High Energy Physics Applications Research Articles

Pressure resistance characterisation of vascular networks embedded in carbon composites for high energy physics applications

In state-of-the-art tracking detectors, lightweight carbon composite structures support the pixel or micro-strip sensors and provide the main thermal path between the silicon and a network of metallic or plastic pipes containing a cooling fluid. Despite the good results obtained with this design approach, the challenges associated with future high energy physics (HEP) experiments demand even lighter and more efficient technologies. In this regard, replacing the existing piping with a network of channels directly embedded in the composite laminates represents a promising solution to improve the thermal coupling, which also offers additional gains in terms of mass and thermo-elastic stability.While some research has been devoted to assessing the mechanical and thermal performance of such laminates, limited information about their resistance to internal pressure is currently available in the literature. This lack of data constitutes an important obstacle for the use of vascular networks in future HEP applications, which the present paper aims to address.Experimental methods were used to investigate the pressure resistance of channels embedded in carbon composite laminates. Modified poly (lactic) acid (PLA) preforms were embedded in carbon-fibre epoxy laminates. A post-cure vaporization technique removed the PLA, thus producing plates with longitudinal channels. Destructive tests were conducted to determine the burst pressure of the plates depending on the lay-up and the cross-section geometry of the channels. Both circular and oblong channels were evaluated, and various reinforcement techniques were explored to enhance the pressure resistance of the laminates. Micro-graphic examinations and X-ray micro-computed tomography were employed to gain a better understanding of the microstructure and the failure mechanisms of the plates.Plates with circular channels measuring 1.75 mm in diameter, embedded in [0/90/0]S laminates and reinforced with 2 mm lay length fuzzy carbon fibre over-braids, achieved burst pressures exceeding 45 MPa. This result, which is approximately an order of magnitude greater than that obtained for the equivalent non-reinforced laminates, demonstrates the enormous potential of this technology for future particle detectors.

Machine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade

The Global Event Processor (GEP) FPGA is an area-constrained, performance-critical element of the Large Hadron Collider's (LHC) ATLAS experiment. It needs to very quickly determine which small fraction of detected events should be retained for further processing, and which other events will be discarded. This system involves a large number of individual processing tasks, brought together within the overall Algorithm Processing Platform (APP), to make filtering decisions at an overall latency of no more than 8ms. Currently, such filtering tasks are hand-coded implementations of standard deterministic signal processing tasks.In this paper we present methods to automatically create machine learning based algorithms for use within the APP framework, and demonstrate several successful such deployments. We leverage existing machine learning to FPGA flows such as hls4ml and fwX to significantly reduce the complexity of algorithm design. These have resulted in implementations of various machine learning algorithms with latencies of 1.2 μs and less than 5% resource utilization on an Xilinx XCVU9P FPGA. Finally, we implement these algorithms into the GEP system and present their actual performance.Our work shows the potential of using machine learning in the GEP for high-energy physics applications. This can significantly improve the performance of the trigger system and enable the ATLAS experiment to collect more data and make more discoveries. The architecture and approach presented in this paper can also be applied to other applications that require real-time processing of large volumes of data.

Theories of Relativistic Dissipative Fluid Dynamics.

Relativistic dissipative fluid dynamics finds widespread applications in high-energy nuclear physics and astrophysics. However, formulating a causal and stable theory of relativistic dissipative fluid dynamics is far from trivial; efforts to accomplish this reach back more than 50 years. In this review, we give an overview of the field and attempt a comparative assessment of (at least most of) the theories for relativistic dissipative fluid dynamics proposed until today and used in applications.

On the spin-quantization-axis selection for the spin polarization modeling during laser–electron collision

Spin polarized electron beams have wide applications in high energy physics. The generation of such electron beams has recently attracted considerable attention where the collision of intense laser pulses with high energy electron beams is proposed as one of the potential methods. In previous numerical studies, different spin-quantization axes (SQA) are used, which lead to different calculation results in some configurations of laser interaction with an electron beam. To be consistent with the well-known Thomas–Bargmann–Michel–Telegdi (TBMT) equation, we have proposed a new SQA which is along the TBMT precession direction. We clearly show the difference among the three kinds of SQA selections by comparing the temporal evolution of the electron beam's polarization during the perpendicular collision of an electron beam with s-polarized two-color laser pulses, where the magnetic fields have a parallel component along the electron motion direction. Calculation shows that such differences could be experimentally measured by nowadays facilities. To get the maximum electron polarization degree in the two-color laser pulses based schemes, we also find the optimized condition for the two-color laser parameters. Our studies give a better understanding of spin-polarization calculation and will be beneficial for future experimental designs for electron polarization manipulation.

