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新论文 · v1arXiv:2611.16336

Visibility graphs of irregularly sampled two-dimensional fields via Voronoi tessellations

Francisco J. Muñoz, Juan Carlos Nuño

原文摘要

Visibility graphs map a time series into a network by linking two data whenever the straight line between them is not obstructed by intermediate data. Their extension to two-dimensional fields has so far been restricted to regular lattices, where visibility is checked along a finite number of lattice directions. We propose a definition for scalar data sampled at arbitrary, irregularly scattered points of the plane. Each point is assigned its Voronoi cell, so that the data become a landscape of prisms, and two points are linked when the line of sight between them clears the cells crossed by the connecting segment. This yields horizontal ($\HVGV$) and natural ($\NVGV$) Voronoi visibility graphs that reduce exactly to the classical horizontal and natural visibility graphs when the points are collinear, contain the Gabriel graph of the point set, and can be built by an exact walk through the Delaunay triangulation. For uncorrelated heights, the horizontal graph depends only on the ranks of the data, so its statistics are independent of the marginal distribution. The probability that two points see each other is $2/[(M+1)(M+2)]$, where $M$ is the number of Voronoi cells crossed, which reduces the problem to the geometry of the tessellation. For Poisson points this gives a degree distribution with a scale-free tail $P(k)\simeq(π^3/4)\,k^{-2}$ and a mean degree that grows as $(π^3/8)\ln N$, in contrast with the finite value $\langle k\rangle=4$ of one-dimensional series. Two dimensions are therefore the marginal case in which isotropic horizontal visibility ceases to be local. Numerical simulations with up to $N=1.28\times10^5$ points support these predictions.

math.DS · cond-mat.stat-mech · cs.CG · physics.data-an

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2504.15779

Shannon observables: A scalable approach to information decomposition

Aaron J. Gutknecht, Fernando E. Rosas, David A. Ehrlich, Abdullah Makkeh, Pedro A. M. Mediano, Michael Wibral

原文摘要

Distributed systems, such as biological and artificial neural networks, process information via complex interactions engaging multiple subsystems, resulting in high-order structure of the information within the system with distinct properties across scales. Investigating how these systems process information remains challenging due to difficulties in defining appropriate multivariate metrics and ensuring their scalability to large systems. To address these challenges, we introduce a novel framework based on what we call "Shannon observables": quantities that capture essential properties of high-order information processing, yet can be computed directly from Shannon entropies and scale efficiently to large systems. Our theoretical results demonstrate how Shannon observables can be used to resolve long-standing ambiguities regarding the interpretation of widely used multivariate information-theoretic measures. On the practical side, we demonstrate the framework's versatility across several domains. An analytical study of error-correcting codes shows how Shannon observables trace out distinct encoding regimes as channel noise varies; analyses of deep classifiers and convolutional autoencoders reveal characteristic information-processing signatures across layers and over training; and applied to magnetoencephalography recordings from the human brain, the framework yields a cortical atlas exposing pronounced regional structure in directed information transfer. Overall, our framework resolves fundamental limitations in analysing high-order information phenomena and offers broad opportunities for theoretical developments and empirical analyses.

cs.IT · cs.AI · cs.LG · nlin.AO · physics.data-an

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12458

Causal discovery from Bayesian symbolic regression

Roger Guimerà, Kai-Hendrik Cohrs, Sergio Pedreira, Marta Sales-Pardo, Gustau Camps-Valls

原文摘要

Causal discovery from observational data is central to scientific reasoning, yet established constraint-based and score-based methods often recover only a Markov equivalence class and provide no signal in bivariate or strongly autocorrelated regimes typical in some areas of science. Here, we recast causal discovery as a Bayesian symbolic regression problem. Given a set of candidate parents for each target variable, the Bayesian symbolic regression samples closed-form expressions from a posterior over symbolic models. The fraction of sampled expressions in which each candidate variable appears defines a posterior inclusion probability that we use as the causal score. This replaces pointwise model selection by an ensemble of plausible mechanisms. We evaluate the method on coupled nonlinear bivariate dynamics, on the CauseMe benchmark suite spanning linear, chaotic, nonlinear, higher-dimensional and high-noise settings, and on a four-variable biogeoscience structural causal model under Gaussian, heavy-tailed and mixed-noise regimes. Our approach is competitive with, and often outperforms, specialized causal discovery algorithms. Additionally, the method returns closed-form mechanisms leading to explicit causality and interpretability.

