High Energy Physics Applications Research Articles

Advanced fabrication process for particle absorbers of highly pure electroplated gold for microcalorimeter applications

Magnetic microcalorimeters (MMCs) have become a key technology for applications requiring outstanding energy resolution, fast signal rise time and excellent linearity. MMCs measure the temperature rise upon absorption of a single particle within a particle absorber by using a paramagnetic temperature sensor that is thermally coupled to the absorber. The design and fabrication of the particle absorber is key for excellent detector performance. Here, we present a microfabrication process for free-standing particle absorbers made of two stacked and independently electroplated high-purity Au layers. This enables, for example, the embedding of radioactive sources within the absorber for realizing a 4π detection geometry in radionuclide metrology or preparing detector arrays with variable quantum efficiency and energy resolution as requested for future applications in high-energy physics. Due to careful optimization of photoresist processing and electroplating parameters, the Au films are of very high purity and very high residual resistivity ratio values above 40, allowing for fast internal absorber thermalization.

Near-complete extraction of maximum stored energy from large-core fibers using coherent pulse stacking amplification of femtosecond pulses

High field science relies on ultrashort pulse lasers with multi-joule pulse energies for studying light–matter interactions under extreme conditions and for driving particle accelerators and secondary radiation sources of x rays, gamma rays, neutrons, positrons, muons, and protons. Next-generation laser drivers will require a 103−104 times increase in pulse repetition rates, producing multi-joule energies at multi-kilowatt average powers to enable practical applications in nuclear engineering, advanced materials, medicine, biology, homeland security, and high-energy physics. Spatially coherently combined femtosecond fiber lasers are recognized as a pathway to these next-generation drivers, with significant practical advantages including high efficiency and the possibility of compact integration. However, chirped pulse amplification in fibers is capable of extracting only a small fraction (usually ∼1%) of the maximum stored energy. Here we demonstrate near-complete maximum stored energy extraction with low accumulated nonlinearity from a large-core fiber amplifier using coherent pulse stacking amplification. We have amplified a 81-pulse stacking burst in a 85 µm core chirally coupled core Yb-doped fiber, extracting up to 9.5 mJ (∼90% of stored energy) with <4.5 radians of accumulated nonlinear phase, temporally combined this burst into a single pulse, and achieved 4.2 mJ pulses of 313 fs bandwidth-limited duration after compression. This represents, to our knowledge, the highest energy extracted and compressed into a femtosecond pulse from a single fiber amplifier, enabling approximately two orders of magnitude size reduction of future high-energy coherently spatially combined fiber laser arrays.

Differentiable vertex fitting for jet flavor tagging

This work explores the use of differentiable programming to integrate domain knowledge, in the form of domain specific software, into neural networks to develop scientific machine learning systems. We propose a differentiable vertex fitting algorithm that estimates the crossing point of multiple curves. In the high energy physics setting, these curves are defined by particle equations of motion and the crossing point represents the origin of particle production. This differentiable vertex fitting algorithm can be seamlessly integrated into neural networks, and we show its utility and efficacy in the high energy physics application of the classification of jets, i.e., collimated streams of particles in particle detectors whose originating parent particle we aim to classify. We demonstrate how differentiable vertex fitting can be integrated into larger transformer-based models for jet flavor tagging and show improvements in heavy flavor jet classification when compared to baseline models. Published by the American Physical Society 2024

High transparency Ce3+-doped oxyfluoride glass scintillator for X-ray imaging and γ-ray detection

High transparency Ce3+-doped dense gadolinium aluminum borosilicate (GSx) glass scintillator was synthesized in reducing atmosphere by melt-quenching process. The transmittance of the glasses exceeds 80%, and the light attenuation length is between 5-20 cm within the range of 400–600 nm. GSx glass shows an photoluminescence (PL) in range of 350–550 nm, with the strongest emission peak around 420 nm. The PL decay time of GSx glasses is around 37 ns, with a maximum PL quantum yield of 44.05%. The integrated X-ray excited luminescence (XEL) intensity of GSS glass is 52.5% compared with BGO crystal. For X-ray imaging, GSL glass based imaging system shows a high spatial resolution of 9 lp/mm, which is comparable to CsI:Tl X-ray detectors. Under γ-ray, the highest light yield of GSS glass is 1235 ph/MeV with an energy resolution of 24.0% at 662 keV, close to that of BSO crystal. The scintillation decay time of GSx glasses is consisted of fast (∼100 ns) and slow (∼560 ns) components. In thermally stimulated luminescence (TSL), GSS glass exhibits a strong TSL peak at approximately 440 K, with low intensity shoulders at about 520 K, implying different types of defects and traps. Therefore, GSx glass has promising prospects for X-ray imaging and high-energy physics applications.

