SHiP Experiment Research Articles

Probing the mixing between sterile and tau neutrinos in the SHiP experiment

We study the expected sensitivity to the mixing between sterile and tau neutrinos directly from the tau neutrino disappearance in the high-energy fixed target experiment. Here, the beam energy is large enough to produce tau neutrinos at the target with large luminosity. During their propagation to the detector, tau neutrinos may oscillate into sterile neutrinos. By examining the energy spectrum of the observed tau neutrino events, we can probe the mixing between sterile and tau neutrinos directly. In this paper, we consider Scattering and Neutrino Detector (SND) at SHiP experiment as a showcase, which uses 400 GeV protons from SPS at CERN, and expect to observe 7,300 tau and anti-tau neutrinos from the 2 × 1020 POT for 5 years operation. Assuming the uncertainty of 10%, we find the sensitivity |Uτ4|2 ∼ 0.08 (90% CL) for ∆m412\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\Delta {m}_{41}^2 $$\\end{document} ∼ 500 eV2 with 10% background to the signal. We also consider a far SND at the end of the SHiP Hidden Sector Decay Spectrometer (HSDS), in which case the sensitivity would be enhanced to |Uτ4|2 ∼ 0.02. Away from this mass, the sensitivity becomes lower than |Uτ4|2 ∼ 0.15 for ∆m412\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\Delta {m}_{41}^2 $$\\end{document} ≲ 100 eV2 or ∆m412\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ \\Delta {m}_{41}^2 $$\\end{document} ≳ 104eV2.

A Method for Predicting Inertial Navigation System Positioning Errors Using a Back Propagation Neural Network Based on a Particle Swarm Optimization Algorithm.

In order to reduce the position errors of the Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation system during GPS denial, this paper proposes a method based on the Particle Swarm Optimization-Back Propagation Neural Network (PSO-BPNN) to replace the GPS for positioning. The model relates the position information, velocity information, attitude information output by the SINS, and the navigation time to the position errors between the position information output by the SINS and the actual position information. The performance of the model is compared with the BPNN through an actual ship experiment. The results show that the PSO-BPNN can obviously reduce the position errors in the case of GPS signal denial.

A method for estimating ship berthing angle based on 2D bird's eye view point cloud

The accurate estimation of berthing angle is essential for the parallel berthing of inshore ships. Herein, a new method for calculating the ship berthing angle using a shore-based LiDAR system is proposed. First, the LiDAR-scanned point cloud data is preprocessed, including coordinate conversion, noise filtering, and data downsampling. Then, the processed point cloud data is projected onto a two-dimensional (2D) plane, and the height value is used as the pixel value to generate bird's eye view (BEV) images. Finally, an improved Hough transform is proposed to detect the straight line representing the ship's nearshore profile in the BEV images and replace the ship midline with the detected line for calculating the berthing angle. The performance of the proposed method is verified by applying it on simulated and real ship data. Simulation experiment results show that the maximum absolute error of our method is only 0.151°. Real ship experiments were conducted at Dalian Port (China) to verify the reliability and stability of the method compared with other berthing angle estimation methods and the compass equipment. Overall, the proposed method can effectively obtain the berthing angle of ships, assist in parallel berthing, and improve berthing efficiency.

Modular production of small ship models using 3D printing for model tests

This paper presents a cost-effective approach for the production of small-scale ship models, particularly suitable for model testing of high-speed vessels characterized by a high Froude number. In contrast to traditional approach employed in ship model production, the proposed approach uses 3D printing technology integrated with modularity. This strategy enables the mitigation of expenses associated with procuring 3D printers, facilitates dynamic design changes, and accommodates parallel printing using multiple printers, thereby resulting in a reduction in production time. The manufacturing process involves modularization of the original CAD model, build scheduling, interface design, assembly, fabrication, and painting. A case study is presented, which demonstrates the proposed process in ship model production using 3D printing. The geometric similarity between the built model and the original CAD model was assessed by generating a point cloud using a 3D scanner. To evaluate the resistance performance of the 3D printed ship model, experiment and numerical simulation were conducted. These results indicate a high level of similarity and reliability.

An Improved VO Method for Collision Avoidance of Ships in Open Sea

In order to effectively deal with collisions in various encounter situations in open water environments, a ship collision avoidance model was established, and multiple constraints were introduced into the velocity obstacle method, a method to determine the ship domain by calculating the safe distance of approach was proposed. At the same time, the ship collision avoidance model based on the ship domain is analyzed, and the relative velocity set of the collision cone is obtained by solving the common tangent line within the ellipse. The timing of starting collision avoidance is determined by calculating the ship collision risk, and a method for ending collision avoidance is proposed. Finally, by comparing the simulation experiments of the improved algorithm with those of the traditional algorithm and the actual ship experiment results of manual ship maneuvering, it is shown that the method can effectively avoid collisions based on safe encounter distances that comply with navigation experience in different encounter situations. At the same time, it has better performance in collision avoidance behavior. It has certain feasibility and practical applicability.