stat.ML · cs.LG · physics.comp-ph · physics.data-an

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.28093

Meteosat Third Generation imagery improves CNN-based SSI retrieval

Gordei Pribõtkin, Piia Post, Velle Toll

原文摘要

Accurate Surface Solar Irradiance (SSI) estimation is increasingly important for photovoltaic energy monitoring and forecasting. The recently introduced Meteosat Third Generation (MTG) satellite constellation provides imaging data with higher spatial resolution compared to the Meteosat Second Generation (MSG) satellite constellation, but its benefits for machine-learning-based SSI retrieval are not well established. We compare multi-resolution convolutional neural network models for 10-minute SSI retrieval over Northern Europe (Estonia) using either MSG/SEVIRI or MTG/FCI satellite imagery. We evaluate model performance against ground-based pyranometer measurements from eight Estonian meteorological stations using site-based cross-validation with a temporally independent test year and multiple training seeds. Model performance is also compared with the SARAH-3 physics-based satellite SSI product. The FCI-only model significantly outperformed the SEVIRI-only model under overcast and cloudy conditions, reducing RMSE by 5.1 W/m^2 and 6.5 W/m^2, respectively. However, under partly cloudy or clear skies, there was no statistically significant difference in RMSE between the FCI-only and the SEVIRI-only models. In all-sky conditions, the RMSE of the FCI-only model was 5.1 W/m^2 lower on average, but this difference was not statistically significant after Bonferroni correction. Compared with physics-based SARAH-3, the FCI-only model yielded skill scores of 28% under overcast conditions, 19% under cloudy conditions, and 17% overall. Furthermore, both models underperformed SARAH-3 in clear-sky conditions. These results show that higher-resolution MTG/FCI imagery improves CNN-based SSI retrieval when clouds dominate irradiance variability, but also indicate that higher resolution alone is insufficient to address clear-sky limitations in machine-learning-based SSI retrieval.

physics.ao-ph · cs.LG · physics.data-an

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10228

FAIR Universe Weak Lensing ML Uncertainty Challenge: Dataset and Cosmological Inference Competition

Biwei Dai, Po-Wen Chang, Erwan Allys, Wahid Bhimji, Boris Bolliet, Thomas Borrett, Paolo Calafiura, Ragansu Chakkappai 等 40 位作者

原文摘要

Weak gravitational lensing is a powerful cosmological probe that enables direct mapping of the matter distribution and precise constraints on the fundamental parameters governing structure formation in the universe. In recent years, machine learning has shown strong potential for extracting cosmological information from weak-lensing data beyond traditional summary statistics. However, progress has been limited by the lack of standardized benchmarks that capture the complexity of the dataset, simulator imperfections, and various systematic biases encountered in realistic analyses. To address this gap, we present the FAIR Universe Weak Lensing Machine Learning Uncertainty Challenge, one of the NeurIPS 2025 competitions, as a benchmark dataset and evaluation study. Our dataset reflects realistic weak-lensing observations, incorporating multiple systematic biases, and thereby providing a controlled testbed for robust cosmological inference under uncertainties. We summarize the main methodological advances developed by the winners of our challenge, including data augmentation with conditional optimal transport, an AI-agent-driven approach for parameter inference, ensemble learning, Vision Transformer-based architectures, and physics-informed feature integration. By unifying the benchmark dataset and the winning solutions, this work provides a community resource for developing and evaluating machine learning methods for weak lensing cosmology and, more broadly, for scientific inference under systematic uncertainties.

astro-ph.CO · cs.AI · cs.CV · cs.LG · physics.data-an

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.09353

comfort-db: A Python toolkit to access, preprocess and explore the oceanographic COMFORT dataset

