Deploying HEP Applications on Multiple Grid Infrastructures

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.

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.

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.

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.

Design study of a low-power, low-noise front-end for multianode silicon drift detectors

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 rely on the networks as one of the critical parts of their infrastructure both within the participating laboratories and sites as well as globally to interconnect the sites, data centres and experiments instrumentation. Network virtualisation and programmable networks are two key enablers that facilitate agile, fast and more economical network infrastructures as well as service development, deployment and provisioning. Adoption of these technologies by HEP sites and experiments will allow them to design more scalable and robust networks while decreasing the overall cost and improving the effectiveness of the resource utilization. The primary challenge we currently face is ensuring that WLCG and its constituent collaborations will have the networking capabilities required to most effectively exploit LHC data for the lifetime of the LHC. In this paper we provide a high level summary of the HEPiX NFV Working Group report that explored some of the novel network capabilities that could potentially be deployment in time for HL-LHC.

In the early 1960s, the first semiconductor detector was used in the electronics industries. After the first implementation of planar technology in 1980 by J. Kemmer [1–3], semiconductors were quickly understood to give detectors of extraordinarily high performance in High Energy Physics (HEP) experiments. Semiconductor detectors have been used in modern HEP experiments in the form of pixel detectors, strip/microstrip detectors. They are popular due to their unmatched energy and spatial resolution and excellent response time. These detectors are manufactured mainly of silicon (Si), traditionally on high-resistivity single crystal float-zone material. Recent progress in micro technology now allows reliable large-scale production of detectors of sophisticated designs at acceptable cost.

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.

Quantum computing, ultra-low-noise sensing, and high-energy physics experiments often rely on superconducting circuits or semiconductor qubits and devices operating at deep cryogenic temperatures (4K and below). Photonic integrated circuits and interconnects have been demonstrated for scalable communications and optical domain transduction in these systems. Due to energy and area constraints, many of these devices need enhanced light-matter interaction, provided by photonic resonators. A key challenge, however, for using these resonators is the sensitivity of resonance wavelength to process variations and thermal fluctuations. While thermo-optical tuning methods are typically employed at room temperature to mitigate this problem, the thermo-optic effect is ineffective at 4K. To address this issue, we demonstrate a non-volatile approach to tune the resonance of photonic resonators using integrated phase-change materials (PCMs) at cryogenic temperatures. In this work, we report a 10 Gb/s free-carrier dispersion based resonant photonic modulator that can be tuned in a non-volatile fashion at sub-4K temperatures using a commercial silicon photonics process. This method paves the way for realizing scalable cryogenic integrated photonics with thousands of resonant devices for quantum and high-energy physics applications.

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 development of improved, cost-effective Nb3Sn superconductors would have an immediate benefit for high field magnets in High Energy Physics (HEP) applications. This project will develop and demonstrate an economical powder-in-tube (PIT) Nb3Sn process for use in magnets for future HEP accelerator research. In Phase I, a low-cost, intermetallic, phasepure Cu5Sn4 powder was developed, a PIT Nb3Sn process was designed, and prototype Nb3Sn conductors were fabricated. A cost-performance analysis was conducted, which suggested that the approach will meet DOE goals. In Phase II, powder manufacturing will be optimized, and advanced PIT designs will be fabricated on intermediate size billets. The data will be used to assemble and manufacture a scaled-up prototype conductor. Material will be produced and made available for building and testing prototype cables and test magnets at DOE national laboratories. In addition to HEP applications, an economical Nb3Sn superconductor would have application in high field magnets used in fusion machines and Nuclear Magnetic Resonance (NMR). For fusion machines, a successful demonstration would have enormous economic and social benefits.

Custom monolithic electronic circuits have been developed recently for large detector applications in high energy physics where subsystems require tens of thousands of channels of signal processing and data acquisition. In the design and construction of these enormous detectors, it has been found that monolithic circuits offer significant advantages over discrete implementations through increased performance, flexible packaging, lower power and reduced cost per channel. Much of the integrated circuit design for the high energy physics community is directly applicable to intermediate energy heavy-ion and electron physics. This STTR project conducted in collaboration with researchers at the Holifield Radioactive Ion Beam Facility (HRIBF) at Oak Ridge National Laboratory, sought to develop a new integrated circuit chip set for barium fluoride (BaF{sub 2}) detector arrays based upon existing CMOS monolithic circuit designs created for the high energy physics experiments. The work under the STTR Phase 1 demonstrated through the design, simulation, and testing of several prototype chips the feasibility of using custom CMOS integrated circuits for processing signals from BaF{sub 2} detectors. Function blocks including charge-sensitive amplifiers, comparators, one shots, time-to-amplitude converters, analog memory circuits and buffer amplifiers were implemented during Phase 1 effort. Experimental results from bench testing and laboratory testing with sources were documented.

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.