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Researcher profile

Lin Chen

Lin Chen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
astro-ph.HEArtificial IntelligenceComputer Visioncond-mat.mes-hallcond-mat.mtrl-scihep-exphysics.app-phSoftware Engineering

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Published work

6 published item(s)

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.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

An Ultrahigh-energy $γ$-ray Bubble Powered by a Super PeVatron

We report the detection of a $γ$-ray bubble spanning at least 100$\rm deg^2$ in ultra high energy (UHE) up to a few PeV in the direction of the star-forming region Cygnus X, implying the presence Super PeVatron(s) accelerating protons to at least 10 PeV. A log-parabola form with the photon index $Γ(E) = (2.71 \pm 0.02) + (0.11 \pm 0.02) \times \log_{10} (E/10 \ {\rm TeV})$ is found fitting the gamma-ray energy spectrum of the bubble well. UHE sources, `hot spots' correlated with very massive molecular clouds, and a quasi-spherical amorphous $γ$-ray emitter with a sharp central brightening are observed in the bubble. In the core of $\sim 0.5^{\circ}$, spatially associating with a region containing massive OB association (Cygnus OB2) and a microquasar (Cygnus X-3), as well as previously reported multi-TeV sources, an enhanced concentration of UHE $γ$-rays are observed with 2 photons at energies above 1 PeV. The general feature of the bubble, the morphology and the energy spectrum, are reasonably reproduced by the assumption of a particle accelerator in the core, continuously injecting protons into the ambient medium.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Electric-current control of anomalous Hall effect

We demonstrate robust and reversible electric-current control of the anomalous Hall effect (AHE) in a two-dimensional WTe2/Fe3GeTe2 (FGT) stack. Applying a current through Td-WTe2 leads to a giant modulation of the AHE of the adjacent FGT layer, with the relative change of the AHE conductivity exceeding 180%. Control experiments show that i) the observed effect is absent in pure FGT, ii) the modulation weakens in thicker FGT films, confirming its interfacial origin, and iii) the modulation peaks for bilayer WTe2, indicating that the Berry-curvature dipole (BCD) plays the dominant role in the modulation. We propose that the charge current I generates an out-of-plane magnetization Mz via BCD in WTe2 and Mz modifies the exchange splitting of FGT via the inverse magnetic proximity effect, thereby altering its Berry curvature and nontrivially influencing the AHE. The demonstrated method of AHE control offers new possibilities for magnetism control, i.e., for the study of AHE-transistors as well as electric-current control of quantum magnets, especially magnetic insulators.

0 citations0 reviewsNo code linkedOpen access
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.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

SaaSBench: Exploring the Boundaries of Coding Agents in Long-Horizon Enterprise SaaS Engineering

As autonomous coding agents become capable of handling increasingly long-horizon tasks, they have gradually demonstrated the potential to complete end-to-end software development. Although existing benchmarks have recently evolved from localized code editing to from-scratch project generation, they remain confined to structurally simplified, single-stack applications. Consequently, they fail to capture the heterogeneous environments, full-stack orchestration, and system-level complexity of real enterprise Software as a Service (SaaS) systems, leaving a critical gap in assessing agents under realistic engineering constraints. To fill this gap, we introduce SaaSBench, the first benchmark designed to explore the boundaries of AI agents in enterprise SaaS engineering. Spanning 30 complex tasks across 6 SaaS domains with 5,370 validation nodes, it incorporates 8 programming languages, 6 databases, and 13 frameworks to meticulously mirror real-world software heterogeneity. Furthermore, we design a dependency-aware hybrid evaluation paradigm tailored for complex systems with long horizons and multi-component coupling, enabling fine-grained, reproducible assessment. Crucially, our extensive experiments reveal a striking insight: the primary bottleneck for state-of-the-art agents is not generating isolated code logic, but successfully configuring and integrating a multi-component system. Over 95\% of task failures occur before agents even reach deep business logic, with models often falling victim to overconfidence and prematurely halting during foundational system setup, or getting trapped in ineffective debugging loops. We hope SaaSBench serves as a practical and challenging testbed to drive the evolution of reliable, system-level coding agents. The code is available at \url{https://github.com/ShadeCloak/SaaSbench}.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision

While Unified Multimodal Models (UMMs) have achieved remarkable success in cross-modal comprehension, a significant gap persists in their ability to leverage such internal knowledge for high-quality generation. We formalize this discrepancy as Conduction Aphasia, a phenomenon where models accurately interpret multimodal inputs but struggle to translate that understanding into faithful and controllable synthesis. To address this, we propose UniCorn, a simple yet elegant self-improvement framework that eliminates the need for external data or teacher supervision. By partitioning a single UMM into three collaborative roles: Proposer, Solver, and Judge, UniCorn generates high-quality interactions via self-play and employs cognitive pattern reconstruction to distill latent understanding into explicit generative signals. To validate the restoration of multimodal coherence, we introduce UniCycle, a cycle-consistency benchmark based on a Text to Image to Text reconstruction loop. Extensive experiments demonstrate that UniCorn achieves comprehensive and substantial improvements over the base model across six general image generation benchmarks. Notably, it achieves SOTA performance on TIIF(73.8), DPG(86.8), CompBench(88.5), and UniCycle while further delivering substantial gains of +5.0 on WISE and +6.5 on OneIG. These results highlight that our method significantly enhances T2I generation while maintaining robust comprehension, demonstrating the scalability of fully self-supervised refinement for unified multimodal intelligence.

0 citations0 reviewsNo code linkedOpen access