arXiv subjects

G. Song

Publications and source records attributed to G. Song.

4 recordsLinked to original sources

arXiv2026

CAGE: An Internal Source Scanning Cryostat for HPGe Characterization

The success of current and future-generation neutrinoless double beta decay experiments relies on the ability to eliminate or reduce extraneous backgrounds. In addition to constructing experiments using radiopure materials and handling in underground laboratories, it is necessary to understand and reduce known backgrounds in data analysis. The Large Enriched Germanium Experiment for Neutrinoless double beta Decay is searching for this decay using 76Ge-enriched high-purity germanium detectors submerged in an active liquid argon veto. A significant background in LEGEND is surface events from shallowly-impinging radiation on detector surfaces. In this paper we introduce the Collimated Alphas, Gammas, and Electrons (CAGE) scanning system, an internal-source scanning vacuum cryostat, designed to perform studies of surface events on sensitive surfaces of HPGe in a surface-lab. CAGE features a collimated radionuclide source inside a movable infrared shield that is able to perform precision scans of detector surfaces by utilizing three independent motor stages for source positioning. This allows detailed studies of pulse shapes as a function of source position and incident angle, where defining features can be extracted and exploited for removing surface backgrounds in data analysis in LEGEND. In this paper, we describe CAGE and demonstrate its performance with a commissioning run with 241Am. The commissioning run was completed with the source at normal incidence, and we estimate a beam spot precision of 3.1 mm, which includes positioning uncertainties and the beam-spot size. Using the 59.5 keV gamma population from 241Am, we show that low-energy photon events near the passivated surface feature risetimes that increase with radial distance from the detector center. We suggest a specific metric that can be used to discriminate low-energy gamma backgrounds in LEGEND with similar characteristics.

physics.ins-det↗

arXiv2025

6Vision: Image-encoding-based IPv6 Target Generation in Few-seed Scenarios

Efficient global Internet scanning is crucial for network measurement and security analysis. While existing target generation algorithms demonstrate remarkable performance in large-scale detection, their efficiency notably diminishes in few-seed scenarios. This decline is primarily attributed to the intricate configuration rules and sampling bias of seed addresses. Moreover, instances where BGP prefixes have few seed addresses are widespread, constituting 63.65% of occurrences. We introduce 6Vision as a solution to tackle this challenge by introducing a novel approach of encoding IPv6 addresses into images, facilitating comprehensive analysis of intricate configuration rules. Through a process of feature stitching, 6Vision not only improves the learnable features but also amalgamates addresses associated with configuration patterns for enhanced learning. Moreover, it integrates an environmental feedback mechanism to refine model parameters based on identified active addresses, thereby alleviating the sampling bias inherent in seed addresses. As a result, 6Vision achieves high-accuracy detection even in few-seed scenarios. The HitRate of 6Vision shows a significant improvement ranging from 181% to 2,490% compared to existing algorithms, while the CoverNum increases by a factor of 1.18 to 11.20 times. Additionally, 6Vision can function as a preliminary detection module for existing algorithms, yielding a conversion gain (CG) ranging from 242% to 2,081%. Ultimately, we achieve a conversion rate (CR) of 28.97% for few-seed scenarios. We develop the IPv6 hitlist Patch, which augments current target generation algorithms for large-scale address detection, thereby effectively supporting IPv6 network measurement and security analysis.

cs.NI↗

arXiv2022

A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform

With the development of industry, drones are appearing in various field. In recent years, deep reinforcement learning has made impressive gains in games, and we are committed to applying deep reinforcement learning algorithms to the field of robotics, moving reinforcement learning algorithms from game scenarios to real-world application scenarios. We are inspired by the LunarLander of OpenAI Gym, we decided to make a bold attempt in the field of reinforcement learning to control drones. At present, there is still a lack of work applying reinforcement learning algorithms to robot control, the physical simulation platform related to robot control is only suitable for the verification of classical algorithms, and is not suitable for accessing reinforcement learning algorithms for the training. In this paper, we will face this problem, bridging the gap between physical simulation platforms and intelligent agent, connecting intelligent agents to a physical simulation platform, allowing agents to learn and complete drone flight tasks in a simulator that approximates the real world. We proposed a reinforcement learning framework based on Gazebo that is a kind of physical simulation platform (ROS-RL), and used three continuous action space reinforcement learning algorithms in the framework to dealing with the problem of autonomous landing of drones. Experiments show the effectiveness of the algorithm, the task of autonomous landing of drones based on reinforcement learning achieved full success.

cs.RO↗

arXiv1994

Coulomb plus Nuclear Scattering in Momentum Space for Coupled Angular-Momentum States

The Vincent--Phatak procedure for solving the momentum-space Schrodinger equation with combined Coulomb-plus-short-range potentials is extended to angular momentum states coupled by an optical potential---as occurs in spin 1/2 times 1/2 scattering. A generalization of the Blatt--Biedenharn phase shift parameterization is derived and applied to 500 MeV polarized-proton scattering from He3 and C13. The requisite high-precision partial-wave expansions and integrations are described.

nucl-th↗