Yvonne Jenniges, Are Olsen, Boris Peter Koch, Sebastian Maneth

原文摘要

comfort-db is a Python library that facilitates working with the COMFORT SQLite dataset, version 2. The dataset assembles global oceanographic measurements from ten datasets and contains a total of 458,724,734 in situ measurements from the years 1772 to 2020. comfort-db enables easy access to the database and provides preprocessing and exploratory analysis utilities. Preprocessing supports variable scaling and filtering by space, time and quality. Unit conversions can be applied during data loading or separately via a converter object. Gridding is supported and optionally works with bathymetry data to exclude grid cells on land. Moreover, the library supports general and field-specific exploratory data analysis through plotting functions, an interactive Dash app and functions to compute oceanographic profile and water mass statistics. comfort-db is open source (MIT license) and available from PyPI and GitHub.

cs.SE · physics.data-an

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.10057

Domain Adaptive Graph Neural Networks for Neutrino Experiments

Hemalata Nayak, Giuseppe Cerati, Aleksandra Ćiprijanović, Tammy Walton

原文摘要

Weakly interacting particles such as neutrinos require highly efficient and precise detection methods to enable searches for new physics. Deep learning, and particularly Graph Neural Networks (GNNs), has shown promise for reconstruction tasks in liquid argon time projection chamber (LArTPC) neutrino experiments. However, differences between simulations and experimental data introduce domain shifts that can degrade model performance and potentially mimic or obscure new-physics signals. Here, we demonstrate that robust learning across datasets can still be achieved by using domain adaptation during training. We introduce, to our knowledge for the first time, domain-adaptation-enabled GNNs for robust LArTPC reconstruction tasks. We use MicroBooNE open dataset as our labeled source domain, and introduce four perturbed datasets as our unlabeled target domains-- including a fixed scale variation, moderate and severe gaussian noise, and a signal variation as a function of the position in the detector. The inclusion of adversarial-based domain adaptation during training improves performance for all four target domains, without a substantial drop in performance in the source domain. Precision in the target domain was improved up to 0.19 and recall up to 0.37, depending on the variation and the network task. These results demonstrate a potential of domain adaptation to bridge the gap between simulations and experiments, and build more trustworthy models for current and future LArTPC neutrino experiments.

physics.data-an · cs.LG · hep-ex

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08606

Just CaloCHILL: fast hadronic shower generation in a learned latent space

Henry Day-Hall, Frank Gaede, Gregor Kasieczka, Katja Krüger, Martina Mozzanica

原文摘要

Calorimeter showers are the most expensive part of detector simulation, and highly granular calorimeters, in which a single hadronic shower leaves thousands of energy deposits, make them more expensive still. We present CaloCHILL (Calorimeter Hits In Learned Latents), a generative model that does not produce these deposits one by one: instead it generates a compact summary of the shower in a learned latent space and expands it to full resolution only at the end. The complete shower, including the arrival time of every hit, is built in three stages: a flow-matching model first sets the number of hits and the earliest hit time in each calorimeter layer, a mean-flow model then generates the latent summary, and the decompressor of a variational autoencoder turns it into the full point cloud, giving every hit its position, energy and time. Sparse attention along the shower axis keeps this compression affordable for showers with thousands of hits. On positive-pion showers in the calorimeter of the International Large Detector, a proposed detector for the FCC-ee, CaloCHILL reproduces the main shower observables, including their time structure. This new model is 600 times faster than its predecessor, while improving on all accuracy metrics considered.

physics.ins-det · cs.LG · hep-ex · hep-ph · physics.data-an

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.08131

Hi-Fi与MMI之间：部分信息分解中冗余与协同的重叠

Between Hi-Fi and MMI: the overlap of redundancy and synergy in partial information decomposition

