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Kai Wang

Kai Wang contributes to research discovery and scholarly infrastructure.

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Computer Vision Artificial Intelligence Machine Learning astro-ph.CO astro-ph.GA Graphics

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preprint2026arXiv

An Enigmatic PeVatron in an Area around HII Region G35.6$-$0.5

Identifying Galactic PeVatrons (PeV particle accelerators) from the ultra-high-energy (UHE, >100 TeV) $γ$-ray sources plays a crucial role in revealing the origin of Galactic cosmic rays. The UHE source 1LHAASO J1857+0203u is suggested to be associated with HESS J1858+020, which may be attributed to the possible PeVatron candidate supernova remnant (SNR) G35.6$-$0.4 or HII region G35.6$-$0.5. We perform detailed analysis on the very-high-energy and UHE $γ$-ray emissions towards this region with data from the Large High Altitude Air Shower Observatory (LHAASO). 1LHAASO J1857+0203u is detected with a significance of 11.6$σ$ above 100 TeV, indicating the presence of a PeVatron. It has an extension of $\sim 0.18^\circ$ with a power-law (PL) spectral index of $\sim$2.5 in 1-25 TeV and a point-like emission with a PL spectral index of $\sim$3.2 above 25 TeV. Using the archival CO and HI data, we identify some molecular and atomic clouds that may be associated with the TeV $γ$-ray emissions. Our modelling indicates that the TeV $γ$-ray emissions are unlikely to arise from the clouds illuminated by the protons that escaped from SNR G35.6$-$0.4. In the scenario that HII region G35.6$-$0.5 could accelerate particles to the UHE band, the observed GeV-TeV $γ$-ray emission could be well explained by a hadronic model with a PL spectral index of $\sim$2.0 and cutoff energy of $\sim$450 TeV. However, an evolved pulsar wind nebula origin cannot be ruled out.

preprint2026arXiv

Constraining the Cosmic-ray Energy Based on Observations of Nearby Galaxy Clusters by LHAASO

Galaxy clusters act as reservoirs of high-energy cosmic rays (CRs). As CRs propagate through the intracluster medium, they generate diffuse $γ$-rays detectable by arrays such as LHAASO. These $γ$-rays result from proton-proton ($pp$) collisions of very high-energy cosmic rays (VHECRs) or inverse Compton (IC) scattering of positron-electron pairs created by $pγ$ interactions of ultra-high-energy cosmic rays (UHECRs). We analyzed diffuse $γ$-ray emission from the Coma, Perseus, and Virgo clusters using LHAASO data. Diffuse emission was modeled as a disk of radius $R_{500}$ for each cluster while accounting for point sources. No significant diffuse emission was detected, yielding 95\% confidence level (C.L.) upper limits on the $γ$-ray flux: for WCDA (1-25~TeV) and KM2A ($>25$~TeV), less than $(49.4, 13.7, 54.0)$ and $(1.34, 1.14, 0.40) \times 10^{-14}$~ph~cm$^{-2}$~s$^{-1}$ for Coma, Perseus, and Virgo, respectively. The $γ$-ray upper limits can be used to derive model-independent constraints on the integral energy of CRp above 10~TeV (corresponding to the LHAASO observational range $>1$~TeV under the $pp$ scenario) to be less than $(1.96, 0.59, 0.08) \times 10^{61}$~erg. The absence of detectable annuli/ring-like structures, indicative of cluster accretion or merging shocks, imposes further constraints on models in which the UHECRs are accelerated in the merging shocks of galaxy clusters.

preprint2026arXiv

COPRA: Conditional Parameter Adaptation with Reinforcement Learning for Video Anomaly Detection

