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In this paper, we consider the advantages of an alternative design concept for HTS accelerator magnets operating at 20 K or above. The idea is primarily built on using REBCO tape as the main conductor, but may be applicable to other HTS. The key concepts are to align REBCO tapes in the most favourable field orientation and to make joints for every turn such that the tapes will not have to be wound over the saddle ends. We argue that such a concept involving resistive joints is viable at 20 K or above due to an increased cryogenic efficiency, and has multiple advantages that would more than compensate for the resistive heating cost penalty. First, the favourable tape orientation can allow a much higher current carrying capability. Second, the short unit length of tapes equal to the length of the magnet will be much more economical and can be specified at a higher performance than a long continuous piece equal to the number of turns multiplied by the length of the magnet. Third, any defective conductor can be replaced easily and at a much lower cost than an entire coil. Fourth, with each tape separately sourced and connected, efficient grading with stress management can be achieved. Fifth, the straight section of the magnet would be modular and easily scalable for production in industry. Correspondingly, the most challenging part is the end cap design and joint technology, whose geometrical constraints are well within national laboratories' capabilities, making the R&D and prototyping phases much more affordable, with a turnover time much quicker than testing full size magnets. Additional attractive potentials include conductor development (e.g., double-sided extra thick REBCO), novel diagnostics (e.g., individual tape quench detection and protection), synergy with fusion devices research (e.g., demountable joints), and other possibilities.

High transparency Ce3+-doped dense gadolinium aluminum borosilicate (GS x) 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. GS x glass shows an photoluminescence (PL) in range of 350–550 nm, with the strongest emission peak around 420 nm. The PL decay time of GS x glasses is around 37 ns, with a maximum PL quantum yield of 44.05%. The integrated X-ray excited luminescence (XEL) intensity of GS S glass is 52.5% compared with BGO crystal. For X-ray imaging, GS L 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 GS S 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 GS x glasses is consisted of fast (∼100 ns) and slow (∼560 ns) components. In thermally stimulated luminescence (TSL), GS S 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, GS x glass has promising prospects for X-ray imaging and high-energy physics applications. [ABSTRACT FROM AUTHOR]

Within the phenomenology of particle physics, the theoretical model of 4-zero textures is validated using a chi-square criterion that compares experimental data with the computational results of the model. Traditionally, analytical methods that often imply simplifications, combined with computational analysis, have been used to validate texture models. In this paper, we propose a new meta-heuristic variant of the differential evolution algorithm that incorporates aspects of the particle swarm optimization algorithm called "HE-DEPSO" to obtain chi-squared values that are less than a bound value, which exhaustive and traditional algorithms cannot obtain. The results show that the proposed algorithm can optimize the chi-square function according to the required criteria. We compare simulated data with experimental data in the allowed search region, thereby validating the 4-zero texture model. [ABSTRACT FROM AUTHOR]

In high-energy physics, one of the key areas of research is the study of extensive air showers (EAS). This phenomenon remains insufficiently understood; scientists still lack comprehensive knowledge about the nature of their origin and the mechanisms responsible for their formation in the upper layers of the atmosphere. In this paper, the main characteristics and properties of air showers are described in detail. Numerous experiments conducted at the Hadron-55 complex form the foundation of EAS research. The showers were recorded and analyzed using the ionization calorimeter method, which measures the energy of EAS by detecting the total ionization produced by particles. This calorimeter, equipped with iron and lead absorbers, ensures high measurement accuracy, with an experimental error of only 10%. The experimental data were processed using a program developed in C++. The experiments revealed that the most common particles in the showers are electron-photon and hadronic components, which account for 95-98% of the particles in the shower core. A total of 8717 showers were processed by the program over a period of 485 hours. An analysis of the energy range of the registered showers supports the theory of their galactic origin. Additionally, calculating the primary parameters allows for further, more detailed studies of EAS. [ABSTRACT FROM AUTHOR]

This synthesis highlights the research efforts in micro black holes in recent years and points to future prospects. Synthesizing recently published articles shows that micro black holes can have a variety of properties. This paper explores articles discussing the instantaneous evaporation of micro black holes due to Hawking Radiation, the inability of a micro black hole to accrete matter, the intense recoil effects that occur if a micro black hole absorbs matter, the stability of micro black holes, as well as the spin and electric charge they may possess. Modern methods of detecting black holes have been described as looking for specific particle signatures that match a predicted profile of Hawking Radiation from a micro black hole and looking for possible signs of a micro black hole remnant. Expectations suggest that planned future projects will aid in the search for micro black holes by taking advantage of high-energy cosmic particle collisions.

