Nuclear modification of high transverse momentum particle production in p+A collisions at RHIC and LHC

We present results and predictions for the nuclear modification of the differential cross sections for inclusive light hadron and prompt photon production in minimum bias d+Au collisions at $\sqrt{s} = 200$ GeV and minimum bias p+Pb collisions at $\sqrt{s} = 5$ TeV at RHIC and LHC, respectively. Our calculations combine the leading order perturbative QCD formalism with cold nuclear matter effects that arise from the elastic, inelastic and coherent multiple scattering of partons in large nuclei. We find that a theoretical approach that includes the isospin effect, Cronin effect, cold nuclear matter energy loss and dynamical shadowing can describe the RHIC d+Au data rather well. The LHC p+Pb predictions will soon be confronted by new experimental results to help clarify the magnitude and origin of cold nuclear matter effects and facilitate precision dense QCD matter tomography.Comment: 8 pages, 4 figure

Semantic Segmentation of Fruits on Multi-sensor Fused Data in Natural Orchards

Semantic segmentation is a fundamental task for agricultural robots to understand the surrounding environments in natural orchards. The recent development of the LiDAR techniques enables the robot to acquire accurate range measurements of the view in the unstructured orchards. Compared to RGB images, 3D point clouds have geometrical properties. By combining the LiDAR and camera, rich information on geometries and textures can be obtained. In this work, we propose a deep-learning-based segmentation method to perform accurate semantic segmentation on fused data from a LiDAR-Camera visual sensor. Two critical problems are explored and solved in this work. The first one is how to efficiently fused the texture and geometrical features from multi-sensor data. The second one is how to efficiently train the 3D segmentation network under severely imbalance class conditions. Moreover, an implementation of 3D segmentation in orchards including LiDAR-Camera data fusion, data collection and labelling, network training, and model inference is introduced in detail. In the experiment, we comprehensively analyze the network setup when dealing with highly unstructured and noisy point clouds acquired from an apple orchard. Overall, our proposed method achieves 86.2% mIoU on the segmentation of fruits on the high-resolution point cloud (100k-200k points). The experiment results show that the proposed method can perform accurate segmentation in real orchard environments

The transverse momentum distribution of hadrons within jets

We study the transverse momentum distribution of hadrons within jets, where the transverse momentum is defined with respect to the standard jet axis. We consider the case where the jet substructure measurement is performed for an inclusive jet sample $pp\to\text{jet}+X$. We demonstrate that this observable provides new opportunities to study transverse momentum dependent fragmentation functions (TMDFFs) which are currently poorly constrained from data, especially for gluons. The factorization of the cross section is obtained within Soft Collinear Effective Theory (SCET), and we show that the relevant TMDFFs are the same as for the more traditional processes semi-inclusive deep inelastic scattering (SIDIS) and electron-positron annihilation. Different than in SIDIS, the observable for the in-jet fragmentation does not depend on TMD parton distribution functions which allows for a cleaner and more direct probe of TMDFFs. We present numerical results and compare to available data from the LHC.Comment: 28 pages, 3 figures, published versio