Opportunities for statistical signal processing in high energy physics

This paper addresses the problem of parameter estimation for tracking charged particles. The ad hoc techniques commonly used in high energy physics may not be sufficient to perform tracking in the complex background environment of future experiments, and algorithms based on modern signal processing may give interesting results. This paper describes a specific parameter estimation problem of a muon detector used in the ATLAS experiment, where the use of the estimation maximization (EM) algorithm results in improved performance, as an example of the potential benefit of using statistical signal processing techniques in high energy physics.

Amplitude adaptive locally optimum processing (ALOP) algorithms has been defined for suppression of large interference signals in direct sequence spread spectrum communication channels. Spatial beamforming and statistical signal processing techniques can be near-optimally integrated when indicators of the performance of statistical signal processing can be used to select matching beamforming directions. We demonstrate the use of the square amplitude variance (as a function of beamsteering angle) as an indicator from which optimal beamforming directions for ALOP can be derived.

Partial Discharge (PD) detection is one of the important methods used to evaluate the insulation condition of in-service high voltage (HV) electrical equipment. On-site PD detection methods are preferred compared to other methods as non-intrusive sensors are easier to install and have no interruptions on the operation. An electrical PD testing utilizing High Frequency Current Transformer (HFCT) installed at the ground terminal of the in-service instrument transformer was used to detect partial discharge activities. However, the limitation of this method is the measurement often lacks of accuracy due to interference in the measured PD waveforms. Therefore, such PD signals are hard to be distinguished from the stochastic noise pulses. Hence, the characteristics of PD signals and interference signals are studied in the Phase Resolved Partial Discharge (PRPD) pattern in order to identify the extent of PD occurrence in the signals. The discharge magnitude could increase significantly because of the noise signals superposed with PD signals. Therefore a method that can remove high magnitude noise will be proposed by using statistical signal processing technique.

<p>The Marsh Lane viaduct is a masonry railway bridge constructed during the 19th century nearby Leeds Central Railway Station. The bridge appears significantly damaged due to the increase of the operational train loads over the last decades and due to environmental effects. Due to this degradation, extensive repair was conducted in 2015. After this repair work, an extensive fibre optic sensor network was installed below three spans of the bridge to monitor surface strains at 68 locations on the underside of the arch spans. The paper compares data collected from two monitoring periods, 16 months apart. Combining statistical analysis and signal processing techniques, the results show that local damage, as well as change in the global dynamic behaviour of the structure over time, can be effectively detected with the use of Fibre Bragg Grating sensors.</p>

Laser-induced breakdown spectroscopy (LIBS) is an analytical technique real-time geochemical analysis that is being developed for portable use outside of the laboratory. In this study, statistical signal processing and classification techniques were applied to single-shot, broadband LIBS spectra, comprising measured plasma light intensities between 200 and 960 nm, for a suite of 157 garnets of different composition from 92 locations worldwide. Partial least squares discriminant analysis was applied to sets of 25 LIBS spectra for each garnet sample and used to classify the garnet samples based on composition and geographic origin. Careful consideration was given to the cross-validation procedure to ensure that the classification algorithm is robust to unseen data. The results indicate that broadband LIBS analysis can be used to discriminate garnets of different composition and has the potential to discern geographic origin.

In High Energy Physics (HEP) experiments, it is useful for physics analysis and outreach if the event display software can provide sophisticated visualization effects. Unity is professional software that provides 3D modeling and animation production. GDML format files are commonly used for detector descriptions in HEP experiments. In this work, we present a method for automating the import of GDML files to the Unity engine modeling through geometric transformation. With the conversion of the file format, the complicated geometric description of the HEP detector in a GDML file can be imported directly into the Unity engine using an FBX file. This format conversion was applied to BESIII experiments to achieve an automated geometry conversion to build geometry models in the Unity engine.

