bluuebunny/crossref_metadata_2025 huggingface dataset:
Cyclic behavior of the R/C frames with reinforced masonry infills
Authors: Tanjung Jafril , Maidiawati
Published: August 2021

This study focuses on the experimental works to define the behavior of the reinforced concrete (R/C) frame model with the strengthening of the brick masonry infill by using the embedded reinforcement bars subjected to lateral reversed cyclic loads. A previous study by applying the lateral monotonic static loads showed that the embedded reinforcement bars increased the lateral capacity of the R/C frame and also delayed the failure of the brick masonry infill and R/C frame structure as well. However, in order to define its seismic capacity, a lateral reversed cyclic loading is required. The experimental works in this study were conducted by preparing and testing the 1/4 scaled-down R/C frame specimens represented the first story of the middle multi-story commonly constructed in the earthquake-prone area such as West Sumatera, Indonesia. The R/C frame specimens were two R/C frames with brick masonry infills where one of them strengthened by the embedded reinforced bars. All specimens were tested for applying the lateral reversed cyclic loads. The applied lateral load, the lateral displacement, the progressive cracks, and the failure mode of the specimens were observed and recorded during experimental works. As it was expected, the presence of the embedded reinforced bars in the brick masonry infills increases the seismic capacity and stiffness of the R/C specimens and also delayed the failure of the specimens. The experimental results in this study imply the simple strengthening method for the brick masonry infills.
XFEM based fracture analysis of single notch reactive powder concrete specimen subjected to three point bending test
Authors: Kusumaningrum Patria , Budiono Bambang , Fajar Muhammad , Elitha
Published: August 2021

An extended finite element method (XFEM) for fracture problem within the finite element software ABAQUS is adopted in order to investigate the mechanical properties and fracture behaviour of RPC material. The RPC materials observed are plain RPC of 0% and steel fiber RPC (SFRPC) of 2% volume fraction. Single edge notch three-point bending (TPB) tests of RPC materials are performed. The mechanical properties of RPC with respect to single edge notch TPB test, i.e. tensile stress, CMOD, and fracture energy obtained from experiment are then compared to those obtained from numerical analysis. A good agreement is observed between the experimental and numerical results.
Evolution of ATLAS analysis workflows and tools for the HL-LHC era
Authors: Cameron David , Forti Alessandra , Klimentov Alexei , Pacheco Pages Andrés , South David
Published: August 2021

The High Luminosity LHC project at CERN, which is expected to deliver a ten-fold increase in the luminosity of proton-proton collisions over LHC, will start operation towards the end of this decade and will deliver an unprecedented scientific data volume of multi-exabyte scale. This vast amount of data has to be processed and analysed, and the corresponding computing facilities must ensure fast and reliable data processing for physics analyses by scientific groups distributed all over the world. The present LHC computing model will not be able to provide the required infrastructure growth, even taking into account the expected evolution in hardware technology. To address this challenge, several novel methods of how end-users analysis will be conducted are under evaluation by the ATLAS Collaboration. State-of-the-art workflow management technologies and tools to handle these methods within the existing distributed computing system are now being evaluated and developed. In addition the evolution of computing facilities and how this impacts ATLAS analysis workflows is being closely followed.
The fight against COVID-19: Running Folding@Home simulations on ATLAS resources
Authors: Forti Alessandra , Glushkov Ivan , Heinrich Lukas , Lassnig Mario , South David , Walker Rodney
Published: August 2021

Following the outbreak of the COVID–19 pandemic, the ATLAS experiment considered how it could most efficiently contribute using its distributed computing resources. After considering many suggestions, examining several potential projects and following the advice of the CERN COVID Task Force, it was decided to engage in the Folding@Home initiative, which provides payloads that perform protein folding simulations. This paper describes how ATLAS made a significant contribution to this project over the summer of 2020.
The evolution of the CMS monitoring infrastructure
Authors: Ariza-Porras Christian , Kuznetsov Valentin , Legger Federica , Indra Rahul , Tuckus Nikodemas , Uzunoglu Ceyhun ,   on behalf of the CMS Collaboration
Published: August 2021

The CMS experiment at the CERN LHC (Large Hadron Collider) relies on a distributed computing infrastructure to process the multi-petabyte datasets where the collision and simulated data are stored. A scalable and reliable monitoring system is required to ensure efficient operation of the distributed computing services, and to provide a comprehensive set of measurements of the system performances. In this paper we present the full stack of CMS monitoring applications, partly based on the MONIT infrastructure, a suite of monitoring services provided by the CERN IT department. These are complemented by a set of applications developed over the last few years by CMS, leveraging open-source technologies that are industry-standards in the IT world, such as Kubernetes and Prometheus. We discuss how this choice helped the adoption of common monitoring solutions within the experiment, and increased the level of automation in the operation and deployment of our services.
Seamless integration of commercial Clouds with ATLAS Distributed Computing
Authors: Megino Fernando Barreiro , Bawa Harinder Singh , De Kaushik , Elmsheuser Johannes , Klimentov Alexei , Lassnig Mario , Serfon Cédric , Wegner Tobias
Published: August 2021

