bluuebunny/crossref_metadata_2025 huggingface dataset:
CloudBank for Europe
Authors: Devouassoux Marion , Fernandes João , Jones Bob , Manou Anna , Pinto Pereira da Cruz Inês
Published: August 2021

The vast amounts of data generated by scientific research pose enormous challenges for capturing, managing and processing this data. Many trials have been made in different projects (such as HNSciCloud and OCRE), but today, commercial cloud services do not yet play a major role in the production computing environments of the publicly funded research sector in Europe. Funded by the Next Generation Internet programme (NGI-Atlantic) from the EC, in partnership with the University California San Diego (UCSD), CERN is piloting the use of CloudBank in Europe. CloudBank has been developed by the UCSD, University of Washington and University of California, Berkeley with NSF grant support, to provide a set of managed services simplifying access to public cloud for research and education, via a cloud procurement partnership with Strategic Blue, a financial broker SME, specialised in cost management and optimisation. The European NGI experiment is provisioning cloud services from multiple vendors and deploying a series of use-cases in the domain of Machine Learning and HPCaaS, contributing to the scientific programme of the Large Hadron Collider. The main objective is to address technical, financial and legal challenges to determine whether CloudBank can be successfully used by Europe’s research community as part of its global research activity.
Harnessing HPC resources for CMS jobs using a Virtual Private Network
Authors: Tovar Benjamin , Bockelman Brian , Hildreth Michael , Lannon Kevin , Thain Douglas
Published: August 2021

The processing needs for the High Luminosity (HL) upgrade for the LHC require the CMS collaboration to harness the computational power available on non-CMS resources, such as High-Performance Computing centers (HPCs). These sites often limit the external network connectivity of their computational nodes. In this paper we describe a strategy in which all network connections of CMS jobs inside a facility are routed to a single point of external network connectivity using a Virtual Private Network (VPN) server by creating virtual network interfaces in the computational nodes. We show that when the computational nodes and the host running the VPN server have the namespaces capability enabled, the setup can run entirely on user space with no other root permissions required. The VPN server host may be a privileged node inside the facility configured for outside network access, or an external service that the nodes are allowed to contact. When namespaces are not enabled at the client side, then the setup falls back to using a SOCKS server instead of virtual network interfaces. We demonstrate the strategy by executing CMS Monte Carlo production requests on opportunistic non-CMS resources at the University of Notre Dame. For these jobs, cvmfs support is tested via fusermount (cvmfsexec), and the native fuse module.
Secure Command Line Solution for Token-based Authentication
Authors: Dykstra Dave , Altunay Mine , Teheran Jeny
Published: August 2021

The WLCG is modernizing its security infrastructure, replacing X.509 client authentication with the newer industry standard of JSON Web Tokens (JWTs) obtained through the Open ID Connect (OIDC) protocol. There is a wide variety of software available using the standards, but most of it is for Web browser-based applications and doesn’t adapt well to the command line-based software used heavily in High Throughput Computing (HTC). OIDC command line client software did exist, but it did not meet our requirements for security and convenience. This paper discusses a command line solution we have made based on the popular existing secrets management software from Hashicorp called vault. We made a package called htvault-config to easily configure a vault service and another called htgettoken to be the vault client. In addition, we have integrated use of the tools into the HTCondor workload management system, although they also work well independent of HTCondor. All of the software is open source, under active development, and ready for use.
ARCHIVER - Data archiving and preservation for research environments
Authors: Devouassoux Marion , Fernandes João , Jones Bob , Peluaga Lozada Ignacio , Urban Jakub
Published: August 2021

Over the last decades, several data preservation efforts have been undertaken by the HEP community, as experiments are not repeatable and consequently their data considered unique. ARCHIVER is a European Commission (EC) co-funded Horizon 2020 pre-commercial procurement project procuring R&D combining multiple ICT technologies including data-intensive scalability, network, service interoperability and business models, in a hybrid cloud environment. The results will provide the European Open Science Cloud (EOSC) with archival and preservation services covering the full research lifecycle. The services are co-designed in partnership with four research organisations (CERN, DESY, EMBL-EBI and PIC/IFAE) deploying use cases from Astrophysics, HEP, Life Sciences and Photon-Neutron Sciences creating an innovation ecosystem for specialist data archiving and preservation companies willing to introduce new services capable of supporting the expanding needs of research. The HEP use cases being deployed include the CERN Opendata portal, preserving a second copy of the completed BaBar experiment and the CERN Digital Memory digitising CERN’s multimedia archive of the 20th century. In parallel, ARCHIVER has established an Early Adopter programme whereby additional use cases can be incorporated at each of the project phases thereby expanding services to multiple research domains and countries.
NOTED: a framework to optimise network traffic via the analysis of data from File Transfer Services
Authors: Waczyńska Joanna , Martelli Edoardo , Karavakis Edward , Cass Tony
Published: August 2021