Vision-language models (VLMs) have shown strong performance in video anomaly detection (VAD) while providing interpretable predictions. However, existing VLM-based VAD methods suffer from a fundamental mismatch between training and inference in both data distribution and model configuration. First, most approaches rely on static post-training adaptation, limiting generalization under distribution shifts such as unseen environments or anomaly types. Second, they train VLMs on sparse frames from long videos, but perform inference on densely sampled short segments, creating inconsistencies between training and testing. To address these limitations, we propose COPRA, a conditional parameter adaptation framework for VLM-based VAD. Instead of fixed prompts or shared parameter updates, COPRA generates input-specific parameter updates to dynamically adapt a frozen VLM for each video segment during both training and inference. Experiments show strong performance on standard VAD benchmarks, consistently outperforming static baselines in both in-domain and cross-domain settings. Moreover, COPRA generalizes beyond VAD to unseen tasks such as multiple-choice Video Question Answering and Dense Captioning. These results highlight COPRA as an effective weight-space generation framework for scalable, adaptive, and context-aware video understanding. The code will be released at https://github.com/THE-MALT-LAB/COPRA

preprint2026arXiv

DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models

Recent advancements in slow thinking reasoning models have shown exceptional performance in complex reasoning tasks. However, these models often exhibit overthinking (generating redundant reasoning steps for simple problems), leading to excessive computational resource usage. While current mitigation strategies uniformly reduce reasoning tokens, they risk degrading performance on challenging tasks that require extended reasoning. This paper introduces Difficulty-Adaptive Slow Thinking (DAST), a novel framework that enables models to autonomously adjust the length of Chain-of-Thought (CoT) based on problem difficulty. We first propose a Token Length Budget (TLB) metric to quantify difficulty, then leverage budget-aware reward shaping and budget preference optimization to implement DAST. DAST penalizes overlong responses for simple tasks while incentivizing sufficient reasoning for complex problems. Experiments on diverse datasets and model scales demonstrate that DAST effectively mitigates overthinking (reducing token usage by over 30\% on average) while preserving reasoning accuracy on complex problems. Our codes and models are available at https://github.com/AnonymousUser0520/AnonymousRepo01.

preprint2026arXiv

Deep view of Composite SNR CTA1 with LHAASO in $γ$-rays up to 300 TeV

The ultra-high-energy (UHE) gamma-ray source 1LHAASO J0007+7303u is positionally associated with the composite SNR CTA1 that is located at high Galactic Latitude $b\approx 10.5^\circ$. This provides a rare opportunity to spatially resolve the component of the pulsar wind nebula (PWN) and supernova remnant (SNR) at UHE. This paper conducted a dedicated data analysis of 1LHAASO J0007+7303u using the data collected from December 2019 to July 2023. This source is well detected with significances of 21$σ$ and 17$σ$ at 8$-$100 TeV and $>$100 TeV, respectively. The corresponding extensions are determined to be 0.23$^{\circ}\pm$0.03$^{\circ}$ and 0.17$^{\circ}\pm$0.03$^{\circ}$. The emission is proposed to originate from the relativistic electrons and positrons accelerated within the PWN of PSR J0007+7303. The energy spectrum is well described by a power-law with an exponential cutoff function $dN/dE = (42.4\pm4.1)(\frac{E}{20\rm\ TeV})^{-2.31\pm0.11}\exp(-\frac{E}{110\pm25\rm\ TeV})$ $\rm\ TeV^{-1}\ cm^{-2}\ s^{-1}$in the energy range from 8 TeV to 300 TeV, implying a steady-state parent electron spectrum $dN_e/dE_e\propto (\frac{E_e}{100\rm\ TeV})^{-3.13\pm0.16}\exp[(\frac{-E_e}{373\pm70\rm\ TeV})^2]$ at energies above $\approx 50 \rm\ TeV$. The cutoff energy of the electron spectrum is roughly equal to the expected current maximum energy of particles accelerated at the PWN terminal shock. Combining the X-ray and gamma-ray emission, the current space-averaged magnetic field can be limited to $\approx 4.5\rm\ μG$. To satisfy the multi-wavelength spectrum and the $γ$-ray extensions, the transport of relativistic particles within the PWN is likely dominated by the advection process under the free-expansion phase assumption.