The CERN ATLAS Experiment successfully uses a worldwide distributed computing Grid infrastructure to support its physics programme at the Large Hadron Collider (LHC). The Grid workflow system PanDA routinely manages up to 700,000 concurrently running production and analysis jobs to process simulation and detector data. In total more than 500 PB of data are distributed over more than 150 sites in the WLCG and handled by the ATLAS data management system Rucio. To prepare for the ever growing data rate in future LHC runs new developments are underway to embrace industry accepted protocols and technologies, and utilize opportunistic resources in a standard way. This paper reviews how the Google and Amazon Cloud computing services have been seamlessly integrated as a Grid site within PanDA and Rucio. Performance and brief cost evaluations will be discussed. Such setups could offer advanced Cloud tool-sets and provide added value for analysis facilities that are under discussions for LHC Run-4.
The ATLAS Data Carousel Project Status
Authors: Borodin Mikhail , Di Girolamo Alessandro , Karavakis Edward , Klimentov Alexei , Korchuganova Tatiana , Lassnig Mario , Maeno Tadashi , Padolski Siarhei , Zhao Xin ,   on behalf of ATLAS Software and Computing
Published: August 2021

The High Luminosity upgrade to the LHC, which aims for a tenfold increase in the luminosity of proton-proton collisions at an energy of 14 TeV, is expected to start operation in 2028/29 and will deliver an unprecedented volume of scientific data at the multi-exabyte scale. This amount of data has to be stored, and the corresponding storage system must ensure fast and reliable data delivery for processing by scientific groups distributed all over the world. The present LHC computing and data management model will not be able to provide the required infrastructure growth, even taking into account the expected hardware technology evolution. To address this challenge, the Data Carousel R&D project was launched by the ATLAS experiment in the fall of 2018. State-of-the-art data and workflow management technologies are under active development, and their current status is presented here.
Research and Evaluation of RoCE in IHEP Data Center
Authors: Zeng Shan , Qi Fazhi , Han Lei , Gong Xiangyu , Wu Tao
Published: August 2021

With more and more large-scale scientific facilities are built, more and more HPC requirements are needed in IHEP. RDMA is a technology that allows servers in a network to exchange data in main memory without involving the processor, cache or operating system of either server, which can provide high bandwidth and low latency. There are two RDMA technologies which were InfiniBand and a relative newcomer called RoCE – RDMA over Converged Ethernet. This paper introduces the RoCE technology, we research and compare the performance of both IB and RoCE in IHEP data center, and we also evaluate the application scenarios of RoCE which can support our future technology selection in HEPS. In the end, we present our future plan.
Exploitation of the MareNostrum 4 HPC using ARC-CE
Authors: Acosta-Silva Carles , Del Peso José , Fullana Torregrosa Esteban , González de la Hoz Santiago , Pacheco Pages Andrés , Salt José , Sánchez Martínez Javier
Published: August 2021

The resources of the HPC centers are a potential aid to meet the future challenges of HL-LHC [1] in terms of computational requirements. Spanish HPC centers have recently been used to implement all necessary edge services to integrate resources into the LHC experiment workflow management system. In this article, we describe the integration of ATLAS with the extension plan to other LHC experiments. We chose to configure a dedicated ARC-CE [2] and interact with the HPC login and transfer nodes using ssh commands.

The repository that includes a partial copy of the ATLAS experiment software on CVMFS is packaged in a singularity image to overcome network isolation for HPC nodes and reduce software requirements. ATLAS provided the initial container, and the authors adapted it to the specific HPC environment. This article shows the Spanish contribution to the simulation of experiments after the Spanish Ministry of Science agreement and the Barcelona Supercomputing Center (BSC), the center that operates MareNostrum 4. Finally, we discuss some challenges to take advantage of the next generation of HPC machines with heterogeneous architecture combining CPU and GPU.
Building a Distributed Computing System for LDMX
Authors: Bryngemark Lene Kristian , Cameron David , Dutta Valentina , Eichlersmith Thomas , Konya Balazs , Moreno Omar , Mullier Geoffrey , Paganelli Florido , Pöttgen Ruth , Rogers Fuzzy , Salnikov Andrii , Weakliem Paul
Published: August 2021

Particle physics experiments rely extensively on computing and data services, making e-infrastructure an integral part of the research collaboration. Constructing and operating distributed computing can however be challenging for a smaller-scale collaboration.

The Light Dark Matter eXperiment (LDMX) is a planned small-scale accelerator-based experiment to search for dark matter in the sub-GeV mass region. Finalizing the design of the detector relies on Monte-Carlo simulation of expected physics processes. A distributed computing pilot project was proposed to better utilize available resources at the collaborating institutes, and to improve scalability and reproducibility.

