Abstract: Proof-of-Possession authorization models derive authority from the possession of artifacts such as tokens, credentials, or capabilities. This paper argues that possession is insufficient for discrete execution chains, whether they span multiple services or occur as separated steps within the same machine, because it does not guarantee preservation of the causal relationship between the origin of a request and the authority exercised at later steps. We introduce Proof-of-Continuity, a minimal authority-propagation discipline for the Provenance Identity Continuity (PIC) model, in which each execution step must be causally linked to the previous step and may only propagate a non-expansive subset of the authority received from the origin. It introduces Proof of Relationship, a single-hop causal primitive whose transitive composition is Proof-of-Continuity; these complement Proof-of-Possession rather than replace it. Under this model, the confused deputy condition cannot be satisfied as valid model behavior: any privilege exercised at a later step must already be present in the origin authority context. This is directly relevant to distributed systems and AI agents, where executors invoke tools and downstream services while holding multiple authority sources, so that the same authority/causality mismatch recurs across service boundaries. Under Proof-of-Continuity these sources may be carried together but are never merged into a combined authority, since each step is authorized only against the authority context of the lineage that caused it.
This paper concerns authorization propagation rather than authentication: identity and authentication mechanisms such as OIDC, verifiable credentials, wallets, and workload identity remain complementary mechanisms for establishing the origin, while Proof-of-Continuity addresses how authority propagates after that origin exists.
arXiv:2607.09434v1 Announce Type: new
Abstract: Recent advances in generative AI tools have significantly changed how software professionals write, evaluate, and interact with code. Generative AI tools such as GitHub Copilot, ChatGPT, and Claude are increasingly being integrated into everyday workflows. Despite the growing adoption of and reliance on these tools, it remains unclear as to how software professionals evaluate the code they generate. To explore this topic, we will conduct a constructivist grounded theory study that incorporates a survey, semi-structured interviews, and laddering interviews. With the initial survey data collection complete, we aim to interview 20--50 software professionals iteratively until theoretical saturation is achieved. This research aims to build a theory of how software professionals evaluate AI-generated code, grounded in their accounts of evaluative practices, perceptions, and preferences.
arXiv:2605.14563v2 Announce Type: replace
Abstract: Automated code documentation is essential for modern software development, providing the contextual grounding that both human developers and coding agents rely on to navigate large codebases. Existing repository-level approaches process components independently, causing redundant retrieval and conflicting descriptions across documents while producing outputs that lack hierarchical structure. Therefore, we propose MemDocAgent, a long-horizon agentic framework that generates documentation within a single, integrated context spanning the entire repository. It combines two components: (i) Dependency-Aware Traversal Guiding that predetermines a traversal order respecting dependency and granularity hierarchies; (ii) Memory-Guided Agentic Interaction, in which the agent interacts with RepoMemory, a shared memory accumulating prior work traces through read, write, and verify operations. Through an in-depth multi-criteria evaluation, MemDocAgent achieves the best performance over both open and closed-source baselines and demonstrates practical applicability in real software development workflows.
arXiv:2607.08912v1 Announce Type: new
Abstract: Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a multimodal LLM (e.g., recognition of common meme templates and cultural references) to convey intent, timing, and implicit meaning through conversational interaction. We evaluate MemeBuddy in a user study with 14 blind participants. Results show that dialog-style meme representations consistently improve engagement and user satisfaction compared to caption-style descriptions, while maintaining comparable comprehension.
arXiv:2607.08915v1 Announce Type: new
Abstract: Missing data is ubiquitous in real-world datasets. Traditional methods either discard incomplete samples or apply imputation techniques that ignore potentially informative missingness patterns, implicitly assuming that missingness occurs randomly. However, missingness patterns might provide additional information. We propose pattern-aware graph neural networks that explicitly encode which features are missing alongside observed values. We used four encoding strategies -- learned embeddings, frozen random embeddings, statistical features, and hierarchical representations -- across seven UCI datasets with naturally occurring missingness. Our Pattern-aware methods achieve substantial improvements over baselines, with an average improvement of 17\% in balanced accuracy and 22\% in F1-macro across all datasets. The benefits vary significantly by dataset: annealing shows dramatic improvement (+80\% balanced accuracy), while hepatitis and soybean show minimal gains (+4--5\%). Notably, even simple random pattern embeddings perform comparably to learned embeddings (0.650 vs 0.663 balanced accuracy), suggesting that distinguishing between patterns may be more important than task-specific optimization. Our ablation study reveals that attention mechanisms, while helpful, are not critical when pattern information is available -- simple mean aggregation with pattern awareness achieves 0.640 balanced accuracy compared to 0.645 for attention-based variants.
