Muon and Cosmogenic Neutron Detection in Borexino

Borexino, a liquid scintillator detector at LNGS, is designed for the detection of neutrinos and antineutrinos from the Sun, supernovae, nuclear reactors, and the Earth. The feeble nature of these signals requires a strong suppression of backgrounds below a few MeV. Very low intrinsic radiogenic contamination of all detector components needs to be accompanied by the efficient identification of muons and of muon-induced backgrounds. Muons produce unstable nuclei by spallation processes along their trajectory through the detector whose decays can mimic the expected signals; for isotopes with half-lives longer than a few seconds, the dead time induced by a muon-related veto becomes unacceptably long, unless its application can be restricted to a sub-volume along the muon track. Consequently, not only the identification of muons with very high efficiency but also a precise reconstruction of their tracks is of primary importance for the physics program of the experiment. The Borexino inner detector is surrounded by an outer water-Cherenkov detector that plays a fundamental role in accomplishing this task. The detector design principles and their implementation are described. The strategies adopted to identify muons are reviewed and their efficiency is evaluated. The overall muon veto efficiency is found to be 99.992% or better. Ad-hoc track reconstruction algorithms developed are presented. Their performance is tested against muon events of known direction such as those from the CNGS neutrino beam, test tracks available from a dedicated External Muon Tracker and cosmic muons whose angular distribution reflects the local overburden profile. The achieved angular resolution is 3-5 deg and the lateral resolution is 35-50 cm, depending on the impact parameter of the crossing muon. The methods implemented to efficiently tag cosmogenic neutrons are also presented.Comment: 42 pages. 32 figures on 37 files. Uses JINST.cls. 1 auxiliary file (defines.tex) with TEX macros. submitted to Journal of Instrumentatio

Muon and Cosmogenic Neutron Detection in Borexino

Borexino, a liquid scintillator detector at LNGS, is designed for the detection of neutrinos and antineutrinos from the Sun, supernovae, nuclear reactors, and the Earth. The feeble nature of these signals requires a strong suppression of backgrounds below a few MeV. Very low intrinsic radiogenic contamination of all detector components needs to be accompanied by the efficient identification of muons and of muon-induced backgrounds. Muons produce unstable nuclei by spallation processes along their trajectory through the detector whose decays can mimic the expected signals; for isotopes with half-lives longer than a few seconds, the dead time induced by a muon-related veto becomes unacceptably long, unless its application can be restricted to a sub-volume along the muon track. Consequently, not only the identification of muons with very high efficiency but also a precise reconstruction of their tracks is of primary importance for the physics program of the experiment. The Borexino inner detector is surrounded by an outer water-Cherenkov detector that plays a fundamental role in accomplishing this task. The detector design principles and their implementation are described. The strategies adopted to identify muons are reviewed and their efficiency is evaluated. The overall muon veto efficiency is found to be 99.992% or better. Ad-hoc track reconstruction algorithms developed are presented. Their performance is tested against muon events of known direction such as those from the CNGS neutrino beam, test tracks available from a dedicated External Muon Tracker and cosmic muons whose angular distribution reflects the local overburden profile. The achieved angular resolution is 3-5 deg and the lateral resolution is 35-50 cm, depending on the impact parameter of the crossing muon. The methods implemented to efficiently tag cosmogenic neutrons are also presented.Comment: 42 pages. 32 figures on 37 files. Uses JINST.cls. 1 auxiliary file (defines.tex) with TEX macros. submitted to Journal of Instrumentatio

Muon and Cosmogenic Neutron Detection in Borexino

Borexino, a liquid scintillator detector at LNGS, is designed for the detection of neutrinos and antineutrinos from the Sun, supernovae, nuclear reactors, and the Earth. The feeble nature of these signals requires a strong suppression of backgrounds below a few MeV. Very low intrinsic radiogenic contamination of all detector components needs to be accompanied by the efficient identification of muons and of muon-induced backgrounds. Muons produce unstable nuclei by spallation processes along their trajectory through the detector whose decays can mimic the expected signals; for isotopes with half-lives longer than a few seconds, the dead time induced by a muon-related veto becomes unacceptably long, unless its application can be restricted to a sub-volume along the muon track. Consequently, not only the identification of muons with very high efficiency but also a precise reconstruction of their tracks is of primary importance for the physics program of the experiment. The Borexino inner detector is surrounded by an outer water-Cherenkov detector that plays a fundamental role in accomplishing this task. The detector design principles and their implementation are described. The strategies adopted to identify muons are reviewed and their efficiency is evaluated. The overall muon veto efficiency is found to be 99.992% or better. Ad-hoc track reconstruction algorithms developed are presented. Their performance is tested against muon events of known direction such as those from the CNGS neutrino beam, test tracks available from a dedicated External Muon Tracker and cosmic muons whose angular distribution reflects the local overburden profile. The achieved angular resolution is 3-5 deg and the lateral resolution is 35-50 cm, depending on the impact parameter of the crossing muon. The methods implemented to efficiently tag cosmogenic neutrons are also presented.Comment: 42 pages. 32 figures on 37 files. Uses JINST.cls. 1 auxiliary file (defines.tex) with TEX macros. submitted to Journal of Instrumentatio

