High Energy Physics Research Research Articles

Dimensional Analysis in General Theory of Relativity

Use of natural unit is utmost important for research in high energy physics, particularly in the fields of ‘Elementary Particle Physics’, ‘Astro-particle Physics’ and `Physical Cosmology', because all the parameters turn out to be dimensionless. However, it is suggestive to work in a system of units, in which all the kinematical parameters may be expressed in terms of different powers of the Planck's mass or equivalently the energy. Here we explicitly exhibit the use of the natural units, some associated problems and also the proposed system of units in connection with their application in ‘General Theory of Relativity

Rare-earth nanocrystalline scintillators for biomedical application: A review

Rare-earth scintillating materials have been widely utilized in nuclear image technique, security screening and high energy physics research. Researchers have devoted extensive efforts to size design, surface modification, and drug assembly to transform them into prospective nanomaterials for diagnostics and cancer treatment. This review endeavors to summarize the advanced preparation technology and design concept of rare-earth nanocrystalline scintillators (RENSs), and offers fundamental guidance for the application of various materials in biomedical research. Specifically, it focuses on RENSs with excellent biocompatibility, outstanding stability, uniform size distribution, and strong drug loading capacity to meet subsequent practical applications. Additionally, this review reports on the application of RENSs in the biomedical field. Specifically, bioimaging and antitumor therapy are the most prevalently utilized and promising areas. X-ray excited optical imaging (XEOL) is combined with advanced imaging techniques (CT, MRI, etc.) to obtain comprehensive information about deep tissue. The combination of X-ray radiation therapy and photodynamic therapy technology can realize safe anti-tumor therapy under the condition of low-dose irradiation. Furthermore, this review also enumerates several recent highlight research strategies and the prospects of RENSs in the biomedical application in the future.

A comprehensive research on γ-ray and particle radiation interaction parameters of Cerium doped heavy oxide inorganic scintillators

In this study, the interaction cross sections of heavy oxide scintillators, which have a very important place in high energy physics research, with photon and particle radiation were comparatively investigated. In this context, the interaction parameters of six different Ce-doped heavy oxide scintillators were obtained using updated software. The interactions of photons with energies between 1 keV and 100 GeV with scintillators were evaluated in the light of the results produced by the Phy-X/PSD software. In addition to the continuous energy range, calculations were also made on the photon energies emitted with the highest probability of emission from sources 241Am (59.54 keV), 133Ba (356 keV), 22Na (511 and 1280 keV), 137Cs (662 keV) and 152Eu (1460 keV). Additionally, evaluations of energy absorption build-up factors valid under broad beam conditions are also included in this study. Fast neutron absorption cross sections and changes in the range (R) values for heavy particle radiation in the range of 1 keV–20 MeV were also determined with SRIM and ESTAR software. All data obtained depending on energy are explained according to the mass percentages of Lu, Gd, Hf, Y, Al and O elements in the scintillators. The highest interaction cross section for photons was obtained in the Lu2Gd2SiO5 (Ce 0.3%) scintillator (HOS2). The interaction cross section of this scintillator was obtained as 3.115 and 0.042 cm2/g at 0.1 MeV and 10 MeV photon energies, respectively. On the other hand, the highest cross-sections of the fast neutrons were observed in the scintillator (HOS5) containing GdAlO3 (Ce 1.0%) as 0.148 cm−1. This difference in neutron radiation has been explained by calculating the individual attenuation abilities of the elements in the structures. It is hoped that the data obtained will guide researchers working on the detection of different types of radiation.