This paper outlines the chosen lightweight distributed solution, presenting requirements, the component integration steps, and the experiences using a pilot system for tests with large-scale simulations. The system leverages existing technologies wherever possible, minimizing the need for software development, and deploys only non-intrusive components at the participating sites. The pilot proved that integrating existing components can dramatically reduce the effort needed to build and operate a distributed e-infrastructure, making it attainable even for smaller research collaborations.
Preservation through modernisation: The software of the H1 experiment at HERA
Authors: Britzger Daniel , Levonian Sergey , Schmitt Stefan , South David ,   for the H1 Collaboration
Published: August 2021

The lepton–proton collisions produced at the HERA collider represent a unique high energy physics data set. A number of years after the end of collisions, the data collected by the H1 experiment, as well as the simulated events and all software needed for reconstruction, simulation and data analysis, were migrated into a preserved operational mode at DESY. A recent modernisation of the H1 software architecture has been performed, which will not only facilitate on going and future data analysis efforts with the new inclusion of modern analysis tools, but also ensure the long-term availability of the H1 data and associated software. The present status of the H1 software stack, the data, simulations and the currently supported computing platforms for data analysis activities are discussed.
A Portable Implementation of RANLUX++
Authors: Hahnfeld Jonas , Moneta Lorenzo
Published: August 2021

High energy physics has a constant demand for random number generators (RNGs) with high statistical quality. In this paper, we present ROOT’s implementation of the RANLUX++ generator. We discuss the choice of relying only on standard C++ for portability reasons. Building on an initial implementation, we describe a set of optimizations to increase generator speed. This allows to reach performance very close to the original assembler version. We test our implementation on an Apple M1 and Nvidia GPUs to demonstrate the advantages of portable code.
Validation of Physics Models of Geant4 Versions 10.4.p03, 10.6.p02 and 10.7.p01 using Data from the CMS Experiment
Authors: Banerjee Sunanda , Ivanchenko Vladimir ,   on behalf of the CMS Collaboration
Published: August 2021

CMS tuned its simulation program and chose a specific physics model of Geant4 by comparing the simulation results with dedicated test beam experiments. Test beam data provide measurements of energy response of the calorimeter as well as resolution for well identified charged hadrons over a large energy region. CMS continues to validate the physics models using the test beam data as well as collision data from the Large Hadron Collider. Isolated charged particles are measured simultaneously in the tracker as well as in the calorimeters. These events are selected using dedicated triggers and are used to measure the response in the calorimeter. Different versions of Geant4 (10.2.p02, 10.4.p03, 10.6.p02) have been used by CMS for its Monte Carlo production and a new version (10.7.p01) is now chosen for future productions. A suitable physics list (collection of physics models) is chosen by optimizing performance against accuracy. A detailed comparison between data and Geant4 predictions is presented in this paper.
The Fast Simulation Chain in the ATLAS experiment
Authors: Javurkova Martina ,   on behalf of the ATLAS collaboration
Published: August 2021

The ATLAS experiment relies heavily on simulated data, requiring the production of billions of simulated proton-proton collisions every run period. As such, the simulation of collisions (events) is the single biggest CPU resource consumer. ATLAS’s finite computing resources are at odds with the expected conditions during the High Luminosity LHC era, where the increase in proton-proton centre-of-mass energy and instantaneous luminosity will result in higher particle multiplicities and roughly five-fold additional interactions per bunch-crossing with respect to LHC Run-2. Therefore, significant effort within the collaboration is being focused on increasing the rate at which Monte Carlo events can be produced by designing and developing fast alternatives to the algorithms used in the standard Monte Carlo production chain.
A novel reconstruction framework for an imaging calorimeter for HL-LHC
Authors: Cristella Leonardo ,   on behalf of the CMS Collaboration
Published: August 2021

To sustain the harsher conditions of the high-luminosity LHC, the CMS collaboration is designing a novel endcap calorimeter system. The new calorimeter will predominantly use silicon sensors to achieve sufficient radiation tolerance and will maintain highly-granular information in the readout to help mitigate the effects of pileup. In regions characterised by lower radiation levels, small scintillator tiles with individual on-tile SiPM readout are employed. A unique reconstruction framework (TICL: The Iterative CLustering) is being developed to fully exploit the granularity and other significant detector features, such as particle identification and precision timing, with a view to mitigate pileup in the very dense environment of HL-LHC. The inputs to the framework are clusters of energy deposited in individual calorimeter layers. Clusters are formed by a density-based algorithm. Recent developments and tunes of the clustering algorithm will be presented. To help reduce the expected pressure on the computing resources in the HL-LHC era, the algorithms and their data structures are designed to be executed on GPUs. Preliminary results will be presented on decreases in clustering time when using GPUs versus CPUs. Ideas for machine-learning techniques to further improve the speed and accuracy of reconstruction algorithms will be presented.
CMS Full Simulation for Run 3
Authors: Ivanchenko Vladimir , Banerjee Sunanda , Hugo Gabrielle , Lo Meo Sergio , Osborne Ianna , Pedro Kevin , Piparo Danilo , Sorokin Dmitry , Srimanobhas Phat , Vuosalo Carl ,   on behalf of the CMS Collaboration
Published: August 2021