preprint2026arXiv

DyDiT++: Diffusion Transformers with Timestep and Spatial Dynamics for Efficient Visual Generation

Diffusion Transformer (DiT), an emerging diffusion model for visual generation, has demonstrated superior performance but suffers from substantial computational costs. Our investigations reveal that these costs primarily stem from the static inference paradigm, which inevitably introduces redundant computation in certain diffusion timesteps and spatial regions. To overcome this inefficiency, we propose Dynamic Diffusion Transformer (DyDiT), an architecture that dynamically adjusts its computation along both timestep and spatial dimensions. Building on these designs, we present an extended version, DyDiT++, with improvements in three key aspects. First, it extends the generation mechanism of DyDiT beyond diffusion to flow matching, demonstrating that our method can also accelerate flow-matching-based generation, enhancing its versatility. Furthermore, we enhance DyDiT to tackle more complex visual generation tasks, including video generation and text-to-image generation, thereby broadening its real-world applications. Finally, to address the high cost of full fine-tuning and democratize technology access, we investigate the feasibility of training DyDiT in a parameter-efficient manner and introduce timestep-based dynamic LoRA (TD-LoRA). Extensive experiments on diverse visual generation models, including DiT, SiT, Latte, and FLUX, demonstrate the effectiveness of DyDiT++. Remarkably, with <3% additional fine-tuning iterations, our approach reduces the FLOPs of DiT-XL by 51%, yielding 1.73x realistic speedup on hardware, and achieves a competitive FID score of 2.07 on ImageNet. The code is available at https://github.com/alibaba-damo-academy/DyDiT.

preprint2026arXiv

Energy calibration of LHAASO-KM2A using the cosmic ray Moon shadow

We present a precise measurement of the westward, rigidity-dependent shift of the Moon's shadow using three and a half years of cosmic-ray data collected by the Kilometer Square Array (KM2A) of the Large High Altitude Air Shower Observatory (LHAASO). These measurements enable us to calibrate the detector energy response in the range 20-260 TeV, with results showing excellent agreement with the response derived from Monte Carlo (MC) simulations of the KM2A detector. We also measure a best-fit parameter $ε= 0.015 \pm 0.08$, corresponding to a 95% confidence interval of [-14%, +17%] for the energy-scale estimation. This result establishes the exceptional accuracy of the KM2A-MC in simulating the detector's response within this energy range.

preprint2026arXiv

Energy-Dependent Shifts of Medium-Scale Anisotropies in Very-High-Energy Cosmic Rays Observed by LHAASO-KM2A

Small deviations from isotropy in the arrival directions of Galactic cosmic rays serve as a unique probe of the local magnetic environment. In this Letter, we report observations of medium-scale anisotropies (MSA) at energies above 10 TeV using the LHAASO-KM2A array. Our analysis identifies four regions of excess and four regions of deficit, each spanning angular scales of approximately ten degrees. Crucially, we detect significant energy-dependent shifts in the centroids of two excess regions: Region B and the newly identified Region $\mathrm{\widetilde{D}}$. We also characterize the energy evolution of the fractional relative intensity across both excess and deficit regions. These findings imply that the observed anisotropies are shaped by the specific realization of the local turbulent magnetic field within the cosmic ray scattering length. Such energy-dependent behaviors impose strict constraints on local turbulence models and cosmic ray propagation theories.