Studying high-energy nuclear physics with machine learning

The research paradigm in physics has evolved through three distinct phases: empirical observation and induction, theoretical modeling and deduction and computational numerical analysis and simulation. We are now situated within a novel epoch wherein the scientific research paradigm is increasingly shaped by the preeminence of large-scale data and artificial intelligence, particularly within the realm of AI for science applications. The advent of high-energy colliders coupled with Monte Carlo simulations has given rise to an unprecedented accumulation of data. Nested within this transformative research paradigm, machine learning and artificial intelligence technologies have been extensively harnessed for the analysis of these vast data sets. Within the domain of high-energy nuclear physics, two prevalent machine learning techniques have emerged: Bayesian analysis and deep learning. The former employs comprehensive fitting methodologies that compare extensive data sets against theoretical models, enabling the extraction of critical information pertaining to the initial nuclear structure, parton distributions, the equation of state governing hot and dense nuclear matter, and the transport coefficients of the quark–gluon plasma, among other parameters. Conversely, the latter capitalizes on the unparalleled pattern recognition capabilities of deep learning to discern robust features from high-dimensional raw data, specifically targeting individual physical parameters. This paper elucidates the fundamental principles of machine learning and delineates its potential to augment high-energy nuclear physics research endeavors.

Zc(3900) observation at BESIII with QSVM method

In recent years, quantum computing shows significant potentials in many areas. In this proceeding, we revisit the observation of the Zc(3900) resonance with quantum machine learning techniques, specifically quantum support vector machine (QSVM). Meanwhile, the outcomes are compared with classical support vector machine (SVM) method. With the IBM Qiskit toolkit, the QSVM method achieves a competitive signal and background classification accuracy compared to classical methods. This study emphasizes the potential of quantum machine learning in high-energy physics research, and it reveals the feasibility of applying quantum computing in future physics data analysis.

Streaming Readout and Data-Stream Processing With ERSAP

With the exponential growth in the volume and complexity of data generated at high-energy physics and nuclear physics research facilities, there is an imperative demand for innovative strategies to process this data in real or near-real-time. Given the surge in the requirement for high-performance computing, it becomes pivotal to reassess the adaptability of current data processing architectures in integrating new technologies and managing streaming data. This paper introduces the ERSAP framework, a modern solution that synergizes flow-based programming with the reactive actor model, paving the way for distributed, reactive, and high performance in data stream processing applications. Additionally, we unveil a novel algorithm focused on time-based clustering and event identification in data streams. The efficacy of this approach is further exemplified through the data-stream processing outcomes obtained from the recent beam tests of the EIC prototype calorimeter at DESY.

Internal structural changes in crystals induced by GeV heavy ion beam irradiation of LiF

<sec>When an incident high-energy heavy ion beam enters into solid material, the energy deposition density along the ion flight path can change the temperature and pressure of macroscopic target, and new material defects can be created under the high-pressure and high-density conditions. To accurately control the extreme state in material generated by heavy ion beam, it is necessary to conduct in-depth research on the energy deposition density of ions and ascertain the new potential defects in matter. Reported in this work is the new experiment conducted on the HIRFL-CSR at Lanzhou, with the extracted 264 MeV/u Xe<sup>36+</sup> ion beams irradiating an LiF crystal target. The emission spectrum of the LiF is measured <i>in-situ</i>. Moreover, the crystal color is observed to vary along the ion path, and X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) are used to observe the potential new phases at different positions of crystal through the target dissociation method.</sec><sec>It is apparent that in No. 3-front a new phase around 52.6° is found in XRD result, which is believed to be LiF<sub>3</sub> (LiF+F<sub>2</sub>) structural phase and appears in the Bragg peak region of Xe ions in LiF. Furthermore, to verify this result, a similar experiment is done by using a 430 MeV/u <sup>84</sup>Kr<sup>26+</sup> ion beam, and the stacked layered LiF target is analyzed after the irradiation. The XPS result shows more complex defects aggregating in the Bragg peak region of Kr ions in LiF at room temperature. In previous study, such complex defects were all created under high temperature conditions. We find that these complex defects can be produced around the Bragg peak region of ions in LiF at room temperature, resulting in a temporally high temperature and high pressure condition. This paper can provide some experimental evidences and references for the target material modification in heavy ion beam driven high-energy density physics research.</sec>

Track Lab: extensible data acquisition software for fast pixel detectors, online analysis and automation

