Matchmaking, Datasets and Physics Analysis

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.

PanDA (Production and Distributed Analysis System) is a workload management system, widely used for data processing at experiments on Large Hadron Collider and others. COMPASS is a high-energy physics experiment at the Super Proton Synchrotron. Data processing for COMPASS runs locally at CERN, on lxbatch, the data itself stored in CASTOR. In 2014 an idea to start running COMPASS production through PanDA arose. Such transformation in experiment’s data processing will allow COMPASS community to use not only CERN resources, but also Grid resources worldwide. During the spring and summer of 2015 installation, validation and migration work is being performed at JINR. Details and results of this process are presented in this paper.

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.

Data processing in high energy physics experiments is a multi-tiered process in which raw detector signals are first processed locally into physics objects, and then collated into event records which can be scrutinized by a fast online trigger system. The resulting selection of events are reconstructed and pass through a number of software filters before arriving at a final offline analysis where hard physical constants are extracted. Although sophisticated statistical data analysis techniques are routinely employed high energy physics, the use of statistical signal processing in the field is has until now been rare. Our paper will begin with an overview of a typical high energy physics data acquisition system, outlining the technologies and tradeoffs involved at each stage. We will then proceed to argue that the dominant roles of model dependence and systematic errors in final physics analyses render statistical signal processing techniques largely inapplicable at this level. We observe, however, that at the low-level pattern recognition and event reconstruction levels, statistical signal processing techniques have been making inroads in high energy physics for a number of years, and examples from the literature will be cited. The viability of the technique for second level triggers will be assessed. Parallels to other other approaches, such as neural networks, will also be drawn. It will be argued that the falling cost of computing hardware favors the growth of statistical signal processing methods in high energy physics

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 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.

Specialized documentation envisioned from a pedagogical bases to train scientifically and technologically teachers and researchers, who initiate themselves in the analysis of high energy physics (HEP) experiments, is scarce. The lack of this material makes that young scientists' learning process be prolonged in time, raising costs in experimental research. In this paper we present the Monte Carlo technique applied to simulate the threshold energy for producing final-state particles of a specific two-body process (A + B → C + D), as pedagogical environment to face both computationally and conceptually an experimental analysis. The active/interactive learning-teaching formative process presented here is expected to be an educational resource for reducing young scientists' learning curve and saving time and costs in HEP scientific research.

Recently, the recovery of SiPM after radiation damage was tentatively promoted by thermal annealing with an external heat source. However, it is hard for practical applications for some specific experiments in high energy physics and space science since the vast well-installed detectors is impossible to be dismantled from a large experiment facility and implement the annealing process with a hot plate or an oven. In this paper, we present a technique of in-situ annealing and temperature monitoring for SiPMs in detail, which may provide a solution to recover the radiation damaged SiPMs and prolong the lifetime of the detectors. By applying a reverse current, the SiPM can be heated and annealed by a Joule effect; while the annealing temperature can be monitored by employing the PN junction of the same SiPM as a temperature sensor. This method may be extended to other semiconductor detectors based on PN junction, and prolong the lifetime of the detectors in a large experiment.

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.

The experiments of High Energy Physics (HEP) at the big accelerators with counter propagating beams serve for research on matter structure, properties and internal reactions. Contemporary HEP experiments are commonly done by elementary particle and nuclear physicists and engineers from many branches of technical sciences. The cutting edge level of technical complication of the experiments requires new research rather than off-the-shelf solutions. Such solutions are adopted as results of separate, large and expensive research programs, organized around a major experiment. One of the technical branches contributing essentially to the HEP experiments results is photonics and electronics. Recent application of fast, multi channel, distributed and synchronous photonic and electronic TRIDAQ systems, provides the HEP experiments with the improved space and time resolution at the measurements of positions, momenta and energies of elementary particles. TRIDAQ systems improve the selection processes of physical events (TRIgger part of the system), and the registration of relevant data (Data AcQuisition of the system). Part I of the paper discusses overall TRIDAQ system architecture, while part II of the paper shows TRIDAQ system solutions for BAC and CMS detectors, comparing the performance differences caused by two decades of system exploitation and design.

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 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 high-energy physics experiments, traditional data analysis methods increasingly show limitations when handling large-scale datasets, highlighting the urgent need for a transition to automated and intelligent analysis techniques to improve accuracy and efficiency. To address this challenge, we utilized an open dataset from CERN and proposed a novel Z-Bagging model, an ensemble learning approach designed to distinguish between two decay processes of the Z boson: Z [Formula: see text]+[Formula: see text] (Zmumu) and Z [Formula: see text] e+e[Formula: see text] (Zee). Initially, we tested several classical machine learning models to evaluate their performance in distinguishing these two decay modes. While these models showed some improvements in accuracy, further optimization was identified. Consequently, we introduced the Z-Bagging model, which combines multiple weak learners, such as Random Forest, Support Vector Machine and K-Nearest Neighbors, into a single strong learner. By integrating diverse models, Z-Bagging achieves more accurate and stable predictions. Experimental results demonstrate that the Z-Bagging model significantly outperforms existing models in classification accuracy for distinguishing Zmumu and Zee decay processes. This improvement provides a more precise tool for data analysis in high-energy physics.

Development of FPGA based phase alignment logic for the high speed protocol in HEP Experiments

In high energy physics experiments, pattern recognition in digital or analog data, is a current technical problem. Considerable effort is expended in these experiments in the development of real time and high speed decision making logic which is used as a rapid filter to enrich data samples with events of desired character. Optical processing techniques are a novel approach to this problem. It offers the advantage of parallel data treatment with a decision speed within 100 ns depending on the optoelectronic devices. A Prototype device was constructed in conjunction with a high energy physics experiment at Brookhaven National Laboratory in which the input data are transformed into an optical pattern through an array of LED's. The device is an incoherent correlator with a kinoform filter; The results are presented and discussed. Other applications are possible in which a computer generated hologram, like a kinoform, acts as an optical element which transforms points on a curve into parameters of that curve. This can solve a major problem in particle physics which is to recognize straight lines in parallel plate detectors. The recent progress and development of Ring Imaging Cerenkov Detectors need to detect a pattern on which the photons are disposed on a circle. It does appear that a great deal in the high energy physics will involve the production of jets. By driving light sources whose intensity is proportional to particle energy and by using a filter which contains geometrical informations, it is possible to calculate variables relating directly to jets, in real time.