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Within the phenomenology of particle physics, the theoretical model of 4-zero textures is validated using a chi-square criterion that compares experimental data with the computational results of the model. Traditionally, analytical methods that often imply simplifications, combined with computational analysis, have been used to validate texture models. In this paper, we propose a new meta-heuristic variant of the differential evolution algorithm that incorporates aspects of the particle swarm optimization algorithm called “HE-DEPSO” to obtain chi-squared values that are less than a bound value, which exhaustive and traditional algorithms cannot obtain. The results show that the proposed algorithm can optimize the chi-square function according to the required criteria. We compare simulated data with experimental data in the allowed search region, thereby validating the 4-zero texture model.

Due to its short radiation length, moderate light output (8000‐9000 photons/MeV), high density (7.13 g cm−3), and non‐hygroscopicity, etc., Bi4Ge3O12 (BGO) material has been widely utilized as an advanced scintillator for irradiation detection. However, pure BGO cannot meet the requirements for future physics experiments and state‐of‐art industrial facilities coupled with a silicon photomultiplier (SiPM) due to its slow response time, poor radiation resistance, and excessive light output. Herein, a Ce‐doping strategy is reported for efficiently improving the overall performance (e.g., irradiation resistance, decay time, radioluminescence, and light output) of BGO that can be expectedly applied in future high energy physics detection. Ce‐doped BGO (BGO:Ce) displays higher radiation resistance ability under 10 h UV irradiation (97% of original) and a faster fluorescence lifetime (269 ns), superior to pure BGO. Furthermore, BGO:Ce shows ≈30% luminescence intensity of pure BGO, well eliminating the oversaturation of SiPM coupled with scintillation detectors. Theoretical calculations imply that an intense competition between electron or hole traps and Ce ions (Ce3+, Ce4+) can effectively reduce the concentration of color centers, thus enhancing irradiation resistance ability. [ABSTRACT FROM AUTHOR]

A proof-of-concept solution based on the machine learning techniques has been implemented and tested within the MUonE experiment designed to search for New Physics in the sector of anomalous magnetic moment of a muon. The results of the DNN based algorithm are comparable to the classical reconstruction, reducing enormously the execution time for the pattern recognition phase. The present implementation meets the conditions of classical reconstruction, providing an advantageous basis for further studies. [ABSTRACT FROM AUTHOR]

When using Wasserstein GAN loss function for training generative adversarial networks (GAN), it is theoretically necessary to limit the discriminators' expressive power (so-called discriminator normalization). Such limitation increases the stability of GAN training at the expense of a less expressive final model. Spectral normalization is one of the normalization algorithms that involves applying a fixed operation independently to each discriminator layer. However, the optimal strength of the discriminator limitation varies for different tasks, which requires a parameterized normalization method. This paper proposes modifications to the spectral normalization algorithm that allow changing the strength of the discriminator limitation. In addition to parameterization, the proposed methods can change the degree of limitation during training, unlike the original algorithm. The quality of the obtained models is explored for each of the proposed methods. [ABSTRACT FROM AUTHOR]

Superconducting magnets designed for high energy physics and nuclear fusion require mechanical and electrical integrity to perform at high currents and magnetic fields. Vacuum Pressure Impregnation (VPI), a process of curing epoxy in and around the superconducting wires, is often used to support and consolidate a magnet. However, the heat and mechanical stresses associated with the process can degrade the wires, significantly lowering their critical current. This study explores different methods of potting and curing CORC wire with the aim of reducing wire performance degradation to less than 3% measured at 77 K, self-field. The wires were 2.9 mm in diameter consisting of a total of six REBCO tapes (three layers of two tapes). Two bending radii (20 mm and 50 mm) were tested to mimic the winding shape of a magnet. Mix 61 epoxy was used in preliminary tests for potting. For each test, two wires were used, and their critical currents were measured simultaneously in liquid nitrogen at 77 K - in their straight form, then bent, followed by the heat treatment used for Mix 61 but without epoxy and finishing with the full epoxy impregnation test. Later tests were performed using CTD-528 to explore a room temperature cure, limiting possibility of degradation from thermal expansion and prolonged exposure of the REBCO tapes to elevated temperature. Here we report the experimental results with multiple CORC wires and different curing schedules. The results obtained are the first steps toward identifying the VPI process with minimum degradation in critical current to be implemented in high-field magnets using CORC wires.