Niels Van Santen, Yves Rosseel, Daniele Marinazzo

AI 速读 基于摘要

该论文通过参数化部分信息分解中冗余与协同的重叠，揭示了Hi-Fi和MMI分别对应零重叠和最大重叠的极端情况，并探讨了多源下的推广与限制。

cs.IT · physics.data-an · stat.ME

arXiv ↗PDF ↗
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Partial information decomposition (PID) splits the information that two sources carry about a target into redundant, unique and synergistic parts, with nonnegative decompositions lying on a segment where redundancy measures are bounded by the positive part of the co-information and the smaller source-target information. We parameterize this segment by the overlap, the common excess of redundancy and synergy over their net balance, and show that for many of the named PID measures, their redundancy can be written as the largest positive part of the co-information over a set of reference distributions. This common form induces an ordering of those PIDs on the segment. With the smallest redundancy and synergy and the largest unique atoms of any nonnegative PID, the information-theoretic version of Hi-Fi, a feature-importance method, is the zero-overlap end of the segment for two sources, with the maximum overlap bound given by the minimum mutual information (MMI) PID. We introduce, by splitting the co-information into a pairwise maximum- entropy part and the third-order connected information, a PID that sits at zero overlap except when pairwise redundancy and triadic synergy coexist. For three or more sources the bivariate results hold on every pair of merged source groups, and among all nonnegative solutions of the lattice equations MMI and Hi-Fi still give the smallest and largest unique atoms but zero overlap on all pairs at once is often unattainable. Two inequalities across pairs are necessary in general and sufficient for three sources. For identity targets zero overlap is possible exactly when the sources' information diagram is nonnegative. In random three- and four-source distributions zero overlap on all pairs fails in 21-100% of the cases.

版本更新 · v2arXiv:2610.35766

信号改善、编码器退化：自监督学习中无标签监控的已知答案检验

Improving Signals, Declining Encoder: A Known-Answer Test of Label-Free Monitoring in Self-Supervised Learning

Nilotpal Kakati

AI 速读 基于摘要

作者用已知生成因子的合成图像训练LeJEPA编码器，检验损失与无标签特征统计能否察觉编码器已丢失部分所学信息。

cs.LG · cs.CV · physics.data-an

arXiv ↗PDF ↗
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Self-supervised training is monitored without labels, through the loss and statistics of the learned features. We report a case where we know the encoders lost part of what they had learned, so we can check whether these signals noticed. We trained LeJEPA encoders on synthetic images with known generating factors. In nine of ten seeds, the readability of those factors fell abruptly and stayed down, nearly always along the same direction in factor space. Meanwhile the loss kept falling, and most of the eleven label-free signals we tracked kept improving and rated the declining runs better than runs that did not decline. Standard change-point detection rarely flagged the decline near its onset, and selecting checkpoints by these signals mostly chose ones after it.

版本更新 · v2arXiv:2609.26683

PROSWIN: Probabilistic Solar Wind Speed Forecasting Using Deep Distributional Regression From Solar Images

Daniel Collin, Yuri Shprits, Luca Chiarabini, Stefan J. Hofmeister, Nadja Klein, Guillermo Gallego

原文摘要

Accurately predicting fast solar wind conditions is challenging, as uncertainties are large and unquantified by traditional single-value prediction models. In particular, the risks of high-speed solar wind streams (HSSs), which can cause damage to technological infrastructure, cannot be reliably assessed without probabilistic forecasts. We present PROSWIN, a probabilistic machine learning model that forecasts the hourly solar wind speed (SWS) at Earth with a four-day lead time. The approach combines solar images and magnetograms using a deep neural network coupled to a distributional regression algorithm. Because standard error metrics underweight the relevance of HSS peaks, we further introduce the prediction score, a model-selection metric that jointly rewards timeline and HSS peak accuracy. On 14 years of data, our forecast achieves very well-calibrated uncertainties (<1% average deviation). Using the continuous ranked probability score (CRPS), a metric that assesses distributional accuracy, we obtain a timeline CRPS of 41.0 km/s, an HSS peak CRPS of 45.3 km/s, and a prediction score of 42.3 km/s. We find that the 171 Å channel is an important complement to the typically used 193 Å and 211 Å channels and that the prediction score for model selection improves the applicability of the model. Compared to selected models from the literature, ours is the only one that is accurate for both timeline and HSS peak values, rather than trading one off against the other. These results support the advantages of probabilistic over single-value solar wind models. The introduced methods are also transferable to other forecasting problems.