arXiv:2607.08922v1 Announce Type: new
Abstract: Molecule-surface interactions are central to many research and technological areas, spanning from heterogeneous catalysis and polymer science to electrochemistry. Of particular relevance are metallic nanogaps used in molecular electronics and near-field spectroscopy. Due to the buried nature of these double interfaces, few methods exist to monitor side-specific interactions and relative molecular orientation inside the gap. In this work, we introduce plasmon-enhanced nonlinear vibrational spectroscopy as an efficient tool to investigate surface molecular adsorption within metallic nanojunctions. By exploiting simultaneous vibrational sum- and difference-frequency generation in dual-resonant nanocavities, we resolve molecular orientation and preferential binding to one of the two gold surfaces, with few-molecule sensitivity. We also discover that the non-resonant (electronic) second-order nonlinear response is not an intrinsic property of the metal surface, but is instead governed by the molecule-surface interaction. Our findings provide a powerful analytical tool, easily implementable as an add-on to Raman spectroscopy, thanks to commercially available mid-infrared quantum cascade lasers.
arXiv:2607.09402v1 Announce Type: new
Abstract: Deep learning models dependency on large-scale inertial datasets presents a significant bottleneck in inertial sensor-based classification tasks, such as human activity recognition and smartphone location recognition. In these domains, data collection requires massive recording campaigns that are complex, time-consuming, and difficult to scale. Currently, data-driven guidelines for determining the minimum sample size required to reach a desired accuracy level do not exist. To address this gap, this study presents a systematic empirical evaluation of learning curve convergence rates in inertial classification. We introduce a unified framework that analyzes classification performance under both binary and multi-class scenarios, and derive an empirical formula to estimate performance relative to dataset size. Testing across six diverse, real-world datasets totaling 102.7 hours of inertial measurements demonstrates that accuracy follows a consistent logarithmic growth pattern, regardless of task complexity. Leveraging this finding, we propose a quantitative stability point metric, defined as the sample size required for the learning curve to stabilize within a predefined mean absolute percentage deviation of its asymptotic maximum. Our analysis reveals that models often reach practical stability with substantially fewer samples than traditional heuristics suggest. Ultimately, we offer a generalizable framework to extrapolate total data requirements from small-scale pilot studies, optimizing the tradeoff between recording effort and model reliability. These findings shift the prevailing paradigm from maximizing data volume toward optimizing data efficiency, offering concrete, data-backed guidelines for planning recording campaigns in inertial sensing applications.
arXiv:2607.08947v1 Announce Type: new
Abstract: The advent of expendable wave buoys has greatly expanded the data available for evaluating and calibrating wave models. Ideally, the newer buoys now drifting around the world's oceans would be merged with conventional time series measurements from moored buoys to form a consistent dataset of in situ observations. However, a comparison across several buoy types (moored Datawell, moored NDBC, and two types of drifting buoys) suggests large differences in the high frequency portion of the observed wave energy spectra (0.2 to 0.6 Hz). When binned by wind speed, the moored Datawell buoys have higher energy in the high frequency tail vs. drifting buoys, by factor 1.2 to 1.6. The moored Datawell buoys also have far better agreement with high-frequency energy levels predicted by a numerical wave model. The key to the difference appears to be the reference frame of the observations. To test this hypothesis, the spectra are adjusted from the drifting reference frame to the fixed reference frame. The adjustment is a two-step process, in which the buoy-observed frequencies are first shifted to an intrinsic reference frame, providing wavenumber at each frequency, and then the drifter-observed spectrum is Doppler-shifted to the fixed reference frame. Two methods for estimation of buoy drift are tested; one is based on wind speed, and one is based on buoy positions. With this adjustment, the observations from the drifting buoys become more consistent with the moored Datawell buoys, though discrepancies still exist with the moored NDBC buoys.
arXiv:2607.09604v1 Announce Type: new
Abstract: We demonstrate a characterisation methodology for a strontium atomic beam, produced by a two-dimensional magneto-optical trap and delivered via a resonant push beam, using fluorescence spectroscopy and time-of-flight (ToF). This provides insight into the beam characteristics of a cold atom source, allowing for direct measurement of the transverse velocity spread, longitudinal velocity distributions, divergence, and the capturable flux for further cooling. From the ToF measurements, we derive a series of flux-per-longitudinal-velocity distributions at varying push saturation parameters ($s_{\mathrm{push}}$) using both a unidirectional and counter-propagating resonant probe beam. A simulation-derived factor is applied to the unidirectional probe longitudinal velocity distribution to account for differences in the scattering rate scaling. The distributions are integrated up to an estimated 3D-MOT capture velocity of \SI{30}{\meter\per\second}. For our system, we find that at $s_{\mathrm{push}} = 0.45$, we obtain a flux of $(1.7 \pm 0.4)\times10^{8}$ atoms/s and $(1.5 \pm 0.4)\times10^{8}$ atoms/s, using a unidirectional probe beam and counter-propagating probe, respectively. These measurements provide a framework for characterising cold atomic sources for applications such as 3D MOT loading and atom interferometers.