Design of multidimensional digital filters by spectral transformations

Imperial Users onl

The eventual leadership in dynamic mobile networking environments

2007-2008 > Academic research: refereed > Refereed conference paperVersion of RecordPublishe

Viking '75 spacecraft design and test summary. Volume 1: Lander design

The Viking Mars program is summarized. The design of the Viking lander spacecraft is described

Optical Quantum Computation

We review the field of Optical Quantum Computation, considering the various implementations that have been proposed and the experimental progress that has been made toward realizing them. We examine both linear and nonlinear approaches and both particle and field encodings. In particular we discuss the prospects for large scale optical quantum computing in terms of the most promising physical architectures and the technical requirements for realizing them

Freeway Multisensor Data Fusion Approach Integrating Data from Cellphone Probes and Fixed Sensors

Freeway traffic state information from multiple sources provides sufficient support to the traffic surveillance but also brings challenges. This paper made an investigation into the fusion of a new data combination from cellular handoff probe system and microwave sensors. And a fusion method based on the neural network technique was proposed. To identify the factors influencing the accuracy of fusion results, we analyzed the sensitivity of those factors by changing the inputs of neural-network-based fusion model. The results showed that handoff link length and sample size were identified as the most influential parameters to the precision of fusion. Then, the effectiveness and capability of proposed fusion method under various traffic conditions were evaluated. And a comparative analysis between the proposed method and other fusion approaches was conducted. The results of simulation test and evaluation showed that the fusion method could complement the drawback of each collection method, improve the overall estimation accuracy, adapt to the variable traffic condition (free flow or incident state), suit the fusion of data from cellphone probes and fixed sensors, and outperform other fusion methods

An Online Adaptive Machine Learning Framework for Autonomous Fault Detection

The increasing complexity and autonomy of modern systems, particularly in the aerospace industry, demand robust and adaptive fault detection and health management solutions. The development of a data-driven fault detection system that can adapt to varying conditions and system changes is critical to the performance, safety, and reliability of these systems. This dissertation presents a novel fault detection approach based on the integration of the artificial immune system (AIS) paradigm and Online Support Vector Machines (OSVM). Together, these algorithms create the Artificial Immune System augemented Online Support Vector Machine (AISOSVM). The AISOSVM framework combines the strengths of the AIS and OSVM to create a fault detection system that can effectively identify faults in complex systems while maintaining adaptability. The framework is designed using Model-Based Systems Engineering (MBSE) principles, employing the Capella tool and the Arcadia methodology to develop a structured, integrated approach for the design and deployment of the data-driven fault detection system. A key contribution of this research is the development of a Clonal Selection Algorithm that optimizes the OSVM hyperparameters and the V-Detector algorithm parameters, resulting in a more effective fault detection solution. The integration of the AIS in the training process enables the generation of synthetic abnormal data, mitigating the need for engineers to gather large amounts of failure data, which can be impractical. The AISOSVM also incorporates incremental learning and decremental unlearning for the Online Support Vector Machine, allowing the system to adapt online using lightweight computational processes. This capability significantly improves the efficiency of fault detection systems, eliminating the need for offline retraining and redeployment. Reinforcement Learning (RL) is proposed as a promising future direction for the AISOSVM, as it can help autonomously adapt the system performance in near real-time, further mitigating the need for acquiring large amounts of system data for training, and improving the efficiency of the adaptation process by intelligently selecting the best samples to learn from. The AISOSVM framework was applied to real-world scenarios and platform models, demonstrating its effectiveness and adaptability in various use cases. The combination of the AIS and OSVM, along with the online learning and RL integration, provides a robust and adaptive solution for fault detection and health management in complex autonomous systems. This dissertation presents a significant contribution to the field of fault detection and health management by integrating the artificial immune system paradigm with Online Support Vector Machines, developing a structured, integrated approach for designing and deploying data-driven fault detection systems, and implementing reinforcement learning for online, autonomous adaptation of fault management systems. The AISOSVM framework offers a promising solution to address the challenges of fault detection in complex, autonomous systems, with potential applications in a wide range of industries beyond aerospace