preprint2026arXiv

Evidence for particle acceleration approaching PeV energies in the W51 complex

The $γ$-ray emission from the W51 complex is widely acknowledged to be attributed to the interaction between the cosmic rays (CRs) accelerated by the shock of supernova remnant (SNR) W51C and the dense molecular clouds in the adjacent star-forming region, W51B. However, the maximum acceleration capability of W51C for CRs remains elusive. Based on observations conducted with the Large High Altitude Air Shower Observatory (LHAASO), we report a significant detection of $γ$ rays emanating from the W51 complex, with energies from 2 TeV to 200 TeV. The LHAASO measurements, for the first time, extend the $γ$-ray emission from the W51 complex beyond 100 TeV and reveal a significant spectrum bending at tens of TeV. By combining the ``$π^0$-decay bump" featured data from Fermi-LAT, the broadband $γ$-ray spectrum of the W51 region can be well-characterized by a simple pp-collision model. The observed spectral bending feature suggests an exponential cutoff at $\sim400$~TeV or a power-law break at $\sim200$~TeV in the CR proton spectrum, most likely providing the first evidence of SNRs serving as CR accelerators approaching the PeV regime. Additionally, two young star clusters within W51B could also be theoretically viable to produce the most energetic $γ$ rays observed by LHAASO. Our findings strongly support the presence of extreme CR accelerators within the W51 complex and provide new insights into the origin of Galactic CRs.

preprint2026arXiv

Extended Very-High-Energy Gamma-Ray Emission Surrounding PSR J0622 + 3749 Observed by LHAASO-KM2A

We report the discovery of an extended very-high-energy (VHE) gamma-ray source around the location of the middle-aged (207.8 kyr) pulsar PSR J0622+3749 with the Large High Altitude Air Shower Observatory (LHAASO). The source is detected with a significance of $8.2σ$ for $E>25$~TeV assuming a Gaussian template. The best-fit location is (R.A., Dec.)$=(95^{\circ}\!.47\pm0^{\circ}\!.11,\,37^{\circ}\!.92 \pm0^{\circ}\!.09)$, and the extension is $0^{\circ}\!.40\pm0^{\circ}\!.07$. The energy spectrum can be described by a power-law spectrum with an index of ${-2.92 \pm 0.17_{\rm stat} \pm 0.02_{\rm sys} }$. No clear extended multi-wavelength counterpart of the LHAASO source has been found from the radio to sub-TeV bands. The LHAASO observations are consistent with the scenario that VHE electrons escaped from the pulsar, diffused in the interstellar medium, and scattered the interstellar radiation field. If interpreted as the pulsar halo scenario, the diffusion coefficient, inferred for electrons with median energies of $\sim160$~TeV, is consistent with those obtained from the extended halos around Geminga and Monogem and much smaller than that derived from cosmic ray secondaries. The LHAASO discovery of this source thus likely enriches the class of so-called pulsar halos and confirms that high-energy particles generally diffuse very slowly in the disturbed medium around pulsars.

preprint2026arXiv

Functionalization via Structure Completion and Motion Rectification

Acquisition and creation of 3D assets have been largely view- or appearance-driven. As a result, existing digital 3D models often lack the requisite structural components to function as intended, such as joints, supports, interiors, or interaction elements. At the same time, even human-annotated motions are frequently error-prone, leading to physically implausible behavior. We introduce object functionalization, a novel task aimed at transforming visually plausible but non-functional 3D models into functional and physically operable ones. We formulate functionalization as a graph completion problem over a new functional graph representation, where labeled nodes represent object parts, labeled edges encode functional and contact relations, and movable nodes carry motion attributes, so that structural functional deficiencies manifest as missing nodes or incorrect edges. We develop a neural Graph Functionalizer (GraFu) to complete an incomplete graph representing a non-functional 3D object. The completed graph then drives a geometry realization stage that instantiates predicted connectors and structural elements in 3D, with the compelling side effect of rectifying erroneous human-annotated and predicted motions. To support training and evaluation, focusing on furniture as a rich and challenging target category, we introduce FurFun-233, a dataset of 233 paired non-functional and functionalized furniture models. On PartNet-Mobility ("zero-shot") and HSSD test sets, our method matches state-of-the-art methods in motion prediction accuracy while substantially improving functionality in terms of collision and connectivity.

preprint2026arXiv

LHAASO Detection of Ultra-High-Energy Gamma-Ray Emission toward the Giant Molecular Clouds