Network traffic optimisation is difficult as the load is by nature dynamic and seemingly unpredictable. However, the increased usage of file transfer services may help the detection of future loads and the prediction of their expected duration. The NOTED project seeks to do exactly this and to dynamically adapt network topology to deliver improved bandwidth for users of such services. This article introduces, and explains the features of, the two main components of NOTED, the Transfer Broker and the Network Intelligence component. The Transfer Broker analyses all queued and on-going FTS transfers, producing a traffic report which can be used by network controllers. Based on this report and its knowledge of the network topology and routing, the Network Intelligence (NI) component makes decisions as to when a network reconfiguration could be beneficial. Any Software Defined Network controller can then apply these decision to the network, so optimising transfer execution time and reducing operating costs.
Convolutional LSTM models to estimate network traffic
Authors: Waczyńska Joanna , Martelli Edoardo , Vallecorsa Sofia , Karavakis Edward , Cass Tony
Published: August 2021

Network utilisation efficiency can, at least in principle, often be improved by dynamically re-configuring routing policies to better distribute ongoing large data transfers. Unfortunately, the information necessary to decide on an appropriate reconfiguration—details of on-going and upcoming data transfers such as their source and destination and, most importantly, their volume and duration—is usually lacking. Fortunately, the increased use of scheduled transfer services, such as FTS, makes it possible to collect the necessary information. However, the mere detection and characterisation of larger transfers is not sufficient to predict with confidence the likelihood a network link will become overloaded. In this paper we present the use of LSTM-based models (CNN-LSTM and Conv-LSTM) to effiectively estimate future network traffic and so provide a solid basis for formulating a sensible network configuration plan.
Deploying a new realtime XRootD-v5 based monitoring framework for GridPP
Authors: Currie Robert , Yuan Wenlong
Published: August 2021

To optimise the performance of distributed compute, smaller lightweight storage caches are needed which integrate with existing grid computing workflows. A good solution to provide lightweight storage caches is to use an XRootD-proxy cache. To support distributed lightweight XRootD proxy services across GridPP we have developed a centralised monitoring framework. With the v5 release of XRootD it is possible to build a monitoring framework which collects distributed caching metadata broadcast from multiple sites. To provide the best support for these distributed caches we have built a centralised monitoring service for XRootD storage instances within GridPP. This monitoring solution is built upon experiences presented by CMS in setting up a similar service as part of their AAA system. This new framework is designed to provide remote monitoring of the behaviour, performance, and reliability of distributed XRootD services across the UK. Effort has been made to simplify ease of deployment by remote site administrators.

The result of this work is an interactive dashboard system which enables administrators to access real-time metrics on the performance of their lightweight storage systems. This monitoring framework is intended to supplement existing functionality and availability testing metrics by providing detailed information and logging from a site perspective.
Reaching new peaks for the future of the CMS HTCondor Global Pool
Authors: Pérez-Calero Yzquierdo A. , Mascheroni M. , Acosta Flechas M. , Dost J. , Haleem S. , Hurtado Anampa K. , Khan F. A. , Kizinevič E. , Peregonov N. ,   for the CMS Collaboration
Published: August 2021