We report the status of the CMS full simulation for Run 3. During the long shutdown of the LHC a significant update has been introduced to the CMS code for simulation. The CMS geometry description is reviewed. Several important modifications were needed. CMS detector description software is migrated to the DD4Hep community developed tool. We will report on our experience obtained during the process of this migration. Geant4 10.7 is the CMS choice for Run 3 simulation productions. We will discuss arguments for this choice, the strategy of adaptation of a new Geant4 version, and will report on the physics performance of the CMS simulation. A special Geant4 Physics List configuration FTFP_BERT_EMM will be described, which provides a compromise between simulation accuracy and CPU performance. A significant fraction of time for simulation of CMS events is spent on tracking of charged particles in a magnetic field. In the CMS simulation a dynamic choice of Geant4 parameters for tracking in field is implemented. A new method is introduced into simulation of electromagnetic components of hadronic showers in the electromagnetic calorimeter of CMS. For low-energy electrons and positrons a parametrization of GFlash type is applied. Results of tests of this method will be discussed. In summary, we expect about 25% speedup of the CMS simulation production for Run 3 compared to the Run 2 simulations.
The ATLAS Tile Calorimeter Tools for Data Quality Assessment
Authors: Scheirich Daniel ,   on behalf of the ATLAS Tile Calorimeter System
Published: August 2021

The ATLAS Tile Calorimeter (TileCal) is the central part of the hadronic calorimeter of the ATLAS experiment and provides important information for reconstruction of hadrons, jets, hadronic decays of tau leptons and missing transverse energy. The readout is segmented into nearly 10000 channels that are calibrated by means of Cesium source, laser, charge injection, and integratorbased systems.

The data quality (DQ) relies on extensive monitoring of both collision and calibration data. Automated checks are performed on a set of pre-defined histograms and results are summarized in dedicated web pages. A set of tools is then used by the operators for further inspection of the acquired data with the goal of spotting the origins of problems or other irregularities. Consequently, the TileCal conditions data (calibration constants, channel statuses etc) are updated in databases that are used for the data-reprocessing, or serve as an important input for the maintenance works during the shutdown periods. This talk reviews the software tools used for the DQ monitoring with emphasis on recent developments aiming to integrate all tools into a single platform.
Deep neural network techniques in the calibration of space-charge distortion fluctuations for the ALICE TPC
Authors: Gorbunov Sergey , Hellbär Ernst , Innocenti Gian Michele , Ivanov Marian , Kabus Maja , Kleiner Matthias , Riaz Haris , Rohr David , Sadikin Rifki , Schweda Kai , Sekihata Daiki , Shahoyan Ruben , Völkel Benedikt , Wiechula Jens , Zampolli Chiara , Appelshäuser Harald , Büsching Henner , Graczykowski Łukasz , Grosse-Oetringhaus Jan Fiete , Hristov Peter , Gunji Taku , Masciocchi Silvia
Published: August 2021

The Time Projection Chamber (TPC) of the ALICE experiment at the CERN LHC was upgraded for Run 3 and Run 4. Readout chambers based on Gas Electron Multiplier (GEM) technology and a new readout scheme allow continuous data taking at the highest interaction rates expected in Pb-Pb collisions. Due to the absence of a gating grid system, a significant amount of ions created in the multiplication region is expected to enter the TPC drift volume and distort the uniform electric field that guides the electrons to the readout pads. Analytical calculations were considered to correct for space-charge distortion fluctuations but they proved to be too slow for the calibration and reconstruction workflow in Run 3. In this paper, we discuss a novel strategy developed by the ALICE Collaboration to perform distortion-fluctuation corrections with machine learning and convolutional neural network techniques. The results of preliminary studies are shown and the prospects for further development and optimization are also discussed.
Event vertex reconstruction with deep neural networks for the DarkSide-20k experiment
Authors: Goicoechea-Casanueva Victor , Kish Alexander , Maricic Jelena ,   on behalf of the DarkSide collaboration
Published: August 2021

While deep learning techniques are becoming increasingly more popular in high-energy and, since recently, neutrino experiments, they are less confidently used in direct dark matter searches based on dual-phase noble gas TPCs optimized for low-energy signals from particle interactions.

In the present study, the application of modern deep learning methods for event vertex reconstruction is demonstrated with an example of the 50-tonne liquid argon DarkSide-20k TPC with 8200 photosensors.

