Xiaokun Li | Searcharxiv

arXiv2026

Long-lived sterile neutrinos from axionlike particles at the Super Tau-Charm Facility

We study the search prospect of long-lived heavy neutral leptons (HNLs) pair produced in decays of axionlike particles (ALPs) at the proposed Super Tau--Charm Facility (STCF), focusing on the center-of-mass energy of $\sqrt{s}=3.773$ GeV. The ALPs are assumed to originate from $D^\pm$-meson decays in association with a charged pion. We perform both truth-level and detector-level Monte Carlo simulations and obtain the expected sensitivity reach to the mixing parameter between the HNL and the electron neutrino, $|V_{eN}|^2$, with a displaced-vertex search at STCF. We find that STCF can probe values of $|V_{eN}|^2$ about one-to-two orders of magnitude beyond the existing bounds. We also perform an approximate reinterpretation of a search for HNLs at the CHARM experiment, which is subject to model-dependent assumptions on the production and kinematic distributions, and find that beam-dump experiments may provide strong complementary constraints.

hep-ph↗

arXiv2026

Dual Feature Decoupling for Fine-Grained OOD Detection

Out-of-distribution detection (OOD) is an indispensable technique when applying machine learning models to real-world scenarios. Most existing OOD detection methods have been developed under the idealized assumption of large inter-class distributional differences, while largely overlooking fine-grained tasks characterized by subtle variations, such as medical image classification and vehicle recognition. The high visual similarity among fine-grained subcategories, together with the interference of background factors, makes OOD detection extremely challenging. To tackle this problem, we propose a novel Dual Feature Decoupling Network (DFDNet), which addresses fine-grained OOD detection from the perspective of feature disentanglement. The proposed DFDNet comprises two key components: a spatial-frequency decoupling module and a reconstruction-guided decoupling module. The spatial-frequency decoupling module is designed to preserve content features that are discriminative for classification while suppressing task-irrelevant style information. On the other hand, the reconstruction-guided decoupling module introduces a novel pixel-level adversarial reconstruction task to further remove low-level, non-discriminative information and enhance category-specific high-level semantic representations. Extensive experiments demonstrate that our method achieves competitive performance improvements on multiple datasets.

cs.CV↗