The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets was intended for real-life experiments and model training. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art OC-SVM reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties. It was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the best_length setup of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.Comment: Related to arXiv:1702.0083

First results from the LUCID-Timepix spacecraft payload onboard the TechDemoSat-1 satellite in Low Earth Orbit

The Langton Ultimate Cosmic ray Intensity Detector (LUCID) is a payload onboard the satellite TechDemoSat-1, used to study the radiation environment in Low Earth Orbit ($\sim$635km). LUCID operated from 2014 to 2017, collecting over 2.1 million frames of radiation data from its five Timepix detectors on board. LUCID is one of the first uses of the Timepix detector technology in open space, with the data providing useful insight into the performance of this technology in new environments. It provides high-sensitivity imaging measurements of the mixed radiation field, with a wide dynamic range in terms of spectral response, particle type and direction. The data has been analysed using computing resources provided by GridPP, with a new machine learning algorithm that uses the Tensorflow framework. This algorithm provides a new approach to processing Medipix data, using a training set of human labelled tracks, providing greater particle classification accuracy than other algorithms. For managing the LUCID data, we have developed an online platform called Timepix Analysis Platform at School (TAPAS). This provides a swift and simple way for users to analyse data that they collect using Timepix detectors from both LUCID and other experiments. We also present some possible future uses of the LUCID data and Medipix detectors in space.Comment: Accepted for publication in Advances in Space Researc

MACHINE LEARNING AND SOFTWARE SOLUTIONS FOR DATA QUALITY ASSESSMENT IN CERN’S ATLAS EXPERIMENT

The Large Hadron Collider (LHC) is home to multiple particle physics experiments designed to verify the standard model and push our understanding of the universe to its limits. The ATLAS detector is one of the large general-purpose experiments that make use of the LHC and generates a significant amount of data as part of its regular operations. Prior to physics analysis, this data is cleaned through a data assessment process which involves significant operator resources. With the evolution of the field of machine learning and anomaly detection, there is great opportunity to upgrade the ATLAS Data Quality Monitoring Framework to include automated, machine learning based solutions to reduce operator requirements and improve data quality for physics analysis. This thesis provides an infrastructure, theoretical foundation and a unique machine learning approach to automate this process. It accomplishes this by combining 2 heavily documented algorithms (Autoencoders and DBScan) and organizing the dataset around geometric descriptor features. The results of this work are released as code and software solutions for the benefit of current and future data quality assessment, research, and collaborations in the ATLAS experiment