The $γ$-ray from Giant molecular clouds (GMCs) is regarded as the most ideal tool to perform in-situ measurement of cosmic ray (CR) density and spectra in our Galaxy. We report the first detection of $γ$-ray emissions in the very-high-energy (VHE) domain from the five nearby GMCs with a stacking analysis based on a 4.5-year $γ$-ray observation with the Large High Altitude Air Shower Observatory (LHAASO) experiment. The spectral energy distributions derived from the GMCs are consistent with the expected $γ$-ray flux produced via CR interacting with the ISM in the energy interval 1 - 100 $~\rm$ TeV. In addition, we investigate the presence of the CR spectral `knee' by introducing a spectral break in the $γ$-ray data. While no significant evidence for the CR knee is found, the current KM2A measurements from GMCs strongly favor a proton CR knee located above 0.9$~\rm$ PeV, which is consistent with the latest measurement of the CR spectrum by ground-based experiments.

preprint2026arXiv

Local Group analogues in a cosmological context -- I. Relating velocity structure to the cosmic web

Our Local Group, dominated in mass by the Milky Way (MW) and M31, provides a unique laboratory for testing $Λ$CDM cosmology on small scales owing to its proximity. However, its connection to the surrounding large-scale environment, which is essential for interpreting its properties, is inadequately understood. In this work, we explore the connection between Local Group analogues (LGAs) and their surrounding large-scale environments using the ABACUSSUMMIT simulation suite, highlighting the key role of the coupling energy of the MW-M31 orbit, $E_{\rm coupling}$. We find that LGAs with high $E_{\rm coupling}$ preferentially reside in denser regions, whereas those with low $E_{\rm coupling}$ tend to occupy low-density environments. Furthermore, LGAs with low $E_{\rm coupling}$ exhibit strong alignment with cosmic filaments, manifested as a pronounced polar anisotropy in the distribution of tracer haloes. By contrast, LGAs with high $E_{\rm coupling}$ show a weaker polar anisotropy but an enhanced azimuthal anisotropy, with large-scale tracer haloes preferentially lying in the plane spanned by the halo pair and the orbital spin vector. Within this framework, our Local Group is characterised by typical $E_{\rm coupling}$ residing in a relatively under-dense environment, yet it remains consistent with the 95\% range of analogue systems identified in the simulation.

preprint2026arXiv

Measurement of attenuation length of the muon content in extensive air showers from 0.3 to 30 PeV with LHAASO

The attenuation length of the muon content in extensive air showers provides important information regarding the generation and development of air showers. This information can be used not only to improve the description of such showers but also to test fundamental models of hadronic interactions. Using data from the LHAASO-KM2A experiment, the development of the muon content in high-energy air showers was studied. The attenuation length of muon content in the air showers was measured from experimental data in the energy range from 0.3 to 30 PeV using the constant intensity cut method. By comparing the attenuation length of the muon content with predictions from high-energy hadronic interaction models (QGSJET-II-04, SIBYLL 2.3d, and EPOS-LHC), it is evident that LHAASO results are significantly shorter than those predicted by the first two models (QGSJET-II-04 and SIBYLL 2.3d) but relatively close to those predicted by the third model (EPOS-LHC). Thus, the LHAASO data favor the EPOS-LHC model over the other two models. The three interaction models confirmed an increasing trend in the attenuation length as the cosmic-ray energy increases.

preprint2026arXiv

Measurement of Very-high-energy Diffuse Gamma-ray Emissions from the Galactic Plane with LHAASO-WCDA