Current quantum systems have significant limitations affecting the processing of large datasets with high dimensionality, typical of high energy physics. In the present paper, feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods, trained both in the reduced and the complete datasets. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets. Sequential Backward Selection and Principal Component Analysis techniques were used for feature's selection and while the former can produce the better quantum machine learning models in specific cases, it is more unstable. Additionally, we show that such variability in the results is caused by the use of discrete variables, highlighting the suitability of Principal Component analysis transformed data for quantum machine learning applications in the high energy physics context.

Data analysis workflows in High Energy Physics (HEP) read data written in the ROOT columnar format. Such data has traditionally been stored in files that are often read via the network from remote storage facilities, which represents a performance penalty especially for data processing workflows that are I/O bound. To address that issue, this paper presents a new caching mechanism, implemented in the I/O subsystem of ROOT, which is independent of the storage backend used to write the dataset. Notably, it can be used to leverage the speed of high-bandwidth, low-latency object stores. The performance of this caching approach is evaluated by running a real physics analysis on an Intel DAOS cluster, both on a single node and distributed on multiple nodes. [ABSTRACT FROM AUTHOR]

There have been a large number of important discoveries in the field of experimental high energy physics over the past fifty years. Some of the important milestones are (1) discovery of the neutral current to establish the first step of the electroweak theory; (2) evidence of charm and beauty through the search of quarkonia and multi-muon events in deep inelastic scattering; (3) discovery of the third generation through the discovery of the tau lepton; (4) evidence of Bjorken scale breaking to initiate Quantum Chromodynamics, the theory of strong interaction; (5) observation of parton (quark) showers in the formation of jets and discovery of gluons; (6) discovery of the W and Z bosons, the mediators of the weak interaction; (7) precision measurements of the properties of the W and the Z bosons to establish the correctness of radiative corrections and to predict of yet to be discovered particles like the top quark and the Higgs boson; (8) discovery of the top quark and the tau neutrino; (9) establishing neutrino oscillation and thereby massive neutrinos; (10) finding CP violation in the decays of hadrons from the third generation of quarks; (11) discovery of the Higgs boson. During this process, the field of experimental high energy physics also experienced revolutions in the detection techniques, triggering and data acquisition systems and finally the use of new analysis techniques like detector simulation and artificial intelligence. Some of these important discoveries will be outlined in this paper. [ABSTRACT FROM AUTHOR]

The continuous growth of data production in almost all scientific areas raises new problems in data access and management, especially in a scenario where the end-users, as well as the resources that they can access, are worldwide distributed. This work is focused on the data caching management in a Data Lake infrastructure in the context of the High Energy Physics field. We are proposing an autonomous method, based on Reinforcement Learning techniques, to improve the user experience and to contain the maintenance costs of the infrastructure. [ABSTRACT FROM AUTHOR]

A large bore 'High-Temperature Superconductor Cable Test Facility Magnet' for testing advanced cables and inserts in high transverse field is in its design phase. This magnet will be the core component of a facility for developing conductors and accelerator magnets operating above 15 T, an enabling technology for next-generation fusion devices using magnetic confinement of plasma and for future energy frontier colliders. The procurement of Nb3Sn conductor, fabrication of cables, winding of coils, and assembly of the dipole magnet will be done at Lawrence Berkeley National Laboratory (LBNL) and the test pit and cryostat will be constructed at Fermi National Accelerator Laboratory. This article will present the conductor element of the LBNL project, specifically cable design parameters (based on the Bruker OST RRP^{\bigcirc \!\!\!\! {\hbox{R}}} Nb3Sn superconducting wire) and the development phase cable fabrication experience. Challenges of the cable fabrication will be discussed. The wire and cable planned for this magnet are similar to those under study for the Future Circular Collider and other large facility magnets. The successful fabrication of the development cable has positive implications for these other projects.

2023 is the year of commemoration of M.L. Ter-Mikayelian, a scientist who had important contribution to high energy physics, coherent bremsstrahlung, quantum and nonlinear optics, quantum electronics and laser physics, high-temperature superconductivity, and related fields. This article is scientific biography of the outstanding scientist. The range of scientific interests of M.L. Ter-Mikaelyan was really wide. In this issue, we tried to collect the works of his students and followers. [ABSTRACT FROM AUTHOR]

Measurements of neutral current Drell-Yan production at large invariant dilepton masses can be used to test the energy scale dependence (running) of the electroweak mixing angle. In this work, we make use of a novel implementation of the full next-to-leading order electroweak radiative corrections to the Drell-Yan process using the MS‾ renormalization scheme for the electroweak mixing angle. The potential of future analyses using proton-proton collisions at s=13.6TeV in the Run 3 and High-Luminosity phases of the LHC is explored. In this way, the Standard Model predictions for the MS‾ running at TeV scales can be probed.