Wavelet Neural Network-Based Half-Period Predictive Roll-Reduction Control Using a Fin Stabilizer at Zero Speed

Among the commonly used ship-stabilizing devices, the fin stabilizer is the most effective. Since the lift force of the conventional fin stabilizer is proportional to the square of the incoming flow velocity, it has a better anti-rolling effect at higher speeds but a poor anti-rolling effect at low speeds and even no effect at zero speed. A combination of modelling analysis, simulation, and a model ship experiment is used in this paper to study the zero-speed roll-reduction control problem of the fin stabilizer. A simulation model of the rolling motion of a polar expedition ship is established. The lift model of the fin stabilizer at zero speed is established using the theory of fluid mechanics. The proportional–integral–differential (PID) controller is selected to control the fin to achieve zero-speed roll reduction. To obtain a better anti-rolling control effect under variable sea conditions, a wavelet neural network (WNN)-based half-period prediction algorithm is adopted to update and adjust PID control parameters in real time. A simulation was carried out, and the effectiveness of the proposed predictive control algorithm is proved. A reduced-scale ship model was established to carry out the water tank experiment, and the results verify the theoretical analysis and simulation. The results also verify the effectiveness of the proposed control strategy.

Vertical diffusion of bubbles injected beneath a flat-bottomed ship for frictional drag reduction

There has been concern about the short effective distance of frictional drag reduction achieved by air lubrication, but recent model ship experiments have shown over 30 m longer than our expectation. Also, the reduction ratio can be improved through the artificial generation of void waves appearing beneath the hull. These topics relate to the spatiotemporal bubble distribution and an understanding of them requires clarification of the distribution. In this study, optical probes were used to measure the distributions of bubble states beneath a 20-m long flat-bottomed ship towed with 6–8 m/s. The measurements explained the long effective distance of drag reduction and the process of void wave generation and development. The drag reduction ratio was about 7–25% and mainly depended on the void fraction within 10 mm of the hull. The vertical distribution of the void fraction remained unchanged over long downstream distances. The development of void waves was affected by the streamwise velocity difference between bubble clusters near the hull. The velocity was determined by the number density of the bubbles, decreasing to the half of ship speed until reaching a certain number density, 100 per meter in our experiments, and then becoming constant at higher density.

Human-induced noise study for cruise cabin: Numerical analysis and experimental validation

Human-induced vibration and noise significantly influence passenger ship comfort. Predicting and controlling human-induced vibration and noise are imperative. The excitation sources of the vibration and noise are human-induced loading (HIL). HIL models are introduced to describe the impact loading with time-variant stochastic fields by single-person or multi-person walking. A numerical analysis of human-induced noise is implemented in a cabin corridor of a passenger ship under single-person or multi-person walking. Finally, a real ship experiment test is implemented, validating the proposed method's feasibility and effectiveness. The results provide a better understanding of passenger ships' vibration and noise reduction design.

Research on online interactive identification method for motion model of double propeller propulsion unmanned surface vehicle based on ESO disturbance estimation

In this paper, the online parameter identification problem of the mathematical model of USV considering the characteristics of the actuator is studied. A data-driven mathematical model of motion is very meaningful to realize trajectory prediction and adaptive motion control of USV. An interactive identification algorithm (ESO-MILS) based on Extended State Observer (ESO) is proposed. The robustness of online identification is improved by expanding the state observer to estimate the current disturbance without making artificial assumptions. Specifically, the three-degree-of-freedom dynamic equation of the double propeller propulsion USV is constructed. A linear model for online identification is derived by parameterization. Based on the least square criterion function, it is proved that the interactive identification method with disturbance estimation can improve the identification accuracy from the perspective of mathematical expectation. The extended state observer is designed to estimate the unknown disturbance in the model. The online interactive update improves the disturbance immunity of the identification algorithm. Finally, the effectiveness of the interactive identification algorithm is verified by simulation experiment and real ship experiment.

Research on the influence factors of ship resistance and navigation drag reduction

With economic growth, ship transportation plays a major role in global logistics. The optimization of the ship form based on minimum resistance has been the research focus of ship builders for hundreds of years, but the actual process is quite complex, which also brings huge benefits. Ship drag reduction is not only conducive to industrial economic growth, but also helpful to improve the global energy consumption and environmental problems. In this paper, the Reynolds number and the Froude number are obtained by a dimensional analysis, and then the data are obtained from the model ship experiment with the same Froude number by the law of similarity. The total resistance is subdivided into four kinds with different causes, namely the friction resistance, wave-making resistance, form resistance, and additional resistance. In this way, the ship resistance can be reduced more scientifically and efficiently.