The CMS experiment at CERN employs a distributed computing infrastructure to satisfy its data processing and simulation needs. The CMS Submission Infrastructure team manages a dynamic HTCondor pool, aggregating mainly Grid clusters worldwide, but also HPC, Cloud and opportunistic resources. This CMS Global Pool, which currently involves over 70 computing sites worldwide and peaks at 350k CPU cores, is employed to successfully manage the simultaneous execution of up to 150k tasks. While the present infrastructure is sufficient to harness the current computing power scales, CMS latest estimates predict a noticeable expansion in the amount of CPU that will be required in order to cope with the massive data increase of the High-Luminosity LHC (HL-LHC) era, planned to start in 2027. This contribution presents the latest results of the CMS Submission Infrastructure team in exploring and expanding the scalability reach of our Global Pool, in order to preventively detect and overcome any barriers in relation to the HL-LHC goals, while maintaining high effciency in our workload scheduling and resource utilization.
Integration of Rucio in Belle II
Authors: Serfon Cédric , Mashinistov Ruslan , De Stefano John Steven , Hernández Villanueva Michel , Ito Hironori , Kato Yuji , Laycock Paul , Miyake Hideki , Ueda Ikuo
Published: August 2021

The Belle II experiment, which started taking physics data in April 2019, will multiply the volume of data currently stored on its nearly 30 storage elements worldwide by one order of magnitude to reach about 340 PB of data (raw and Monte Carlo simulation data) by the end of operations. To tackle this massive increase and to manage the data even after the end of the data taking, it was decided to move the Distributed Data Management software from a homegrown piece of software to a widely used Data Management solution in HEP and beyond : Rucio. This contribution describes the work done to integrate Rucio with Belle II distributed computing infrastructure as well as the migration strategy that was successfully performed to ensure a smooth transition.
Grid-based minimization at scale: Feldman-Cousins corrections for light sterile neutrino search
Authors: Wospakrik Marianette , Schulz Holger , Kowalkowski Jim , Paterno Marc , Sehrish Saba , Ketchum Wesley , Ge Guanqun , Karagiorgi Georgia , Ross-Lonergan Mark
Published: August 2021

High Energy Physics (HEP) experiments generally employ sophisticated statistical methods to present results in searches of new physics. In the problem of searching for sterile neutrinos, likelihood ratio tests are applied to short-baseline neutrino oscillation experiments to construct confidence intervals for the parameters of interest. The test statistics of the form Δχ2 is often used to form the confidence intervals, however, this approach can lead to statistical inaccuracies due to the small signal rate in the region-of-interest. In this paper, we present a computational model for the computationally expensive Feldman-Cousins corrections to construct a statistically accurate confidence interval for neutrino oscillation analysis. The program performs a grid-based minimization over oscillation parameters and is written in C++. Our algorithms make use of vectorization through Eigen3, yielding a single-core speed-up of 350 compared to the original implementation, and achieve MPI data parallelism by employing DIY. We demonstrate the strong scaling of the application at High-Performance Computing (HPC) sites. We utilize HDF5 along with HighFive to write the results of the calculation to file.
Columnar data analysis with ATLAS analysis formats
Authors: Hartmann Nikolai , Elmsheuser Johannes , Duckeck Günter ,   on behalf of ATLAS Software and Computing
Published: August 2021

Future analysis of ATLAS data will involve new small-sized analysis formats to cope with the increased storage needs. The smallest of these, named DAOD_PHYSLITE, has calibrations already applied to allow fast downstream analysis and avoid the need for further analysis-specific intermediate formats. This allows for application of the “columnar analysis” paradigm where operations are applied on a per-array instead of a per-event basis. We will present methods to read the data into memory, using Uproot, and also discuss I/O aspects of columnar data and alternatives to the ROOT data format. Furthermore, we will show a representation of the event data model using the Awkward Array package and present proof of concept for a simple analysis application.
ATLAS in-file metadata and multi-threaded processing
Authors: Berghaus Frank , Krasznahorkay Attila , Martin Tim , Novak Tadej , Nowak Marcin , Schaffer A.C. , Tsulaia Vakho , van Gemmeren Peter
Published: August 2021

Processing and scientific analysis of the data taken by the ATLAS experiment requires reliable information describing the event data recorded by the detector or generated in software. ATLAS event processing applications store such descriptive metadata information in the output data files along with the event information.

To better leverage the available computing resources during LHC Run3 the ATLAS experiment has migrated its data processing and analysis software to a multi-threaded framework: AthenaMT. Therefore in-file metadata must support concurrent event processing, especially around input file boundaries. The in-file metadata handling software was originally designed for serial event processing. It grew into a rather complex system over the many years of ATLAS operation. To migrate this system to the multi-threaded environment it was necessary to adopt several pragmatic solutions, mainly because of the shortage of available person-power to work on this project in early phases of the AthenaMT development.

