Distributed processing and analysis of physics data in the D0 SAM system at Fermilab

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.

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.

Solid-State-Drives are currently introduced in storage facilities for High Energy Physics (HEP) experiments and their performances are measured and compared to standard magnetic disks. For this paper a typical HEP data analysis is performed and used as a test to measure computing performances. The tests exploit the features provided by PROOF-Lite which allows to distribute a huge number of events among different CPU cores, thus reducing the overall time needed to complete the analysis task. These tests are carried on few computational devices typically hosted at a current Tier-2/Tier-3 facility. The performance results are provided in terms of figures of merit and the main issue is scalability described in terms of speed up factor and processing event rate. The obtained results can be used as guideline for both the typical HEP analyst and the Tier-2/Tier-3 manager: the former in the configuration of his own analysis task while dealing with increasing data sizes, the latter in the implementation of an interactive data analysis facility for HEP experiments while facing solutions that concern both technological and economical aspects.

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.

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.

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.

Quantum generative modeling is emerging as a powerful tool for advancing data analysis in high-energy physics, where complex multivariate distributions are common. However, efficiently learning and sampling these distributions remains challenging. We propose a quantum protocol for a bivariate probabilistic model based on shifted Chebyshev polynomials, trained as a circuit-based representation of two correlated variables, with sampling performed via quantum Chebyshev transforms. As a key application, we study fragmentation functions (FFs) of charged pions and kaons from single-inclusive hadron production in electron-positron annihilation. We learn the joint distribution of momentum fraction z and energy scale Q, and infer their correlations from the entanglement structure. Building on the generalization capabilities of the quantum model and extended register architecture, we perform fine-grid multivariate sampling for FF dataset augmentation. Our results highlight the growing potential of quantum generative modeling to advance data analysis and scientific discovery in high-energy physics. Quantum computing holds great promise for advancing data analysis in science, including high-energy physics. This work presents a quantum protocol for learning and sampling multivariate distributions, using Chebyshev polynomials and quantum Chebyshev transforms to study fragmentation functions and energy-momentum correlations in hadron production.

The subject of this special feature in Measurement Science and Technology concerns measurement methods, devices and subsystems, both hardware and software aspects, applied in large experiments of high energy physics (HEP) and superconducting RF accelerator technology (SRF). These experiments concern mainly the physics of elementary particles or the building of new machines and detectors. The papers present practical examples of applied solutions in large, contemporary, international research projects such as HERA, LHC, FLASH, XFEL, ILC and others.These machines are unique in their global scale and consist of extremely dedicated apparatus. The apparatus is characterized by very large dimensions, a considerable use of resources and a high level of overall technical complexity. They possess a large number of measurement channels (ranging from thousands to over 100 million), are characterized by fast of processing of measured data and high measurement accuracies, and work in quite adverse environments. The measurement channels cooperate with a large number of different sensors of momenta, energies, trajectories of elementary particles, electron, proton and photon beam profiles, accelerating fields in resonant cavities, and many others.The provision of high quality measurement systems requires the designers to use only the most up-to-date technical solutions, measurement technologies, components and devices. Research work in these demanding fields is a natural birthplace of new measurement methods, new data processing and acquisition algorithms, complex, networked measurement system diagnostics and monitoring. These developments are taking place in both hardware and software layers. The chief intention of this special feature is that the papers represent equally some of the most current metrology research problems in HEP and SRF. The accepted papers have been divided into four topical groups: superconducting cavities (4 papers), low level RF systems (8 papers), ionizing radiation (5 papers) and HEP experiments (8 papers).The editors would like to thank cordially all the authors who accepted our invitation to present their very recent results. A number of authors of the papers in this issue are active in the 6th European Framework Research Program CARE—Coordinated Accelerators Research in Europe and ELAN—the European Linear Accelerator Network. Some authors are active in research programs of a global extent such as the LHC, ILC and GDE—the Global Design Effort for the International Linear Collider.We also would like to thank personally, as well as on behalf of all the authors, the Editorial Board of Measurement Science and Technology for accepting this very exciting field of contemporary metrology. This field seems to be really a birthplace of a host of new metrological technologies, where the driving force is the incredibly high technical requirements that must soon be fulfilled if we dream of building new accelerators for elementary particles, new biological materials and medicine alike. Special thanks are due to Professor R S Jachowicz of Warsaw University of Technology for initiating this issue and for continuous support and advice during our work.

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

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.

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.

SOFIE is a fast Machine Learning inference engine developed at CERN, capable of translating trained deep learning models—provided in ONNX, Keras, or PyTorch formats—into C++ code for efficient inference. The generated code has minimal dependencies, making it easily integrable into the data processing and analysis workflows of high-energy physics (HEP) experiments. This study presents a comprehensive benchmark analysis of SOFIE against leading machine learning frameworks for model evaluation, including PyTorch, TensorFlow XLA, and ONNX Runtime. We focus on evaluating their performance in HEP applications, particularly for typical models such as Graph Neural Networks for jet tagging, and Variational Autoencoders and Generative Adversarial Networks for fast simulation. Our assessment considers key factors such as computational speed, memory usage, scalability, and ease of integration with existing HEP software ecosystems. Through this comparative study, we aim to provide insights that help the HEP community select the most suitable framework for their specific needs.

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.

The High Energy cosmic-Radiation Detection (HERD) facility was proposed as a space cosmic-ray and gamma-ray detector onboard China’s Space Station around 2027. The primary scientific objectives of HERD include searching for dark matter particles, studying the energy spectrum and composition of cosmic rays, and conducting high energy gamma-ray astronomy. The HERD offline software (HERDOS) is designed and developed based on SNiPER, a lightweight framework for high energy physics (HEP) experiments. HERDOS is designed to provide the common data processing functionalities and also responsible for building the offline data processing chain, which encompasses detector simulation, calibration, reconstruction, and data analysis. Several common HEP software toolkits, such as Geant4, ROOT, DD4hep, PODIO, and TBB, have also been integrated into HERDOS. This paper describes the design and implementation of the core software of the HERDOS, including the event data and detector data management system, parallelized simulation framework and database system. Currently, HERDOS operates at full capacity to support the conceptual design of detectors, conduct detector performance studies, and physics potential study.

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.