Studies of C2H2F4 and SF6 alternative gas mixtures for HPL RPC detectors for High Energy Physics applications

This research investigates the viability of replacing greenhouse gases C2H2F4 and SF6 with eco-friendly gas mixtures in Resistive Plate Chamber (RPC) detectors used at CERN LHC experiments. The study focuses on the addition of CO2 as a substitute of C2H2F4 to minimize greenhouse gas emissions and reduce operational costs. Initial performance studies with cosmic muons evaluate key parameters such as efficiency, current, streamer probability, prompt charge, cluster size, and time resolution of the detectors. Subsequent assessments take place at the CERN Gamma Irradiation Facility, emulating the LHC experiment background radiation. Furthermore, the study explores alternatives to SF6, including 3M NOVECTM 4710 and AMOLEATM 1224yd, presenting perforances results about these gas alternatives in High Pressure Laminate RPCs.

Characterization of Ultra-Fast Silicon Detectors (UFSD) for high energy physics applications

Ultra-fast silicon detectors (UFSD) are a specialized type of radiation detectors based on Low Gain Avalanche Detectors (LGADs) that are designed to have extremely fast response times. This work presents detailed results on the study of 50μm thin-LGADs with different doping concentrations produced by Micron Semiconductors Ltd. A temperature dependent study of electrical characteristics and infrared laser measurements are presented in this work.

Characterization of a 28 nm CMOS Technology for Analog Applications in High Energy Physics

Characterization of a 28 nm CMOS Technology for Analog Applications in High Energy Physics

Design and development of Low Gain Avalanche Detectors using Teledyne e2v process

The Low Gain Avalanche Detectors (LGAD) has become the preferred technological choice for fast track timing detectors in High Energy Physics applications. Additional uses of such sensors are currently being investigated, ranging from Light Detection and Ranging (LIDAR) to Low Let particle dosimetry in medical and space applications. This paper describes the development and test of LGAD devices, fabricated using a commercial process from Teledyne e2v silicon foundry. Motivation behind the project, and details of the TCAD simulations of the manufacturing process and of the electrical performances of the devices will be provided. Test results on gain and time resolution using laser injection will also be shown.

Fast, high-quality pseudo random number generators for heterogeneous computing

Random number generation is key to many applications in a wide variety of disciplines. Depending on the application, the quality of the random numbers from a particular generator can directly impact both computational performance and critically the outcome of the calculation. High-energy physics applications use Monte Carlo simulations and machine learning widely, which both require high-quality random numbers. In recent years, to meet increasing performance requirements, many high-energy physics workloads leverage GPU acceleration. While on a CPU, there exist a wide variety of generators with different performance and quality characteristics, the same cannot be stated for GPU and FPGA accelerators. On GPUs, the most common implementation is provided by cuRAND - an NVIDIA library that is not open source or peer reviewed by the scientific community. The highest-quality generator implemented in cuRAND is a version of the Mersenne Twister. Given the availability of better and faster random number generators, high-energy physics moved away from Mersenne Twister several years ago and nowadays MIXMAX is the standard generator in Geant4 via CLHEP. The MIXMAX original design supports parallel streams with a seeding algorithm that makes it especially suited for GPU and FPGA where extreme parallelism is a key factor. In this study we implement the MIXMAX generator on both architectures and analyze its suitability and applicability for accelerator implementations. We evaluated the results against “Mersenne Twister for a Graphic Processor” (MTGP32) on GPUs which resulted in 5, 13 and 14 times higher throughput when a 240, 17 and 8 sized vector space was used respectively. The MIXMAX generator coded in VHDL and implemented on Xilinx Ultrascale+ FPGAs, requires 50% fewer total Look Up Tables (LUTs) compared to a 32-bit Mersenne Twister (MT-19337), or 75% fewer LUTs per output bit. In summary, the state-of-the art MIXMAX pseudo random number generator has been implemented on GPU and FPGA platforms and the performance benchmarked.