In order to simplify the migration, first the redundant parts of the code were cleaned up wherever possible. Next the infrastructure was improved by removing reliance on constructs that are problematic during multi-threaded processing. Finally, the remaining software infrastructure was redesigned for thread safety.
Software framework for the Super Charm-Tau factory detector project
Authors: Belozyorova Maria , Maksimov Dmitry , Razuvaev Georgiy , Sukharev Andrey , Vorobyev Vitaly , Zhadan Anastasiia , Zhadan Daniil
Published: August 2021

The project of Super Charm-Tau (SCT) factory — a high-luminosity electron-positron collider for studying charmed hadrons and tau lepton — is proposed by Budker INP. The project implies single collision point equipped with a universal particle detector. The Aurora software framework has been developed for the SCT detector. It is based on trusted and widely used in high energy physics software packages, such as Gaudi, Geant4, and ROOT. At the same time, new ideas and developments are employed, in particular the Aurora project benefits a lot from the turnkey software for future colliders (Key4HEP) initiative. This paper describes the first release of the Aurora framework, summarizes its core technologies, structure and roadmap for the near future.
Novel features and GPU performance analysis for EM particle transport in the Celeritas code
Authors: Johnson Seth R. , Tognini Stefano C. , Canal Philippe , Evans Thomas , Jun Soon Yung , Lima Guilherme , Lund Amanda , Pascuzzi Vincent R.
Published: August 2021

Celeritas is a new computational transport code designed for high-performance simulation of high-energy physics detectors. This work describes some of its current capabilities and the design choices that enable the rapid development of efficient on-device physics. The abstractions that underpin the code design facilitate low-level performance tweaks that require no changes to the higher-level physics code. We evaluate a set of independent changes that together yield an almost 40% speedup over the original GPU code for a net performance increase of 220× for a single GPU over a single CPU running 8.4M tracks on a small demonstration physics app.
Evaluation of Portable Acceleration Solutions for LArTPC Simulation Using Wire-Cell Toolkit
Authors: Yu Haiwang , Dong Zhihua , Knoepfel Kyle , Lin Meifeng , Viren Brett , Yu Kwangmin
Published: August 2021

The Liquid Argon Time Projection Chamber (LArTPC) technology plays an essential role in many current and future neutrino experiments. Accurate and fast simulation is critical to developing efficient analysis algorithms and precise physics model projections. The speed of simulation becomes more important as Deep Learning algorithms are getting more widely used in LArTPC analysis and their training requires a large simulated dataset. Heterogeneous computing is an efficient way to delegate computationally intensive tasks to specialized hardware. However, as the landscape of compute accelerators quickly evolves, it becomes increasingly difficult to manually adapt the code to the latest hardware or software environments. A solution which is portable to multiple hardware architectures without substantially compromising performance would thus be very beneficial, especially for long-term projects such as the LArTPC simulations. In search of a portable, scalable and maintainable software solution for LArTPC simulations, we have started to explore high-level portable programming frameworks that support several hardware backends. In this paper, we present our experience porting the LArTPC simulation code in the Wire-Cell Toolkit to NVIDIA GPUs, first with the CUDA programming model and then with a portable library called Kokkos. Preliminary performance results on NVIDIA V100 GPUs and multi-core CPUs are presented, followed by a discussion of the factors affiecting the performance and plans for future improvements.
Porting CMS Heterogeneous Pixel Reconstruction to Kokkos
Authors: Kortelainen Matti J. , Kwok Martin ,   (on behalf of the CMS Collaboration), Childers Taylor , Strelchenko Alexei , Wang Yunsong
Published: August 2021