Fast, incremental evolution of physics instrumentation raises the question of efficient software abstraction and transferability of algorithms across similar technologies. This contribution aims to provide an answer by introducing Track Lab, a modern data acquisition program focusing on extensibility and high performance. Shipping with documented API and more than 20 standard modules, Track Lab allows complex analysis pipelines to be constructed from simple, reusable building blocks. Thanks to multi-threaded infrastructure, data can be clustered, filtered, aggregated and plotted concurrently in real-time. In addition, full hardware support for Timepix2, Timepix3 pixel detectors and embedded photomultiplier systems enables such analysis to be carried out online during data acquisition. Repetitive procedures can be automated with support for motorized stages and X-ray tubes. Freely distributed on 7 popular operating systems and 2 CPU architectures, Track Lab is a versatile tool for high energy physics research.

BABAR’s Experience with the Preservation of Data and Analysis Capabilities

The BABAR experiment collected electron-positron collisions at the SLAC National Accelerator Laboratory (SLAC) from 1999-2008. Although data taking has stopped 15 years ago, the collaboration is still actively doing data analyses, publishing results, and giving presentations at international conferences. Special considerations were needed to do analyses using a computing environment that was developed decades ago. A framework is required that preserves the data, data access, and the capability of doing analyses using a well defined and preserved environment. Also, BABAR’s support by SLAC ended at the beginning of 2021. Fortunately, the High Energy Physics Research Computing group at the University of Victoria (UVic), Canada, offered to provide the new home for the main BABAR computing infrastructure, the Grid Computing Centre Karlsruhe offered to host all data for access by analyses running at UVic, and CERN and the IN2P3 Computing Centre offered to store a backup of all data. This paper presents what was done at BABAR to preserve the data and analysis capabilities and what was needed to move the whole computing infrastructure, including collaboration tools and data files, away from SLAC. It will be shown how BABAR preserved the ability to continue to do data analyses and also have a working collaboration tools infrastructure. This paper will describe on BABAR’s experience with such a big change in its infrastructure and what was learned from it, which may be useful to other experiments which are interested in long term analysis support and data preservation in general.

Potential of the Julia Programming Language for High Energy Physics Computing

Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus on the application when developing the code, better research productivity pleads for a high-level programming language. A popular approach consists of combining Python, used for the high-level interface, and C++, used for the computing intensive part of the code. A more convenient and efficient approach would be to use a language that provides both high-level programming and high-performance. The Julia programming language, developed at MIT especially to allow the use of a single language in research activities, has followed this path. In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language. The HEP-specific foundation libraries that would need to be consolidated are identified.

Insights into radiation resistance of titanium alloys from displacement cascade simulations

Radiation damage in beam window materials limits the use of high power proton beams in high energy physics research. The alloy Ti-6Al-4V is presently used as a beam window material but a prospective alternative, Ti-15V-3Cr-3Sn-3Al has been proposed. Since both these alloys contain dual phases at room temperature, we compare the radiation damage in the α and β-phases of these two materials via primary knock-on atom (PKA) cascade simulations in the 10–40 keV energy range. At PKA energies 30 and 40 keV, the number of Frenkel pairs in the ballistic stage is higher in the β-phase of Ti-6Al-4V than that in the β-phase of Ti-15V-3Cr-3Sn-3Al almost by a factor of 2. The α-phase, of both these alloys, by far outperforms the β-phases of the two alloys, both in terms of damage during the ballistic stage and in terms of the surviving defects. The average displacement threshold energy (Ed) in the α-phase of both alloys was found to be 66 eV while that in the β-phase of Ti-15-3-3 was 55 eV and in the β-phase of Ti-6-4 was 46. While the number of surviving defects is almost equal in both alloys, the vacancy and interstitial clustering mechanisms differ notably, which can impact the degree of radiation hardening and loss of ductility. Our simulations show larger vacancy and interstitial clusters form in Ti-6Al-4V as compared to that in Ti-15-3-3-3 alloy. These results indicate that Ti-15-3-3-3 alloys may be a promising candidate for next generation beam window material with a higher radiation tolerance than the existing Ti-6-4 alloy.