GaN based DC-DC converters for high energy physics applications

This paper introduces a prototype of a GaN-FET based 200 W DC-DC converter. Its design has been carried out to evaluate power density (or power dissipation and volume) and ensure minimal electromagnetic interference (EMI) issues that are commonly associated with the high switching frequency converters. To achieve this ANSYS HFSS-SiWave models have been developed to assess noise emissions based on the parasitic elements of PCB layout. Prototypes performance have been evaluated through extensive testing. This work offers insight into the potential of this technology in future physics detectors.

A dimensions aware evaluator for High Energy Physics applications

A dimensions aware evaluator for High Energy Physics applications

High Energy Physics Applications on Fiber Optic Sensors in Relative Humidity

Our multidisciplinary research group has been conducting studies in recent years on the use of fiber grating-based sensors for relative humidity measurement in the Compact Muon Solenoid (CMS) instrument at CERN in Geneva. In order to monitor low relative humidity (RH) values even in cold temperatures, our multidisciplinary research group has been working to develop near-field fibre optic sensors based on tin dioxide particle layers, in light of the extensive research conducted in the past few years to evaluate the radiation hardness capability of fibre optic technology in high-energy physics settings. Compared to other sensor types, optical fiber sensors provide several advantages. These benefits mostly stem from the characteristics of optical fibers, which include their tiny size, inexpensive, electromagnetic passivity, resistance to high pressure, and resistance to high temperatures. Sensing is the process of measuring variables like temperatures, stress, or angular velocity by examining the characteristics of light. To evaluate the sensors' performance under the circumstances needed for CERN experiments, untried tests in the [0-65] %RH range at various temperatures were conducted. There were other campaigns of progressive irradiation using γ-ionizing radiations. The results obtained show that the suggested technologies have great potential for usage in High Energy Physics (HEP) activities as a reliable and acceptable replacement for commercial hygrometers in the future.

Comparison of Bayesian and particle swarm algorithms for hyperparameter optimisation in machine learning applications in high energy physics

When using machine learning (ML) techniques, users typically need to choose a plethora of algorithm-specific parameters, referred to as hyperparameters. In this paper, we compare the performance of two algorithms, particle swarm optimisation (PSO) and Bayesian optimisation (BO), for the autonomous determination of these hyperparameters in applications to different ML tasks typical for the field of high energy physics (HEP). Our evaluation of the performance includes a comparison of the capability of the PSO and BO algorithms to make efficient use of the highly parallel computing resources that are characteristic of contemporary HEP experiments.

System Development of Radiation-Tolerant Silicon Photonics Transceivers for High Energy Physics Applications

System Development of Radiation-Tolerant Silicon Photonics Transceivers for High Energy Physics Applications

Development of an air cooling system with low material budget for high-energy physics applications

This work presents the cooling system of a future particle detector at the Large Hadron Collider (LHC): the Inner Tracking System 3 (ITS3) of the ALICE experiment. Efficient cooling is crucial for maximizing the particle measurement quality in the LHC. The system introduces open-cell carbon foams as heat exchangers that combine heat conduction and convection for the first time in a high-level engineering application. The system is designed to have an unprecedentedly low material budget, which is a requirement for particle detectors aiming at precise tracking at low particle momenta. This novel approach leads to a 80% reduction of the material budget compared to the previous version (ITS2), resulting in enhanced measurement accuracy. The study employs an experimental wind tunnel setup and numerical simulation, showing excellent agreement with mean deviations under 0.5 K that are attributed to sensor installation uncertainties. The results confirm the system adherence to the temperature requirements. Compared to the baseline design, it is shown that enhancements based on numerical simulations produce potential reductions of up to 50% in the maximum temperature variation of the detector layers.

Ultrafast and Radiation-Hard Lead Halide Perovskite Nanocomposite Scintillators.