The developed methods successfully reconstruct event positions within sub-cm precision and apply to any dual-phase argon or xenon TPC of arbitrary size with any sensor shape and array pattern.
PandAna: A Python Analysis Framework for Scalable High Performance Computing in High Energy Physics
Authors: Groh Micah , Buchanan Norman , Doyle Derek , Kowalkowski James B. , Paterno Marc , Sehrish Saba
Published: August 2021

Modern experiments in high energy physics analyze millions of events recorded in particle detectors to select the events of interest and make measurements of physics parameters. These data can often be stored as tabular data in files with detector information and reconstructed quantities. Most current techniques for event selection in these files lack the scalability needed for high performance computing environments. We describe our work to develop a high energy physics analysis framework suitable for high performance computing. This new framework utilizes modern tools for reading files and implicit data parallelism. Framework users analyze tabular data using standard, easy-to-use data analysis techniques in Python while the framework handles the file manipulations and parallelism without the user needing advanced experience in parallel programming. In future versions, we hope to provide a framework that can be utilized on a personal computer or a high performance computing cluster with little change to the user code.
Performance of CUDA Unified Memory in CMS Heterogeneous Pixel Reconstruction
Authors: Kortelainen Matti J. , Kwok Martin ,   on behalf of the CMS Collaboration
Published: August 2021

The management of separate memory spaces of CPUs and GPUs brings an additional burden to the development of software for GPUs. To help with this, CUDA unified memory provides a single address space that can be accessed from both CPU and GPU. The automatic data transfer mechanism is based on page faults generated by the memory accesses. This mechanism has a performance cost, that can be with explicit memory prefetch requests. Various hints on the inteded usage of the memory regions can also be given to further improve the performance. The overall effect of unified memory compared to an explicit memory management can depend heavily on the application. In this paper we evaluate the performance impact of CUDA unified memory using the heterogeneous pixel reconstruction code from the CMS experiment as a realistic use case of a GPU-targeting HEP reconstruction software. We also compare the programming model using CUDA unified memory to the explicit management of separate CPU and GPU memory spaces.
Fast simulation of the electromagnetic calorimeter response using Self-Attention Generative Adversarial Networks
Authors: Ratnikov Fedor , Rogachev Alexander
Published: August 2021

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider need, so the experiment is in urgent need of new fast simulation techniques. The application of Generative Adversarial Networks is a promising solution to speed up the simulation while providing the necessary physics performance. In this paper we propose the Self-Attention Generative Adversarial Network as a possible improvement of the network architecture. The application is demonstrated on the performance of generating responses of the LHCb type of the electromagnetic calorimeter.
Design and engineering of a simplified workflow execution for the MG5aMC event generator on GPUs and vector CPUs
Authors: Valassi Andrea , Roiser Stefan , Mattelaer Olivier , Hageboeck Stephan
Published: August 2021

Physics event generators are essential components of the data analysis software chain of high energy physics experiments, and important consumers of their CPU resources. Improving the software performance of these packages on modern hardware architectures, such as those deployed at HPC centers, is essential in view of the upcoming HL-LHC physics programme. In this paper, we describe an ongoing activity to reengineer the Madgraph5_aMC@NLO physics event generator, primarily to port it and allow its efficient execution on GPUs, but also to modernize it and optimize its performance on vector CPUs. We describe the motivation, engineering process and software architecture design of our developments, as well as the current challenges and future directions for this project. This paper is based on our submission to vCHEP2021 in March 2021, complemented with a few preliminary results that we presented during the conference. Further details and updated results will be given in later publications.
An Error Analysis Toolkit for Binned Counting Experiments
Authors: Messerly Ben , Fine Rob , Olivier Andrew ,   on behalf of the MINERvA Collaboration
Published: August 2021

We introduce the MINERvA Analysis Toolkit (MAT), a utility for centralizing the handling of systematic uncertainties in HEP analyses. The fundamental utilities of the toolkit are the MnvHnD, a powerful histogram container class, and the systematic Universe classes, which provide a modular implementation of the many universe error analysis approach. These products can be used stand-alone or as part of a complete error analysis prescription. They support the propagation of systematic uncertainty through all stages of analysis, and provide flexibility for an arbitrary level of user customization. This extensible solution to error analysis enables the standardization of systematic uncertainty definitions across an experiment and a transparent user interface to lower the barrier to entry for new analyzers.
Fast and Accurate Electromagnetic and Hadronic Showers from Generative Models
Authors: Buhmann Erik , Diefenbacher Sascha , Eren Engin , Gaede Frank , Hundhausen Daniel , Kasieczka Gregor , Korcari William , Korol Anatolii , Krüger Katja , McKeown Peter , Rustige Lennart
Published: August 2021

Generative machine learning models offer a promising way to efficiently amplify classical Monte Carlo generators’ statistics for event simulation and generation in particle physics. Given the already high computational cost of simulation and the expected increase in data in the high-precision era of the LHC and at future colliders, such fast surrogate simulators are urgently needed. This contribution presents a status update on simulating particle showers in high granularity calorimeters for future colliders. Building on prior work using Generative Adversarial Networks (GANs), Wasserstein-GANs, and the information-theoretically motivated Bounded Information Bottleneck Autoencoder (BIB-AE), we further improve the fidelity of generated photon showers. The key to this improvement is a detailed understanding and optimisation of the latent space. The richer structure of hadronic showers compared to electromagnetic ones makes their precise modeling an important yet challenging problem. We present initial progress towards accurately simulating the core of hadronic showers in a highly granular scintillator calorimeter.
Graph Variational Autoencoder for Detector Reconstruction and Fast Simulation in High-Energy Physics
Authors: Hariri Ali , Dyachkova Darya , Gleyzer Sergei
Published: August 2021

Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with the detector is both time consuming and computationally expensive. With its proton-proton collision energy of 13 TeV, the Large Hadron Collider is uniquely positioned to detect and measure the rare phenomena that can shape our knowledge of new interactions. The High-Luminosity Large Hadron Collider (HLLHC) upgrade will put a significant strain on the computing infrastructure and budget due to increased event rate and levels of pile-up. Simulation of highenergy physics collisions needs to be significantly faster without sacrificing the physics accuracy. Machine learning approaches can offer faster solutions, while maintaining a high level of fidelity. We introduce a graph generative model that provides effiective reconstruction of LHC events on the level of calorimeter deposits and tracks, paving the way for full detector level fast simulation.
Deep learning based low-dose synchrotron radiation CT reconstruction
Authors: Li Ling , Hu Yu
Published: August 2021

Synchrotron radiation sources are widely used in various fields, among which computed tomography (CT) is one of the most important. The amount of effort expended by the operator varies depending on the subject. If the number of angles needed to be used can be greatly reduced under the condition of similar imaging effects, the working time and workload of the experimentalists will be greatly reduced. However, decreasing the sampling angle can produce serious artifacts and blur the details. We try to use a deep learning model which can build high quality reconstruction sparse data sampling from the angle of the image and ResAttUnet are put forward. ResAttUnet is roughly a symmetrical U-shaped network that incorporates similar mechanisms to ResNet and attention. In addition, the mixed precision is adopted to reduce the demand for video memory of the model and training time.
Apprentice for Event Generator Tuning
Authors: Krishnamoorthy Mohan , Schulz Holger , Ju Xiangyang , Wang Wenjing , Leyffer Sven , Marshall Zachary , Mrenna Stephen , Müller Juliane , Kowalkowski James B.
Published: August 2021

APPRENTICE is a tool developed for event generator tuning. It contains a range of conceptual improvements and extensions over the tuning tool Professor. Its core functionality remains the construction of a multivariate analytic surrogate model to computationally expensive Monte-Carlo event generator predictions. The surrogate model is used for numerical optimization in chi-square minimization and likelihood evaluation. Apprentice also introduces algorithms to automate the selection of observable weights to minimize the effect of mis-modeling in the event generators. We illustrate our improvements for the task of MC-generator tuning and limit setting.
Embedding of particle tracking data using hybrid quantum-classical neural networks
Authors: Rieger Carla , Tüysüz Cenk , Novotny Kristiane , Vallecorsa Sofia , Demirköz Bilge , Potamianos Karolos , Dobos Daniel , Vlimant Jean-Roch
Published: August 2021

The High Luminosity Large Hadron Collider (HL-LHC) at CERN will involve a significant increase in complexity and sheer size of data with respect to the current LHC experimental complex. Hence, the task of reconstructing the particle trajectories will become more involved due to the number of simultaneous collisions and the resulting increased detector occupancy. Aiming to identify the particle paths, machine learning techniques such as graph neural networks are being explored in the HEP.TrkX project and its successor, the Exa.TrkX project. Both show promising results and reduce the combinatorial nature of the problem. Previous results of our team have demonstrated the successful attempt of applying quantum graph neural networks to reconstruct the particle track based on the hits of the detector. A higher overall accuracy is gained by representing the training data in a meaningful way within an embedded space. That has been included in the Exa.TrkX project by applying a classical MLP. Consequently, pairs of hits belonging to different trajectories are pushed apart while those belonging to the same ones stay close together. We explore the applicability of variational quantum circuits that include a relatively low number of qubits applicable to NISQ devices within the task of embedding and show preliminary results.
Multi-particle reconstruction in the High Granularity Calorimeter using object condensation and graph neural networks
Authors: Qasim Shah Rukh , Long Kenneth , Kieseler Jan , Pierini Maurizio ,   for the CMS Collaboration, Nawaz Raheel
Published: August 2021

The high-luminosity upgrade of the LHC will come with unprecedented physics and computing challenges. One of these challenges is the accurate reconstruction of particles in events with up to 200 simultaneous protonproton interactions. The planned CMS High Granularity Calorimeter offers fine spatial resolution for this purpose, with more than 6 million channels, but also poses unique challenges to reconstruction algorithms aiming to reconstruct individual particle showers. In this contribution, we propose an end-to-end machine-learning method that performs clustering, classification, and energy and position regression in one step while staying within memory and computational constraints. We employ GravNet, a graph neural network, and an object condensation loss function to achieve this task. Additionally, we propose a method to relate truth showers to reconstructed showers by maximising the energy weighted intersection over union using maximal weight matching. Our results show the efficiency of our method and highlight a promising research direction to be investigated further.
Ethernet evaluation in data distribution traffic for the LHCb filtering farm at CERN
Authors: Krawczyk Rafał Dominik , Pisani Flavio , Colombo Tommaso , Frank Markus , Neufeld Niko
Published: August 2021