Is infrared-collinear safe information all you need for jet classification?

Machine learning-based jet classifiers are able to achieve impressive tagging performance in a variety of applications in high-energy and nuclear physics. However, it remains unclear in many cases which aspects of jets give rise to this discriminating power, and whether jet observables that are tractable in perturbative QCD such as those obeying infrared-collinear (IRC) safety serve as sufficient inputs. In this article, we introduce a new classifier, Jet Flow Networks (JFNs), in an effort to address the question of whether IRC unsafe information provides additional discriminating power in jet classification. JFNs are permutation-invariant neural networks (deep sets) that take as input the kinematic information of reconstructed subjets. The subjet radius and a cut on the subjet’s transverse momenta serve as tunable hyperparameters enabling a controllable sensitivity to soft emissions and nonperturbative effects. We demonstrate the performance of JFNs for quark vs. gluon and Z vs. QCD jet tagging. For small subjet radii and transverse momentum cuts, the performance of JFNs is equivalent to the IRC-unsafe Particle Flow Networks (PFNs), demonstrating that infrared-collinear unsafe information is not necessary to achieve strong discrimination for both cases. As the subjet radius is increased, the performance of the JFNs remains essentially unchanged until physical thresholds that we identify are crossed. For relatively large subjet radii, we show that the JFNs may offer an increased model independence with a modest tradeoff in performance compared to classifiers that use the full particle information of the jet. These results shed new light on how machines learn patterns in high-energy physics data.

Low gain avalanche diodes for photon science applications

Low Gain Avalanche Diodes (LGADs) are silicon sensors designed to achieve an internal gain in the order of 10 through the impact ionization process. The development of LGADs was pushed forward by their application in High Energy Physics (HEP) experiments, where they will be employed to provide measurements of the time of arrival of minimum ionizing particles with a resolution of around 30 ps. The initial technological implementation of the sensors constrains their minimum channel size to be larger than 1 mm2, in order to reduce inefficiencies due to the segmentation of the gain structure. The gain of the sensors is kept in the order of 10 to limit the sensor shot noise and their power consumption. In photon science, the gain provided by the sensor can boost the signal-to-noise ratio of the detector system, effectively reducing the x-ray energy threshold of photon counting detectors and the minimum x-ray energy where single photon resolution is achieved in charge integrating detectors. This can improve the hybrid pixel and strip detectors for soft and tender x-rays by simply changing the sensor element of the detector system. Photon science applications in the soft and tender energy range require improvements over the LGADs developed for HEP, in particular the presence of a thin entrance window to provide a satisfactory quantum efficiency and channel size with a pitch of less than 100 μm. In this review, the fundamental aspects of the LGAD technology are presented, discussing also the ongoing and future developments that are of interest for photon science applications.

Testing silicon photonic Mach–Zehnder modulators versus total ionizing dose

Silicon photonics is emerging as a key technology for developing radiation-tolerant optical transceivers. In this work, we present the electro-optical characterization of two radiation-hardened shallow-etched Mach–Zehnder modulators with different doping configurations when exposed to 1.2 Grad(SiO2) total ionizing dose. The trade-offs between radiation hardness and nominal performance metrics are highlighted to provide insights for optimizing SiPh devices for high-energy physics applications.

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

Tunable valence state and scintillation performance of dense Ce3+-doped borosilicate glasses prepared in ambient atmosphere

Tunable valence state and scintillation properties from Ce-doped gadolinium aluminoborosilicate glass scintillators 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.