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Chenlu Hu

Publications and source records attributed to Chenlu Hu.

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A practical methodology for $\Lambda$ global polarization extraction in fixed-target experiments

Non-central heavy-ion collisions generate large orbital angular momentum in the created medium, which leads to polarization of final-state particles via spin-orbit coupling, known as global spin polarization. The observation of significant global polarization of $\Lambda$ hyperon in heavy-ion collisions indicates that the quark-gluon plasma is the most vortical fluid known in nature. Exploring $\Lambda$ global polarization at lower energies is important for understanding spin dynamics across different regions of the quantum chromodynamics (QCD) phase diagram. Low-energy nuclear experiments are typically conducted with asymmetric detector acceptance, as in fixed-target collisions at RHIC-STAR, and at facilities such as FAIR, NICA, HIAF and HIRFL-CSR. The asymmetric rapidity coverage in these experiments enhances the coupling between directed flow and detector inefficiencies, creating significant bias in $\Lambda$ global polarization measurements. In this paper, we propose a methodology to eliminate such bias arising from asymmetric detector acceptance. The method is validated using realistic detector simulations based on the STAR fixed-target configuration.

physics.data-an

Studies of nuclear equation of state with the HIRFL-CSR external-target experiment

The HIRFL-CSR external-target experiment (CEE) under construction is expected to provide novel opportunities to the studies of the thermodynamic properties, namely the equation of state of nuclear matter (nEOS) with heavy ion collisions at a few hundreds MeV/u beam energies. Based on Geant 4 packages, the fast simulations of the detector responses to the collision events generated using transport model are conducted. The overall performance of CEE, including spatial resolution of hits, momentum resolution of tracks and particle identification ability has been investigated. Various observables proposed to probe the nEOS, such as the production of light clusters, $\rm t/^3He$ yield ratio, the radial flow, $\pi^{-}/\pi^{+}$ yield ratio and the neutral kaon yields, have been reconstructed. The feasibility of studying nEOS beyond the saturation density via the aforementioned observables to be measured with CEE has been demonstrated.

nucl-ex

LoG-CAN: local-global Class-aware Network for semantic segmentation of remote sensing images

Remote sensing images are known of having complex backgrounds, high intra-class variance and large variation of scales, which bring challenge to semantic segmentation. We present LoG-CAN, a multi-scale semantic segmentation network with a global class-aware (GCA) module and local class-aware (LCA) modules to remote sensing images. Specifically, the GCA module captures the global representations of class-wise context modeling to circumvent background interference; the LCA modules generate local class representations as intermediate aware elements, indirectly associating pixels with global class representations to reduce variance within a class; and a multi-scale architecture with GCA and LCA modules yields effective segmentation of objects at different scales via cascaded refinement and fusion of features. Through the evaluation on the ISPRS Vaihingen dataset and the ISPRS Potsdam dataset, experimental results indicate that LoG-CAN outperforms the state-of-the-art methods for general semantic segmentation, while significantly reducing network parameters and computation. Code is available at~\href{https://github.com/xwmaxwma/rssegmentation}{https://github.com/xwmaxwma/rssegmentation}.

cs.CV