High energy physics (HEP) experiments are facing unprecedented challenges to deal with huge volumes of data. Grid is the excellent technology for it. However, Incompatibility of a variety of grid middewares is an obstacle to exploit the resources on multiple grid infrastructures. This paper firstly introduces HEP experiments and HEP grid, and then reviews the solutions for grid interoperability and interoperations. Because current HEP applications are tightly with gLite middleware, this paper highlights a gateway approach to interoperate with gLite and described it in detail. Direct access to gLite storage system is very important, and a dedicated tool for HEP data sharing is then proposed and implemented. Finally, the implementation of the interoperability of GOS and gLite is introduced.

With the development of the new generation of High Energy Physics (HEP) experiments, huge amounts of data are being generated. Efficient parallel algorithms/frameworks and High IO throughput are key to meet the scalability and performance requirements of HEP offline data analysis. Though Hadoop has gained a lot of attention from scientific community for its scalability and parallel computing framework for large data sets, it’s still difficult to make HEP data processing tasks run directly on Hadoop. In this paper we investigate the application of Hadoop to make HEP jobs run on it transparently. Particularly, we discuss a new mechanism to support HEP software to random access data in HDFS. Because HDFS is streaming data stored only supporting sequential write and append. It cannot satisfy HEP jobs to random access data. This new feature allows the Map/Reduce tasks to random read/write on the local file system on data nodes instead of using Hadoop data streaming interface. This makes HEP jobs run on Hadoop possible. We also develop diverse MapReduce model for HEP jobs such as Corsika simulation, ARGO detector simulation and Medea++ reconstruction. And we develop a toolkit for users to submit/query/remove jobs. In addition, we provide cluster monitoring and account system to benefit to the system availability. This work has been in production for HEP experiment to gain about 40,000 CPU hours per month since September, 2016.

The high-precision measurements of detectors in a High-Energy Physics (HEP) experiment need a continuous sampling of recorded events during collisions. Therefore, significant hardware changes are required to do online data processing due to a large amount of data generated by such detectors. Because of all these changes, a new Detector Control System (DCS) design is required. This paper presents a definition of the software requirements to be considered during the design, integration, and operation of a detector’s DCS into physics data-taking, for continuous and non-continuous measurement conditions in the experiments. For this, the main operating processes, elements, characteristics, and guidelines of the DCS in the most important HEP experiments around the world were analyzed. Additionally, characteristics, functional and non-functional requirements, and use-cases of the main actors involved in the different processes of this control system software are defined. A visual modeling and design tool based on Unified Modeling Language (UML) is used to obtain a description of these requirements.

In this paper, an image fusion technique based on statistical signal processing is studied. Without the multiscale transform (MST) for the original images, the fusion is directly made via using Expectation-Maximization (EM) algorithm. Unlike the standard EM fusion algorithm, we only use the neighboring pixels in the window to estimate the centered pixel. The improvement of the iterative procedure is helpful to obtain the final fusion result effectively. At last, some performance evaluations are suggested and applied to compare the performance of these fusion methods. Consequently, the obtained results and comparisons indicate that the method here has significant advantage. Keywords-image fusion; statistical signal processing; EM algorithm

Blind signal separation is applied to suppress backscatter for a 532 nm chaotic lidar underwater rangefinder. When operating in turbid waters, chaotic lidar returns contain information for both submerged objects and an optical backscatter “clutter” component. The statistical digital signal processing technique of blind signal separation (BSS) is applied to dynamically measure and cancel out the backscatter component before computing range to the desired object. Results from simulations and laboratory experiments are presented to demonstrate the combined chaotic/BSS approach. The chaotic/BSS approach extends operating range by almost 40% and achieves centimeter-order range accuracy. Receiver operating curves are simulated for the chaotic/BSS approach, which show convergence towards the ideal classifier in several conditions of interest.