The use of scintillators for the detection of ionizing radiation is a critical aspect in many fields, including medicine, nuclear monitoring, and homeland security. Recently, lead halide perovskite nanocrystals (LHP-NCs) have emerged as promising scintillator materials. However, the difficulty of affordably upscaling synthesis to the multigram level and embedding NCs in optical-grade nanocomposites without compromising their optical properties still limits their widespread use. In addition, fundamental aspects of the scintillation mechanisms are not fully understood, leaving the scientific community without suitable fabrication protocols and rational guidelines for the full exploitation of their potential. In this work, we realize large polyacrylate nanocomposite scintillators based on CsPbBr3 NCs, which are synthesized via a novel room temperature, low waste turbo-emulsification approach, followed by their in situ transformation during the mass polymerization process. The interaction between NCs and polymer chains strengthens the scintillator structure, homogenizes the particle size distribution and passivates NC defects, resulting in nanocomposite prototypes with luminescence efficiency >90%, exceptional radiation hardness, 4800 ph/MeV scintillation yield even at low NC loading, and ultrafast response time, with over 30% of scintillation occurring in the first 80 ps, promising for fast-time applications in precision medicine and high-energy physics. Ultrafast radioluminescence and optical spectroscopy experiments using pulsed synchrotron light further disambiguate the origin of the scintillation kinetics as the result of charged-exciton and multiexciton recombination formed under ionizing excitation. This highlights the role of nonradiative Auger decay, whose potential impact on fast timing applications we anticipate via a kinetic model.

A radiation hard RISC-V microprocessor for high-energy physics applications

Recently, applications in high energy physics are considering the use of RISC-V microprocessors in harsh radiation environments. In this paper, we present the STRV-R1, a first RISC-V processor design adapted to ensure sufficient tolerance to radiation-induced soft errors in a near-beamline LHC environment and to enable the development of microprocessor-based systems in such environments. We compare the resource overhead imposed by the TMR based protection scheme with an unprotected design. The processor was exposed to 1 Grad TID to verify adequate hardness against long-term cumulative ionizing damage. Pulsed laser and heavy ion irradiations were performed to characterize the response of the design to radiation-induced soft errors. Over the different irradiation configurations, the cross sections for SEFIs were improved by up to 8000× compared to the SEU cross section by introducing TMR-based soft error protection. This reduction in cross section resulted in a 5.12× increase in the cell count of the processor design compared to the unprotected design and a 4.96× increase in the required power budget. During irradiation, it was observed that in systems with complex combinatorial logic, such as a RISC-V microprocessor core, the SEEs are predominantly caused by SETs.

A Scalable DC/DC Converter Topology With Modularized Energy Storage for High Energy Physics Applications

This paper presents the design and control of a high-power modular DC-DC converter for electromagnets used in high energy physics applications. The fundamental building modules, herein called bricks, can either be connected to the grid or to separate energy-storage components. The proposed modular converter enables independent power flow control among the bricks under control that is agnostic to mission profiles. In particular, the proposed converter design and control allow for independent control of currents delivered from each brick, while respecting the total voltage constraints, and losses optimisation of the converter without compromising the desired electrical performance. In addition, the cost potential of the converter is briefly analysed. The performance of the proposed modular converter is validated experimentally on a full-scale lab prototype rated at 800 kW. The proposed converter demonstrates how separating the total storage into a number of storage bricks can be beneficial to adapt the design of a modular converter to a large range of profiles and operating constraints. It is shown that up to 30% cost savings can be achieved by eliminating converter components in the storage and grid connection, while the converter performance on the load is maintained and the same amount of energy is recycled.

Study on properties of AISI 316L produced by Laser Powder Bed Fusion for high energy physics applications

Nowadays Additive Manufacturing (AM) is catching on and spreading across various fields at an astonishing rate. High energy physics, where materials are often exposed to special environmental conditions, is also starting to use this technology. The aim of this paper is to compare traditional and 3D printed stainless steel AISI 316L products with an eye turned to the specific high energy applications environment. The manufactured samples are subjected to different heat treatments, including vacuum firing, as this thermal treatment is usually adopted for ultra-vacuum applications and cryogenic. Experimental tests are carried out on a set of samples to analyse the material composition and to assess properties such as mechanical performance in cryogenic application, high magnetic fields and ultra-vacuum compatibility. Such analysis of the material behaviour allows weakness and strength of the technology to be identified, compared to traditional AISI 316L.

Evaluating Generative Adversarial Networks for particle hit generation in a cylindrical drift chamber using Fréchet Inception Distance

We use Fréchet Inception Distance (FID) measured in the latent spaces of pre-trained, fine-tuned and custom-made inception networks to evaluate Generative Adversarial Networks (GANs) developed by the COherent Muon to Electron Transition (COMET) collaboration to generate sequences of background hits in a Cylindrical Drift Chamber (CDC). We validate the convergence of the GANs' training and show that the use of self-attention layers reduces FID. Our method enables the use of FID as an evaluation metric even when an application-specific inception network is not readily available, making it transferable to other GAN applications in High Energy Physics.