The diffuse Galactic gamma-ray emission is a very important tool used to study the propagation and interaction of cosmic rays in the Milky Way. In this work, we report the measurements of the diffuse emission from the Galactic plane, covering Galactic longitudes from $15^{\circ}$ to $235^{\circ}$ and latitudes from $-5^{\circ}$ to $+5^{\circ}$, in an energy range of 1 TeV to 25 TeV, with the Water Cherenkov Detector Array (WCDA) of the Large High Altitude Air Shower Observatory (LHAASO). After masking the sky regions of known sources, the diffuse emission is detected with $24.6σ$ and $9.1σ$ significance in the inner Galactic plane and outer Galactic plane, respectively. The WCDA spectra in both regions can be well described by a power-law function, with spectral indices of $-2.67\pm0.05_{\rm stat}$ in the inner region and $-2.83\pm0.19_{\rm stat}$ in the outer region, respectively. Combined with the Square Kilometer Array (KM2A) measurements at higher energies, a clear softening of the spectrum is found in the inner region, with change of spectral indices by $\sim0.5$ at a break energy around $30$ TeV. The fluxes of the diffuse emission are higher by a factor of $1.5-2.7$ than the model prediction assuming local CR spectra and the gas column density, which are consistent with those measured by the KM2A. Along Galactic longitude, the spatial distribution of the diffuse emission shows deviation from that of the gas column density. The spectral shape of the diffuse emission are possibly variation in different longitude region. The WCDA measurements bridge the gap between the low-energy measurements by space detectors and the ultra-high-energy observations by LHAASO-KM2A and other experiments. These results suggest that improved modeling of the wide-band diffuse emission is required.

preprint2026arXiv

MVT: Mask-Grounded Vision-Language Models for Taxonomy-Aligned Land-Cover Tagging

Land-cover understanding in remote sensing increasingly demands class-agnostic systems that generalize across datasets while remaining spatially precise and interpretable. We study a geometry-first discovery-and-interpretation setting under domain shift, where candidate regions are delineated class-agnostically and supervision avoids lexical class names via anonymized identifiers. Complementary to open-set recognition and open-world learning, we focus on coupling class-agnostic mask evidence with taxonomy-grounded scene interpretation, rather than unknown rejection or continual class expansion. We propose MVT, a three-stage framework that (i) extracts boundary-faithful region masks using SAM2 with domain adaptation, (ii) performs mask-grounded semantic tagging and scene description generation via dual-step LoRA fine-tuning of multimodal LLMs, and (iii) evaluates outputs with LLM-as-judge scoring calibrated by stratified expert ratings. On cross-dataset segmentation transfer (train on OpenEarthMap, evaluate on LoveDA), domain-adapted SAM2 improves mask quality; meanwhile, dual-step MLLM fine-tuning yields more accurate taxonomy-aligned tags and more informative mask-grounded scene descriptions.

preprint2026arXiv

Neural-Driven Image Editing

Traditional image editing typically relies on manual prompting, making it labor-intensive and inaccessible to individuals with limited motor control or language abilities. Leveraging recent advances in brain-computer interfaces (BCIs) and generative models, we propose LoongX, a hands-free image editing approach driven by multimodal neurophysiological signals. LoongX utilizes state-of-the-art diffusion models trained on a comprehensive dataset of 23,928 image editing pairs, each paired with synchronized electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), photoplethysmography (PPG), and head motion signals that capture user intent. To effectively address the heterogeneity of these signals, LoongX integrates two key modules. The cross-scale state space (CS3) module encodes informative modality-specific features. The dynamic gated fusion (DGF) module further aggregates these features into a unified latent space, which is then aligned with edit semantics via fine-tuning on a diffusion transformer (DiT). Additionally, we pre-train the encoders using contrastive learning to align cognitive states with semantic intentions from embedded natural language. Extensive experiments demonstrate that LoongX achieves performance comparable to text-driven methods (CLIP-I: 0.6605 vs. 0.6558; DINO: 0.4812 vs. 0.4636) and outperforms them when neural signals are combined with speech (CLIP-T: 0.2588 vs. 0.2549). These results highlight the promise of neural-driven generative models in enabling accessible, intuitive image editing and open new directions for cognitive-driven creative technologies. The code and dataset are released on the project website: https://loongx1.github.io.