This paper evaluates the real-time distribution of data over Ethernet for the upgraded LHCb data acquisition cluster at CERN. The system commissioning ends in 2021 and its total estimated input throughput is 32 Terabits per second. After the events are assembled, they must be distributed for further data selection to the filtering farm of the online trigger. High-throughput and very low overhead transmissions will be an essential feature of such a system. In this work RoCE (Remote Direct Memory Access over Converged Ethernet) high-throughput Ethernet protocol and Ethernet flow control algorithms have been used to implement lossless event distribution. To generate LHCb-like traffic, a custom benchmark has been implemented. It was used to stress-test the selected Ethernet networks and to check resilience to uneven workload distribution. Performance tests were made with selected evaluation clusters. 100 Gb/s and 25 Gb/s links were used. Performance results and overall evaluation of this Ethernet-based approach are discussed.
Development of the Safety System for the Inner Tracking System of the ALICE Experiment
Authors: Blanc Pascal Herve , Mendez Lorenzo Patricia , Pons Xavier
Published: August 2021

During the LHC Long Shutdown 2, the ALICE experiment has undergone numerous upgrades to cope with the large amount of data expected. Among all new elements integrated into ALICE, the experiment counts with a new Inner Tracking System (ITS), with innovative pixel sensors that will substantially improve the performance of the system. The new detector is powered up through a complex Low Voltage (LV) distribution, increasing the power dissipated by the detector and requiring the installation of a large number of temperature measurement points. In 2020, a new safety system has been developed to distribute the ITS LV interlock system and to monitor the new temperature values. The safety system is based on a Siemens S7-1500 PLC device. The control application governing the PLC has been configured through the UNICOS-CPC infrastructure made at CERN for the standardisation of industrial applications. UNICOS-CPC enables both the automatisation of control tasks governing the PLC and the interface to the WinCC OA based SCADA system. This paper provides a complete description of the setup of this safety system.
Evolution of the energy efficiency of LHCb’s real-time processing
Authors: Aaij Roel , Cámpora Pérez Daniel Hugo , Colombo Tommaso , Fitzpatrick Conor , Gligorov Vladimir Vava , Hennequin Arthur , Neufeld Niko , Nolte Niklas , Schwemmer Rainer , Vom Bruch Dorothea
Published: August 2021

The upgraded LHCb detector, due to start datataking in 2022, will have to process an average data rate of 4 TB/s in real time. Because LHCb’s physics objectives require that the full detector information for every LHC bunch crossing is read out and made available for real-time processing, this bandwidth challenge is equivalent to that of the ATLAS and CMS HL-LHC software read-out, but deliverable five years earlier. Over the past six years, the LHCb collaboration has undertaken a bottom-up rewrite of its software infrastructure, pattern recognition, and selection algorithms to make them better able to efficiently exploit modern highly parallel computing architectures. We review the impact of this reoptimization on the energy efficiency of the realtime processing software and hardware which will be used for the upgrade of the LHCb detector. We also review the impact of the decision to adopt a hybrid computing architecture consisting of GPUs and CPUs for the real-time part of LHCb’s future data processing. We discuss the implications of these results on how LHCb’s real-time power requirements may evolve in the future, particularly in the context of a planned second upgrade of the detector.
A real-time FPGA-based cluster finding algorithm for LHCb silicon pixel detector
Authors: Bassi Giovanni , Giambastiani Luca , Lazzari Federico , Morello Michael J. , Pajero Tommaso , Punzi Giovanni
Published: August 2021

Starting from the next LHC run, the upgraded LHCb High Level Trigger will process events at the full LHC collision rate (averaging 30 MHz). This challenging goal, tackled using a large and heterogeneous computing farm, can be eased addressing lowest-level, more repetitive tasks at the earliest stages of the data acquisition chain. FPGA devices are very well-suited to perform with a high degree of parallelism and efficiency certain computations, that would be significantly demanding if performed on general-purpose architectures. A particularly time-demanding task is the cluster-finding process, due to the 2D pixel geometry of the new LHCb pixel detector. We describe here a custom highly parallel FPGA-based clustering algorithm and its firmware implementation. The algorithm implementation has shown excellent reconstruction quality during qualification tests, while requiring a modest amount of hardware resources. Therefore it can run in the LHCb FPGA readout cards in real time, during data taking at 30 MHz, representing a promising alternative solution to more common CPU-based algorithms.
Heterogeneous techniques for rescaling energy deposits in the CMS Phase-2 endcap calorimeter
Authors: Alves Bruno , Bocci Andrea , Kortelainen Matti , Pantaleo Felice , Rovere Marco
Published: August 2021