The goal of this study is to check which, if any, of the published versions of the pion and kaon fragmentation functions (FFs) is compatible with the STAR data on semi-inclusive pion and kaon production in proton–proton collisions, and on the basis of this analysis to make reliable predictions for the p T spectra of the pions and kaons in inclusive pion and kaon production at the future NICA proton–proton collider. The calculations are carried out in next-to-leading order (NLO) of perturbative quantum chromodynamics (pQCD), using the well-tested CTEQ6 parton distributions. We consider the following pion and kaon FFs — DSEHS-14 [D. de Florian, R. Sassot, M. Epele, R. J. Hernández-Pinto and M. Stratmann, Phys. Rev. D 91, 014035 (2015), 1410.6027], DSEHS-17 [D. de Florian, M. Epele, R. J. Hernandez-Pinto, R. Sassot and M. Stratmann, Phys. Rev. D 95, 094019 (2017), 1702.06353], LSS-15 [E. Leader, A. V. Sidorov and D. B. Stamenov, Phys. Rev. D 93, 074026 (2016), 1506.06381], HKNS-07 [M. Hirai, S. Kumano, T. H. Nagai and K. Sudoh, Phys. Rev. D 75, 094009 (2007), hep-ph/0702250] and AKK-08 [S. Albino, B. A. Kniehl and G. Kramer, Nucl. Phys. B 803, 42 (2008), 0803.2768]. Our analysis shows that within the experimental errors all tested sets of FFs provide a good fit to STAR data at the c.m. energy S = 2 0 0 GeV , and the best ones are both LSS-15 and DSEHS-14 for pions and DSEHS-17 for kaons. From comparison of the LO and NLO results it is clear that the latter fit data much better, specially in the region of small p T . The NLO cross-sections are also less scale- Q 2 dependent, where p T / 2 ≤ Q ≤ 2 p T , than the LO ones. In order to make predictions for NICA energies, we compare the NLO pQCD results with the existing experimental BES STAR data on semi-inclusive hadron production in the most peripheral Au+Au collisions where the nuclear effects can be neglected. The comparison for lower energy scales, like at NICA, shows that a purely pQCD approach is inadequate and suggests the necessity to take into account also higher-order effects of initial-state soft-gluon radiation. Nevertheless, these data on the p T spectra of π + , K + and also the ratios π − / π + and K − / K + seem to favor LSS-15 and DSEHS-14 FFs for pions and DSEHS-17 for kaons, similarly as at the energy scale S = 2 0 0 GeV. [ABSTRACT FROM AUTHOR]

We study a model for production of stable micro black holes based on investigation of the thermodynamics of micro black holes and the LHC test. That showed how this production can be obtained by a thermodynamic process of stability. The general second law of black hole thermodynamics plays an important role here and, through Hawking radiation and fusion reactions entropy formulas a valid total entropic is obtained. Therefore, we reach an energy of stability by quantum perturbation expansion over this total entropic formula that is illustrated in detail in this paper. Based on this study, the producing of stable particles (in terms of our investigation, micro-black holes) at the LHC might yielded an interesting result that it is worth a try, which could have different results.

Links able to sustain high-speed data transfer while exposed to radiation phenomena are required by several applications, including aerospace and high-energy physics experiments. To satisfy this need, this paper outlines the design of a radiation-hard line driver for communication up to 10 Gb/s. The driver design is focused on the techniques adopted to increase its radiation hardness, namely the use of long-channel transistors, the avoidance of p-type MOSFETs and thick oxide devices. Circuital strategies to boost driver speed, such as inductive peaking, buffer chaining, and optimal layout placement and routing, are discussed and implemented to compensate for the downsides caused by adopted radiation-hard techniques. The driver, fabricated in 65 nm technology, has been experimentally tested demonstrating its ability to operate up to 10 Gb/s in a radiation-pervaded environment. In particular, after exposure to 1 Grad(SiO2) X-ray, the line driver exhibits an output signal amplitude reduction of 18.3% and a jitter increase of 5.53%. Performed temperature tests highlight that the line driver is capable to operate at 125 °C with 15.9% and 6.7% output signal swing reduction and jitter increase, respectively. The temperature tests also demonstrate the driver’s ability to work up to 160 °C with an output signal swing reduction below 25% and a jitter increase below 12%.