preprint2026arXiv

Position: Weight Space Should Be a First-Class Generative AI Modality

Neural network checkpoints have quietly become a large-scale data resource: millions of trained weight vectors now exist, each encoding task-, domain-, and architecture-specific knowledge. This position paper argues that model checkpoints should be treated as a first-class data modality, and that generative modeling in weight space should be standardized as a core machine learning primitive. Recent advances demonstrate that neural weights can be synthesized on demand, often matching fine-tuning performance while reducing adaptation cost by orders of magnitude. We contend that these results reflect an underlying structural fact: high-performing models occupy low-dimensional, highly structured regions of weight space shaped by symmetry, flatness, modularity, and shared subspaces. Building on this view, we organize existing methods into a five-stage pipeline, survey applications where the approach is already practical, and clarify current limits: adapter-scale and conditional generation are advancing rapidly, while unrestricted frontier-scale checkpoint synthesis remains open. Our goal is to shift the community's default mindset from optimizing models per task to sampling models from learned weight distributions, accelerating toward an era in which AI systems routinely improve or create other AI systems.

preprint2026arXiv

Transient Large-Scale Anisotropy in TeV Cosmic Rays due to an Interplanetary Coronal Mass Ejection

Large- or medium-scale cosmic ray anisotropy at TeV energies has not previously been confirmed to vary with time. Transient anisotropy changes have been observed below 150 GeV, especially near the passage of an interplanetary shock and coronal mass ejection containing a magnetic flux rope ejected by a solar storm, which can trigger a geomagnetic storm with practical consequences. In such events, cosmic rays provide remote sensing of the magnetic field properties. Here we report the observation of transient large-scale anisotropy in TeV cosmic ray ions using data from the Large High Altitude Air Shower Observatory (LHAASO). We analyze hourly skymaps of the transient cosmic ray intensity excess or deficit, the gradient of which indicates the direction and magnitude of transient large-scale anisotropy across the field of view. We observe enhanced anisotropy above typical hourly fluctuations with $>$5$σ$ significance during some hours of November 4, 2021, in separate data sets for four primary cosmic ray energy ranges of median energy from $E$=0.7 to 3.1 TeV. The gradient varies with energy as $E^γ$, where $γ\approx-0.5$. At a median energy $\leq$1.0 TeV, this gradient corresponds to a dipole anisotropy of at least 1\%, or possibly a weaker anisotropy of higher order. This new type of observation opens the opportunity to study interplanetary magnetic structures using air shower arrays around the world, complementing existing in situ and remote measurements of plasma properties.

preprint2026arXiv

Unsupervised Stereo via Multi-Baseline Geometry-Consistent Self-Training

Photometric loss and pseudo-label-based self-training are two widely used methods for training stereo networks on unlabeled data. However, they both struggle to provide accurate supervision in occluded regions. The former lacks valid correspondences, while the latter's pseudo labels are often unreliable. To overcome these limitations, we present S$^3$, a simple yet effective framework based on multi-baseline geometry consistency. Unlike conventional self-training where teacher and student share identical stereo pairs, S$^3$ assigns them different target images, introducing natural visibility asymmetry. Regions occluded in the student's view often remain visible and matchable to the teacher, enabling reliable pseudo labels even in regions where photometric supervision fails. The teacher's disparities are rescaled to align with the student's baseline and used to guide student learning. An occlusion-aware weighting strategy is further proposed to mitigate unreliable supervision in teacher-occluded regions and to encourage the student to learn robust occlusion completion. To support training, we construct MBS20K, a multi-baseline stereo dataset synthesized using the CARLA simulator. Extensive experiments demonstrate that S$^3$ provides effective supervision in both occluded and non-occluded regions, achieves strong generalization performance, and surpasses previous state-of-the-art methods on the KITTI 2015 and 2012 benchmarks.

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Computer Vision Artificial Intelligence Machine Learning astro-ph.CO astro-ph.GA Graphics

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