Luminescence and light yield of Ce3+-doped (60−x)SiO2-xBaF2-20Al2O3-20Gd2O3 scintillation glasses: The effect of BaF2 admixture

Ce3+-doped dense glasses with high light yield (LY) are promising scintillators for use in high-energy particle physics research and radiation sensors for high-energy X- and γ- ray detection. In this work, Ce3+-doped (60-x)SiO2-xBaF2-20Al2O3-20Gd2O3 oxyfluoride glasses have been prepared under a CO reducing atmosphere. Substitution of SiO2 by BaF2 has been made to increase the density, and the effects on luminescence and scintillation properties have been investigated. The amorphous nature of the prepared glass is confirmed by the XRD profile showing an absence of any diffraction peaks. The broad photoluminescence (PL) emission band of the Ce3+ 5d–4f transition in the 330–580 nm range is observed peaking around 385 nm and the PL decay is governed in the range of 46–53 ns decay time. The 10 mol% BaF2 glass with density of 4.54 g/cm3 shows the highest LY value of 1590 photons/MeV and energy resolution of 16.5% in the pulse height spectrum of 662 keV γ rays, and the LY of 141 photons/MeV and energy resolution of 21.7% in the pulse height spectrum of 5.5 MeV α particles.

Conceptual Design of a 20 T Hybrid Cos-Theta Dipole Superconducting Magnet for Future High-Energy Particle Accelerators

High energy physics research will need more and more powerful circular accelerators in the next decades. It is therefore desirable to have dipole magnets able to produce the largest possible magnetic field, in order to keep the machine diameter within a reasonable size. A 20 T dipole is considered a desired achievement since it would allow the construction of an 80 km machine, able to circulate 100 TeV proton beams. In order to reach 20 T, a hybrid Low-Temperature Superconductor (LTS) - High-Temperature Superconductor (HTS) magnet is needed, since LTS technology is presently limited to ∼16 T for accelerator magnet applications. In this paper, we present the design of a 6 layers 20 T hybrid dipole magnet using Nb <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Sn (LTS) and Bi2212 (HTS). We show that it is possible to achieve this magnetic field with accelerator field quality, with sufficient margin on a realistic conductor, keeping the stresses within safe limit, avoiding conductor degradation.

Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access

Computing plays a significant role in all areas of high energy physics. The Snowmass 2021 CompF4 topical group's scope is facilities R&D, where we consider "facilities" as the computing hardware and software infrastructure inside the data centers plus the networking between data centers, irrespective of who owns them, and what policies are applied for using them. In other words, it includes commercial clouds, federally funded High Performance Computing (HPC) systems for all of science, and systems funded explicitly for a given experimental or theoretical program. This topical group report summarizes the findings and recommendations for the storage, processing, networking and associated software service infrastructures for future high energy physics research, based on the discussions organized through the Snowmass 2021 community study.

Machine learning and LHC event generation

First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

Leveraging State-of-the-Art Engines for Large-Scale Data Analysis in High Energy Physics

The Large Hadron Collider (LHC) at CERN has generated a vast amount of information from physics events, reaching peaks of TB of data per day which are then sent to large storage facilities. Traditionally, data processing workflows in the High Energy Physics (HEP) field have leveraged grid computing resources. In this context, users have been responsible for manually parallelising the analysis, sending tasks to computing nodes and aggregating the partial results. Analysis environments in this field have had a common building block in the ROOT software framework. This is the de facto standard tool for storing, processing and visualising HEP data. ROOT offers a modern analysis tool called RDataFrame, which can parallelise computations from a single machine to a distributed cluster while hiding most of the scheduling and result aggregation complexity from users. This is currently done by leveraging Apache Spark as the distributed execution engine, but other alternatives are being explored by HEP research groups. Notably, Dask has rapidly gained popularity thanks to its ability to interface with batch queuing systems, widespread in HEP grid computing facilities. Furthermore, future upgrades of the LHC are expected to bring a dramatic increase in data volumes. This paper presents a novel implementation of the Dask backend for the distributed RDataFrame tool in order to address the aforementioned future trends. The scalability of the tool with both the new backend and the already available Spark backend is demonstrated for the first time on more than two thousand cores, testing a real HEP analysis.