RPC-based Muon Identification System for the neutrino detector of the SHiP experiment

The SHiP experiment has been proposed at CERN to shed light on phenomena still unexplained in the framework of the Standard Model, such as the nature of dark matter, the baryonic asymmetry of the Universe and the neutrino oscillations, searching for hints of New Physics. A section of the detector will be dedicated to the study of neutrino physics with special emphasis on tau neutrino properties, still very poorly measured. A system to identify the muons produced in neutrino interactions, based on RPC technology, has been proposed and it is presented in detail in this paper.

INS/GPS Integrated Navigation for Unmanned Ships Based on EEMD Noise Reduction and SSA-ELM

The primary problem faced by the integrated navigation system based on the inertial navigation system (INS) and global positioning system (GPS) is providing reliable navigation and positioning solutions during GPS failure. Thus, this study proposes an innovative integrated navigation algorithm to address the limitation of precise positioning when GPS fails. First, for the limitation of noise interference in INS, noise reduction technology based on ensemble empirical mode decomposition (EEMD) is proposed to improve the quality of the INS signal and enhance the noise reduction effect. Second, an INS/GPS integrated framework based on the sparrow search algorithm (SSA) and extreme learning machine (ELM) is proposed. During normal GPS conditions, SSA-ELM is used to develop a high-precision prediction model to estimate differences between INS and GPS. When the GPS signal is interrupted, the difference predicted by SSA-ELM is used as the measurement input and the INS is corrected. To confirm the effectiveness of this method, a real ship experiment is conducted with other commonly used methods. The experimental results demonstrate that the proposed method can improve positioning accuracy and reliability when GPS is interrupted.

A Polar Robust Kalman Filter Algorithm for DVL-Aided SINSs Based on the Ellipsoidal Earth Model.

Autonomous underwater vehicles (AUVs) play an increasingly essential role in the field of polar ocean exploration, and the Doppler velocity log (DVL)-aided strapdown inertial navigation system (SINS) is widely used for it. Due to the rapid convergence of the meridians, traditional inertial navigation mechanisms fail in the polar region. To tackle this problem, a transverse inertial navigation mechanism based on the earth ellipsoidal model is designed in this paper. Influenced by the harsh environment of the polar regions, unknown and time-varying outlier noise appears in the output of DVL, which makes the performance of the standard Kalman filter degrade. To address this issue, a robust Kalman filter algorithm based on Mahalanobis distance is used to adaptively estimate measurement noise covariance; thus, the Kalman filter gain can be modified to weight the measurement. A trial ship experiment and semi-physical simulation experiment were carried out to verify the effectiveness of the proposed algorithm. The results demonstrate that the proposed algorithm can effectively resist the influence of DVL outliers and improve positioning accuracy.

Multicomponent scalar dark matter at high-intensity proton beam experiments

We study a scalar dark matter (DM) model with two DM species coupled to the standard model (SM) particles via a sub-GeV dark photon. In this model, we find that DM conversion occurs through the dark photon and it plays a fundamental role in setting the observed relic abundance. Furthermore, the two DM candidates can be produced at fixed-target experiments a la beam-dump. Detailed predictions for signal and backgrounds are obtained with the help of MadDump and NuWro Monte Carlo generators. We explore the potential reach on the sensitivity of DUNE near detector and SHiP experiment, and we find that portions of the parameter space will be within reach of the two experiments.

First measurement of the surface tension of a liquid scintillator based on Linear Alkylbenzene (HYBLENE 113)

We measured the surface tension of linear alkylbenzene (LAB) HYBLENE 113 mixed with Diphenyloxazole (PPO) as well as of pure LAB HYBLENE 113 as part of material studies for the liquid-scintillator based surround background tagger (SBT) in the proposed SHiP experiment. The measurement was performed using the iron wire method and the surface tension for linear alkyl benzene HYBLENE 113 plus PPO was found to be (30.0 ± 0.6) mN/m 22.0 ± 0.5°C and for pure HYBLENE 113, (29.2 ± 0.6) mN/m at 21.0 ± 0.5°C.

Augmented Reality

Augmented Reality (AR) interfaces have happened intentional widely over the last few decades, accompanying a increasing number of consumer-located experiments. In this paper, we orderly review 10 age of ultimate effective AR consumer studies, from 2005 to 2014. A total of 291 documents accompanying 369 individual consumer studies have existed inspected and top-secret established their request fields. The basic offering of the review search out present the broad countryside of consumer-located AR research, and to determine a high-ranking view of by what method that countryside has exchanged. We rehash the high-ranking gifts each type of documents, and present instances of ultimate powerful consumer studies. We again label fields place skilled have happened few consumer studies, and convenience for future research. Among other things, we find that skilled is a increasing flow toward handheld AR consumer studies, what most studies are administered in workshop backgrounds and do not include ship experiment. This research will suffice for AR scientists the one be going to attend best practices in plotting their own AR consumer studies.