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. [ABSTRACT FROM AUTHOR]

The alpha magnetic spectrometer (AMS) is a high-precision particle detector onboard the International Space Station containing six different subdetectors. The transition radiation detector and electromagnetic calorimeter (ECAL) are used to separate electrons/positrons from the abundant cosmic-ray proton background. The positron flux measured in space by AMS falls with a power law which unexpectedly softens above 25 GeV and then hardens above 280 GeV. Several theoretical models try to explain these phenomena, and a more accurate measurement of positrons at higher energies is needed to help test them. The currently used methods to reject the proton background at high energies involve extrapolating shower features from the ECAL to use as inputs for boosted decision tree and likelihood classifiers. We present a new approach for particle identification with the AMS ECAL using deep learning (DL). By taking the energy deposition within all the ECAL cells as an input and treating them as pixels in an image-like format, we train an MLP, a CNN, and multiple ResNets and convolutional vision transformers (CvTs) as shower classifiers. Proton rejection performance is evaluated using Monte Carlo (MC) events and ISS data separately. For MC, using events with a reconstructed energy between 0.2–2 TeV, at 90% electron accuracy, the proton rejection power of our CvT model is more than five times that of the other DL models. Similarly, for ISS data with a reconstructed energy between 50–70 GeV, the proton rejection power of our CvT model is more than 2.5 times that of the other DL models.

Highly granular pixel detectors allow for increasingly precise measurements of charged particle tracks. Next-generation detectors require that pixel sizes will be further reduced, leading to unprecedented data rates exceeding those foreseen at the High- Luminosity Large Hadron Collider. Signal processing that handles data incoming at a rate of $\mathcal{O}$ (40 MHz) and intelligently reduces the data within the pixelated region of the detector at rate will enhance physics performance at high luminosity and enable physics analyses that are not currently possible. Using the shape of charge clusters deposited in an array of small pixels, the physical properties of the traversing particle can be extracted with locally customized neural networks. In this first demonstration, we present a neural network that can be embedded into the on-sensor readout and filter out hits from low momentum tracks, reducing the detector’s data volume by 57.1%–75.7%. The network is designed and simulated as a custom readout integrated circuit with 28 nm CMOS technology and is expected to operate at less than 300 $\mu W$ with an area of less than 0.2 mm ^2 . The temporal development of charge clusters is investigated to demonstrate possible future performance gains, and there is also a discussion of future algorithmic and technological improvements that could enhance efficiency, data reduction, and power per area.

The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to new phenomena not previously observed. We show that novel machine learning algorithms, specifically Normalizing Flows and Flow Matching, can be used to replicate accurate simulations from traditional approaches with several orders of magnitude of speed-up. The classical simulation chain starts from a physics process of interest, computes energy deposits of particles and electronics response, and finally employs the same reconstruction algorithms used for data. Eventually, the data are reduced to some high-level analysis format. Instead, we propose an end-to-end approach, simulating the final data format directly from physical generator inputs, skipping any intermediate steps. We use particle jets simulation as a benchmark for comparing both discrete and continuous Normalizing Flows models. The models are validated across a variety of metrics to identify the most accurate. We discuss the scaling of performance with the increase in training data, as well as the generalization power of these models on physical processes different from the training one. We investigate sampling multiple times from the same physical generator inputs, a procedure we name oversampling , and we show that it can effectively reduce the statistical uncertainties of a dataset. This class of ML algorithms is found to be capable of learning the expected detector response independently of the physical input process. The speed and accuracy of the models, coupled with the stability of the training procedure, make them a compelling tool for the needs of current and future experiments.

In this work we demonstrate that significant gains in performance and data efficiency can be achieved in High Energy Physics (HEP) by moving beyond the standard paradigm of sequential optimization or reconstruction and analysis components. We conceptually connect HEP reconstruction and analysis to modern machine learning workflows such as pretraining, finetuning, domain adaptation and high-dimensional embedding spaces and quantify the gains in the example usecase of searches of heavy resonances decaying via an intermediate di-Higgs system to four b -jets. To our knowledge this is the first example of a low-level feature extraction network finetuned for a downstream HEP analysis objective.

The Compact Muon Solenoid (CMS) is one of the general purpose detectors at the CERN Large Hadron Collider (LHC), where the products of proton–proton collisions at the center of mass energy up to 13.6 TeV are reconstructed. The electromagnetic calorimeter (ECAL) is one of the crucial components of the CMS since it reconstructs the energies and positions of electrons and photons. Even though several Machine Learning (ML) algorithms have been already used for calorimetry, with the constant advancement of the field, more and more sophisticated techniques have become available, which can be beneficial for object reconstruction with calorimeters. In this paper, we present two novel ML algorithms for object reconstruction with the ECAL that are based on graph neural networks (GNNs). The new approaches show significant improvements compared to the current algorithms used in CMS. [ABSTRACT FROM AUTHOR]