Interest in many-core architectures applied to real time selections is growing in High Energy Physics (HEP) experiments. In this paper we describe performance measurements of many-core devices when applied to a typical HEP online task: the selection of events based on the trajectories of charged particles. We use as benchmark a scaled-up version of the algorithm used at CDF experiment at Tevatron for online track reconstruction - the SVT algorithm - as a realistic test-case for low-latency trigger systems using new computing architectures for LHC experiment. We examine the complexity/performance trade-off in porting existing serial algorithms to many-core devices. We measure performance of different architectures (Intel Xeon Phi and AMD GPUs, in addition to NVidia GPUs) and different software environments (OpenCL, in addition to NVidia CUDA). Measurements of both data processing and data transfer latency are shown, considering different I/O strategies to/from the many-core devices.

We attempted to study gamma-ray attenuation and sensing properties of conventional and modern inorganic scintillators being employed in the high energy physics (HEP) experiments. The mass attenuation coefficient (μm ), effective atomic number (Zeff ) and mean free path (mfp)were theoretically evaluated for the conventional scintillators materials such as CsI and NaI (Tl) and compared with advanced scintillator materials: PWO, PbF2, and BGO along with rare earth elements based scintillators such as LYSO:Ce, LuAG:Ce, BaF2:Y which have been proposed for applications in the future HEP experiments. Thegamma-ray attenuation parameters are analyzed within the framework of online software toolkit ‘py-MULBF’ over wide photon energy range from 0.015 MeV to 15 MeV.Variationof μm (and, Zeff ) with photon energy follows a trend similar for most of the inorganic scintillator materialsinvestigated here.CsI, however, maintained almost same effective atomic number value with respect to photon energy which signifies that CsI may be suitable for specific gamma-ray detection and sensing applications. Lead-based scintillator materials such as PbF2, PWO along with high-Z BGO are observed to exhibit better radiation attenuation capabilities.

The availability of low-power, radiation-resistant components has an enormous importance in the development of the electronic systems for modern detectors in a High Energy Physics (HEP) experiment. This paper describes the characterization in terms of radiation effects of two serializer blocks within a high speed transmitter, prior developed with the objective of achieving a power consumption of less than 30 mW at the operating speed of 4.8 Gbit/sec. Within the first serializer, called “simple TMR”, a traditional solution, based on the hardware redundancy, has been implemented. In the second case a new architecture, less power consuming, called “code protected”, has been proposed. The tests previously performed shown an average consumption of ∼30 mW and ∼19 mW, respectively, for a bit rate of 4.8 Gbit/sec but do not fully clarify if the blocks are suitable for working under extremely high radiation levels. Hence, a deep radiation hardness investigation has been performed and presented here to confirm the availability of these blocks in a HEP electronic system. SEU sensitivities are measured and bit error rates better than 2 E-15 are obtained, confirming that the “code protected” solution assures reliable communications in HEP experiments environment with a smaller power consumption. These blocks have also been designed and tested to cope with a total ionizing dose of 100 Mrad over 10 years of operation.

In this paper, an independent component analysis (ICA)-based disturbance separation scheme is proposed for statistical process monitoring. ICA is a novel statistical signal processing technique and has been widely applied in medical signal processing, audio signal processing, feature extraction and face recognition. However, there are still few applications of using ICA in process monitoring. In the proposed scheme, ICA is first applied to in-control training process data to determine the de-mixing matrix and the corresponding independent components (ICs). The IC representing the white noise information of the training data is then identified and the associated row vector of the IC in the de-mixing matrix is preserved. The preserved row vector is then used to generate the monitoring IC of the process data under monitoring. The disturbances in the monitoring process can be effectively enhanced in the monitoring IC. Finally, the traditional exponentially weighted moving average control chart is used to the monitoring IC for process control. For evaluating the effectiveness of the proposed scheme, simulated manufacturing process datasets with step-change disturbance are evaluated. Experiments reveal that the proposed monitoring scheme outperforms the traditional control charts in most instances and thus is effective for statistical process monitoring.