Nonstandard interactions and sterile neutrinos in super-weak U(1) extension of the standard model

The super-weak force is a minimal, anomaly-free U(1) extension of the standard model (SM), designed to explain the origin of (i) neutrino masses and mixing matrix elements, (ii) dark matter, (iii) cosmic inflation, (iv) stabilization of the electroweak vacuum and (v) leptogenesis. We discuss the neutrino sector of this model in detail and study the allowed parameter space of the neutrino Yukawa matrices and mixing matrix elements. The model generates nonstandard neutrino interactions, whose allowed experimental limits are used to constrain the parameter space of the model. We provide benchmark points in the relevant parameter space that fall within the sensitivity region of the SHiP and MATHUSLA experiments.

Modeling of relative collision risk based on the ships group situation

The collision risk of ships is a fuzzy concept, which is the measurement of the likelihood of a collision between ships. Most of existed studies on the risk of multi-ship collision are based on the assessment of two-ship collision risk, and collision risk between the target ship and each interfering ship is calculated respectively, to determine the key avoidance ship. This method is far from the actual situation and has some defects. In open waters, it is of certain reference value when there are fewer ships, but in busy waters, it cannot well represent the risk degree of the target ship, since it lacks the assessment of the overall risk of the perceived area of the target ship. Based on analysis of complexity of ships group situation, the concept of relative domain was put forward and the model was constructed. On this basis, the relative collision risk was proposed, and the corresponding model was obtained, so as to realize risk assessment. Through the combination of real ship and simulation experiments, the variation trend, stability and sensitivity of the model were verified. The results showed that risk degree of the environment of ships in open and busy waters could be well assessed, and good references for decision-making process of ships collision avoidance could be provided.

Segmentation of EM showers for neutrino experiments with deep graph neural networks

We introduce a first-ever algorithm for the reconstruction of multiple showers from the data collected with electromagnetic (EM) sampling calorimeters. Such detectors are widely used in High Energy Physics to measure the energy and kinematics of in-going particles. In this work, we consider the case when many electrons pass through an Emulsion Cloud Chamber (ECC) brick, initiating electron-induced electromagnetic showers, which can be the case with long exposure times or large input particle flux. For example, SHiP experiment is planning to use emulsion detectors for dark matter search and neutrino physics investigation. The expected full flux of SHiP experiment is about 1020 particles over five years. To reduce the cost of the experiment associated with the replacement of the ECC brick and off-line data taking (emulsion scanning), it is decided to increase exposure time. Thus, we expect to observe a lot of overlapping showers, which turn EM showers reconstruction into a challenging point cloud segmentation problem. Our reconstruction pipeline consists of a Graph Neural Network that predicts an adjacency matrix and a clustering algorithm. We propose a new layer type (EmulsionConv) that takes into account geometrical properties of shower development in ECC brick. For the clustering of overlapping showers, we use a modified hierarchical density-based clustering algorithm. Our method does not use any prior information about the incoming particles and identifies up to 87% of electromagnetic showers in emulsion detectors. The achieved energy resolution over 16,577 showers is σE/E = (0.095 ± 0.005) + (0.134 ± 0.011)/√(E). The main test bench for the algorithm for reconstructing electromagnetic showers is going to be SND@LHC.

2.4 MW급 4행정 주기관의 부하에 따른 배기가스 특성 분석 (어로실습선 새바다호를 중심으로)

The engines used as the main engine of a ship vary depending on the fuel, size and output. Most of the ships are currently using internal combustion engines. This study examined the exhaust gas and air pollutants emitted by SAEBADA, the fishing training ship of Gyeongsang National University. It has a 2,427 kW main engine of Nigatta Engineering that uses marine gas oil (MGO) as fuel oil. It was measured in real time for each load. According to this measurement results, the engine of the ship is operated at an optimal load to reduce the emission concentration of air pollutants in the exhaust gas. For two days from May 13th to May 14th, 2020, a real ship emission gas measurement test was conducted on SAEBADA, the fishing training ship of Gyeongsang National University while operating the ship in four modes that were mainly composed of changes in output. The gas phase materials, particulate materials, exhaust temperature, etc., were measured. As a result of the test, the nitrogen oxide (NOx) emission of SAEBADA was Tier II level. It was found that when marine gas oil (MGO) emitted relatively less air pollutants than heavy fuel oil (HFO). In addition, it was found that the nitrogen oxide (NOx) emission in the high-load region was relatively less than in the low-load region regardless of the oil type and the aging of the engine. This may be due to the combustion state. Therefore, we intend to operate SAEBADA in an efficient and eco-friendly with the results of these real ship experiments.