astro-ph.SR · cs.LG · physics.data-an · physics.space-ph

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2510.01335

HomID : Benchmarking Intrinsic Dimension Estimators on Homogenous Manifolds with Anisotropic Embeddings

Aritra Das, Joseph T. Iosue, Victor V. Albert

原文摘要

The manifold hypothesis suggests that data lies on manifolds with smaller intrinsic dimension (ID) than their ambient dimension. However there is no empirical agreement on the estimates for ID from different estimators for realistic datasets. Thus it is important to test ID estimators (IDEs) with targeted stressors. In this work, we consider the role of anisotropy. To this end, we propose HomID, a collection of homogeneous spaces with anisotropic embedding, for benchmarking ID estimators. We observe that methods that perform well on standard benchmarks systematically degrade on HomID under identical resource allocation. We further observe that anisotropic distortion of such benchmarks also results in performance degradation. Finally, we demonstrate that controlled anisotropic distortions induce systematic shifts in the distributions on which these methods rely, providing a concrete mechanism for the resulting estimation errors in two particular IDEs. The code for the project is available at https://github.com/a-writes-code/homid

cs.LG · cond-mat.dis-nn · math.MG · physics.data-an · quant-ph

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.14373

Machine-learned particle flow as a foundation model for collider physics

Farouk Mokhtar, Joosep Pata, Michael Kagan, Javier Duarte

原文摘要

The workflow from particle collision to physics analysis passes through a series of reconstruction steps that are traditionally modular and disconnected, with no shared representation linking low-level detector data to high-level analysis tasks. We show that casting event reconstruction as a machine learning problem naturally produces such a shared representation. We repurpose a machine learning model trained for particle-flow reconstruction (MLPF) to perform three distinct analysis tasks: jet flavor identification, jet energy regression, and missing momentum regression. By appending the per-particle latent representations learned during reconstruction as additional input features, we substantially improve over baselines that use conventional particle-level features alone. We further demonstrate that a single linear layer trained using only the latent representations achieves competitive performance against state-of-the-art baseline architectures, and outperforms the baseline for missing momentum regression with approximately 35 times fewer parameters. These results demonstrate that the latent representations learned during reconstruction encode essential physics information needed for downstream analysis, establishing MLPF as a foundation model and offering a concrete step toward an end-to-end pipeline from detector data to physics analysis.

hep-ex · cs.LG · hep-ph · physics.data-an · physics.ins-det

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.15215

Stochastic Ising networks with asymmetric and time-delayed interactions

Hantao Zhang, Sidra Gibeault, Matthew W. Daniels, Philippe Talatchian, Ursula Ebels, Advait Madhavan, Mark D. Stiles

原文摘要

Stochastic Ising networks are widely used to describe collective dynamics in complex systems and to compute, yet physical implementations of these networks often contain both asymmetric interactions and nonzero signal propagation times that fall outside conventional theories of their behavior. Here, we study stochastic Ising networks with asymmetric and time-delayed interactions motivated by experimental observations in coupled superparamagnetic tunnel junctions. Time delay fundamentally reshapes the dynamics induced by antisymmetric couplings, producing strong oscillatory temporal correlations consistent with experiment. Sufficiently long delays strongly suppress the non-uniformity of steady-state probability distributions, a behavior explained by an exponential suppression of the first-order equal-time cross-correlation between pairs of spins. Residual non-uniformity is due to the equal-time cross-correlation mediated by a third spin that contributes only at higher order in the coupling strength. These relatively featureless probability distributions coexist with pronounced delayed temporal correlations, distinguishing them from equilibrium high-temperature behavior. We further show analytically that these delay-induced near-uniform distributions emerge in a broad class of stochastic networks that lack symmetry-breaking bias fields. Introducing symmetry-breaking bias fields restores a strong dependence of the steady state on interactions with qualitatively modified behavior. Simulations of networks with five coupled spins demonstrate that these effects persist beyond minimal systems with only two spins.

physics.app-ph · cond-mat.mes-hall · cond-mat.stat-mech · cs.ET · physics.data-an