Deployment of High Energy Physics software with a standard method

The installation and maintenance of scientific software for research in experimental, phenomenological, and theoretical High Energy Physics (HEP) requires a considerable amount of time and expertise. While many tools are available to make the task of installation and maintenance much easier, many of these tools require maintenance on their own, have little documentation and very few are used outside of HEP community.For the installation and maintenance of the software, we rely on the well tested, extensively documented, and reliable stack of software management tools with the RPM Package Manager (RPM) at its core. The precompiled HEP software packages can be deployed easily and without detailed Linux system knowledge and are kept up-to-date through the regular system update process. The precompiled packages were tested on multiple installations of openSUSE, RHEL clones, and Fedora. As the RPM infrastructure is adopted by many Linux distributions, the approach can be used on more systems.In this contribution, we discuss our approach to software deployment in detail, present the software repositories for multiple RPM-based Linux distributions to a wider public and call for a collaboration for all the interested parties.

Enabling Large Scale Simulations for Particle Accelerators

International high-energy particle physics research centers, like CERN and Fermilab, require excessive studies and simulations to plan for the upcoming upgrades of the world's largest particle accelerators, and the design of future machines given the technological challenges and tight budgetary constraints. The Beam Longitudinal Dynamics (BLonD) simulator suite incorporates the most detailed and complex physics phenomena in the field of longitudinal beam dynamics, required for providing extremely accurate predictions. Modern challenges in beam dynamics dictate for longer, larger and numerous simulation studies to draw meaningful conclusions that will drive the baseline choices for the daily operation of current machines and the design choices of future projects. These studies are extremely time consuming, and would be impractical to perform without a High-Performance Computing oriented simulator framework. In this article, at first, we design and evaluate a highly-optimized distributed version of BLonD. We combine approximate computing techniques, and leverage a dynamic load-balancing scheme to relax synchronization and improve scalability. In addition, we employ GPUs to accelerate the distributed implementation. We evaluate the highly optimized distributed beam longitudinal dynamics simulator in a supercomputing system and demonstrate speedups of more than two orders of magnitude when run on 32 GPU platforms, w.r.t. the previous state-of-art. By driving a wide range of new studies, the proposed high performance beam longitudinal dynamics simulator forms an invaluable tool for accelerator physicists.

The application of Low-Temperature Cofired Ceramics technology in Gas Electron Multiplier microstructures

The main goal of the work is to develop a novel technique for the production of improved Gas Electron Multiplier (GEM) microstructures that are critical elements of particle detectors used for High Energy Physics research. Invented at CERN, the Micro-Chemical-Vias (MCV) technology has proven optimal for producing high volumes of large size GEMs elements. We aim to develop a technology for the production of GEMs with higher vias densities, increasing the gain of a single structure and ensuring better system reliability. Additionally, different conductive layers are planned for use. Tests were carried out using Low-Temperature Cofired Ceramics (LTCC) and various types of conductive layers.

Parameterization of electron attachment rate constants for impurities in LArTPC detectors

The ability of free electrons to drift long distances at high velocities in pure liquid argon under an applied electric field has been exploited for the past forty years to implement detectors with increasingly larger volumes for high energy physics research. The attachment of free electrons to impurities in the LAr is an important limit on the free instrumented volume of these extremely large detectors, and impurity concentrations as small as 100 ppt can reduce their resolution and efficiency. In this paper, we summarize the electron attachment rate constants as a function of the applied electric field, for common impurities in LArTPCs, obtained from data in the literature. We further provide analytical functions to parameterize the data, which are useful to compare with new measurements, to model and analyze the performance of existing detectors, and to predict the performance of new detectors.