We present the porting to heterogeneous architectures of the algorithm used for applying linear transformations of raw energy deposits in the CMS High Granularity Calorimeter (HGCAL). This is the first heterogeneous algorithm to be fully integrated with HGCAL’s reconstruction chain. After introducing the latter and giving a brief description of the structural components of HGCAL relevant for this work, the role of the linear transformations in the calibration is reviewed. The many ways in which parallelization is achieved are described, and the successful validation of the heterogeneous algorithm is covered. Detailed performance measurements are presented, including throughput and execution time for both CPU and GPU algorithms, therefore establishing the corresponding speedup. We finally discuss the interplay between this work and the porting of other algorithms in the existing reconstruction chain, as well as integrating algorithms previously ported but not yet integrated.
Jet Single Shot Detection
Authors: Pol Adrian Alan , Aarrestad Thea , Govorkova Katya , Halily Roi , Kopetz Tal , Klempner Anat , Loncar Vladimir , Ngadiuba Jennifer , Pierini Maurizio , Sirkin Olya , Summers Sioni
Published: August 2021

We apply object detection techniques based on Convolutional Neural Networks to jet reconstruction and identification at the CERN Large Hadron Collider. In particular, we focus on CaloJet reconstruction, representing each event as an image composed of calorimeter cells and using a Single Shot Detection network, called Jet-SSD. The model performs simultaneous localization and classification and additional regression tasks to measure jet features. We investigate TernaryWeight Networks with weights constrained to {-1, 0, 1} times a layer- and channel-dependent scaling factors. We show that the quantized version of the network closely matches the performance of its full-precision equivalent.
A NONLINEAR KINETIC THEORY FOR HF PLASMA GENERATION AND ELECTRON HEATING.
Authors: Cicconi G. , Molinari V. , Pollachini L.
Published: August 2021

Nowdays a consistent progress has been obtained in describing, by means of kinetic models, the electron kinetics of many gas discharges and plasma generation ailso in nonsteady state and inhomogeneous to understand the microphysics of the phenomena involved in these plasmas /1/ /2/. Particularly a nonlinear asymptotic analysis of the positive column /4/ and the more extensive theoretical investigations on plasma column at high degree of ionization and under free fall and ambipolar diffusion regimes /5/, have been considered. The replacement of the positive column of a glow discharge, by means of a plasma column produced by a HF field, was recently also proved /3/. The theory presented in this paper may be considered in this context as a first approach for a more detailed description of plasma generation and electron heating in HF discharges.
Investigation of COVID-19 mathematical model under fractional order derivative
Authors: Shah Kamal , Arfan Muhammad , Deebani Wejdan , Shutaywi Meshal , Baleanu Dumitru
Published: August 2021

The given article is devoted to presentation of some results regarding existence and uniqueness of solution to a fractional order model that addressing the effect of immigration on the transmission dynamics of a population model. Further, in view of this investigation the effect of immigration have been checked on transmission of recent pandemic known as Corona virus COVID-19. The concerned results have been established by using fixed point theory approach. After investigation qualitative analysis of the considered model, by applying Laplace transform along with decomposition method, we have calculated some series type results for the concerned model. The unknown quantities of each equation have been decomposed into small quantities to calculate each small quantity very easily for the series solution by adding first few terms of the said quantities. Approximate results of some testing data with different cases are given to illustrate the results.
Public authorities as an object of modern historical and legal research
Authors: Chikhladze Levan Teimurazovich , Ganina Olga Yurievna , Babaeva Yulia Grigorievna , Aldabergenov Azamat Tynyshtykbayevich
Published: August 2021

The purpose of the study is to analyze the views of theoretical scientists on the concept of public authorities and the place of local self-government bodies in the system of public authorities, based on well-established concepts, doctrines, and ideas developed in modern realities. To achieve the stated goals and objectives in scientific work, general scientific and specific scientific methods were used. The principles of objectivity and consistency, methods of analysis, synthesis, comparison made it possible to study the views of theoretical scientists, highlighting the argumentation that is significant for the research topic, based on facts, guided by the principles of reliability and impartiality, considering the topic in all its versatility and contradictoriness. The research is based on the theoretical works of Russian scientists studying various aspects of the multifaceted problem of public authorities as state institutions. The novelty of the research lies in the attempt to theoretically comprehend and scientifically analyze the concepts of public authority and public authorities based on the works of domestic authors, as well as to determine the positions of theoretical scientists in the place of local self-government bodies in the system of public authorities. The study made it possible to substantiate the rejection of the established stereotypes in the identification of the concepts of “public power” and “state power”, the reorientation of legal science towards the formation of the concept of state power as a variant of public power, the legal basis of which is the law. The paper also concludes the implementation of power relations through public authorities. At the same time, a lack of a unified approach to determining the place of local self-government bodies in the system of public authorities at present is indicated.