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.04123

An automated photometric pipeline for measuring the corotation radius of spiral galaxies

Kimia Yazdani, Sahel Mesforoush, Wayne B. Hayes

原文摘要

The corotation radius R_CR of a spiral galaxy marks the radius where the angular speed of the spiral pattern matches the orbital speed of the disk material. Despite its importance for understanding spiral structure and galaxy evolution, very few galaxies have corotation measurements, often derived using heterogeneous techniques. We present an automated photometric pipeline that identifies corotation radii by tracking the azimuthal phase offset between young and old stellar populations. Building on the color-gradient framework of \citet{Puerari1997}, we extract azimuthal brightness profiles from deprojected dual-band images, fit periodic models to the profiles, and identify corotation candidates at radii where the phase difference between the two stellar tracers changes sign. Each candidate is assigned an adaptive uncertainty interval and reported with a goodness-of-fit metric. We benchmark the method against a cross-method literature compilation, scoring every technique -- including our own -- against a reference built from the published measurements alone and evaluated leave-one-out. Over the $90$ galaxies for which such a reference exists, the pipeline places a correct corotation radius in $80.0\%$ of them, recovers $63.6\%$ of their known corotation radii, and reaches a harmonic mean of recall and purity of $0.58$, the highest values of any method in the comparison. Unlike the published methods, each of which was applied to a small, curated set of galaxies, our pipeline is run on the entire reference sample without any pre-selection, a sample two to six times larger than existing methods. The pipeline is scalable to large surveys, flexible with respect to spiral mode, and naturally recovers multiple corotation radii in galaxies with complex spiral structure, providing a uniform framework for studying disk resonances across diverse galaxy populations.

astro-ph.GA · cs.CV · physics.data-an

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03250

Anomaly-ranked confirmation slows new-class learning

Argyro Sasli, Michael Coughlin

原文摘要

Anomaly detectors rank unfamiliar examples for expert confirmation, and the confirmed examples teach a classifier to recognise a new class. Across five benchmarks from physics, medicine and computer vision, the members flagged by the detector are those the classifier learns first. In three systems, confirming the highest-ranked members of a new class gives lower recognition than confirming the same number of random members. Handing subsequent selection to the promoted classifier recovers part of this loss in two systems. A paired intervention further shows that replacing nearby known classes with distant ones sharply reduces the confirmations needed for recognition. These results distinguish finding a new class from selecting examples that teach its extent: within the tested classes, concentrating confirmations at the top of the anomaly ranking makes learning less efficient.

astro-ph.IM · cs.LG · physics.data-an

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.24264

Discovery of connectivity-trainability trade-off of IQP circuits for Hamiltonian optimization

Quoc Chuong Nguyen, Dao Duy Tung, Vu Tuan Hai

原文摘要

Instantaneous Quantum Polynomial-time (IQP) circuits are promising candidates for near-term quantum computing due to the conjectured classical hardness of their sampling task and their shallow, highly parallelizable structure. Despite extensive studies of their complexity-theoretic properties, the role of circuit connectivity in their trainability as variational ansatze remains poorly understood. In this work, we present an investigation of IQP circuits for Hamiltonian optimization that traces a mechanistic chain linking circuit connectivity, entanglement generation, and gradient scaling: increasing connectivity drives the circuit output toward Haar-like statistics and volume-law entanglement, and this increase in entanglement is consistently associated with an exponential decay of the gradient variance and degraded optimization performance, while sparser architectures retain stable gradients at the cost of global expressibility and entanglement capacity. This connectivity-entanglement-gradient mechanism produces a nontrivial connectivity-trainability trade-off, demonstrating that effective IQP-based Hamiltonian optimization requires a balance between global expressibility and trainability rather than maximal expressivity alone. In particular, on our benchmarks, a circuit generating no entanglement already matches the optimization performance of more highly connected circuits, and a circular topology retains entanglement capacity while preserving trainability, making it a broadly effective choice among the architectures considered.

cs.SI · math.NA · physics.data-an · quant-ph

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arXiv ↗PDF ↗