Programming for a diverse set of compute accelerators in addition to the CPU is a challenge. Maintaining separate source code for each architecture would require lots of effort, and development of new algorithms would be daunting if it had to be repeated many times. Fortunately there are several portability technologies on the market such as Alpaka, Kokkos, and SYCL. These technologies aim to improve the developer’s productivity by making it possible to use the same source code for many different architectures. In this paper we use heterogeneous pixel reconstruction code from the CMS experiment at the CERNL LHC as a realistic use case of a GPU-targeting HEP reconstruction software, and report experience from prototyping a portable version of it using Kokkos. The development was done in a standalone program that attempts to model many of the complexities of a HEP data processing framework such as CMSSW. We also compare the achieved event processing throughput to the original CUDA code and a CPU version of it.
Event Classification with Multi-step Machine Learning
Authors: Saito Masahiko , Kishimoto Tomoe , Kaneta Yuya , Itoh Taichi , Umeda Yoshiaki , Tanaka Junichi , Iiyama Yutaro , Sawada Ryu , Terashi Koji
Published: August 2021

The usefulness and value of Multi-step Machine Learning (ML), where a task is organized into connected sub-tasks with known intermediate inference goals, as opposed to a single large model learned end-to-end without intermediate sub-tasks, is presented. Pre-optimized ML models are connected and better performance is obtained by re-optimizing the connected one. The selection of an ML model from several small ML model candidates for each sub-task has been performed by using the idea based on Neural Architecture Search (NAS). In this paper, Differentiable Architecture Search (DARTS) and Single Path One-Shot NAS (SPOS-NAS) are tested, where the construction of loss functions is improved to keep all ML models smoothly learning. Using DARTS and SPOS-NAS as an optimization and selection as well as the connections for multi-step machine learning systems, we find that (1) such a system can quickly and successfully select highly performant model combinations, and (2) the selected models are consistent with baseline algorithms, such as grid search, and their outputs are well controlled.
GPU simulation with Opticks: The future of optical simulations for LZ
Authors: Creaner Oisín , Blyth Simon , Eriksen Sam , Gerhardt Lisa , Monzani Maria Elena , Riffard Quentin
Published: August 2021

The LZ collaboration aims to directly detect dark matter by using a liquid xenon Time Projection Chamber (TPC). In order to probe the dark matter signal, observed signals are compared with simulations that model the detector response. The most computationally expensive aspect of these simulations is the propagation of photons in the detector’s sensitive volume. For this reason, we propose to offload photon propagation modelling to the Graphics Processing Unit (GPU), by integrating Opticks into the LZ simulations workflow. Opticks is a system which maps Geant4 geometry and photon generation steps to NVIDIA’s OptiX GPU raytracing framework. This paradigm shift could simultaneously achieve a massive speed-up and an increase in accuracy for LZ simulations. By using the technique of containerization through Shifter, we will produce a portable system to harness the NERSC supercomputing facilities, including the forthcoming Perlmutter supercomputer, and enable the GPU processing to handle different detector configurations. Prior experience with using Opticks to simulate JUNO indicates the potential for speed-up factors over 1000× for LZ, and by extension other experiments requiring photon propagation simulations.
CORSIKA 8
Authors: Alves Antonio Augusto , Reininghaus Maximilian , Schmidt André , Prechelt Remy , Ulrich Ralf ,   for the CORSIKA 8 collaboration
Published: August 2021

The CORSIKA 8 project is an international collaboration of scientists working together to deliver the most modern, flexible, robust and efficient framework for the simulation of ultra-high energy secondary particle cascades in matter. The main application is for cosmic ray air shower simulations, but it can also be applied to other problems in astro(particle)-physics, particle physics and nuclear physics. Besides a comprehensive and state-of-the-art collection of physics models as well as algorithms relevant for the field, also all possible interfaces to hardware acceleration (e.g. GPU) and parallelization (vectorization, multi-threading, multi-core) will be provided. We present the status and roadmap of this project. This code will soon be available for novel explorative studies and phenomonological research, and at the same time for massive productions runs for experiments.
Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations
Authors: Rehm Florian , Vallecorsa Sofia , Borras Kerstin , Krücker Dirk
Published: August 2021

The precise simulation of particle transport through detectors remains a key element for the successful interpretation of high energy physics results. However, Monte Carlo based simulation is extremely demanding in terms of computing resources. This challenge motivates investigations of faster, alternative approaches for replacing the standard Monte Carlo technique.

We apply Generative Adversarial Networks (GANs), a deep learning technique, to replace the calorimeter detector simulations and speeding up the simulation time by orders of magnitude. We follow a previous approach which used three-dimensional convolutional neural networks and develop new two-dimensional convolutional networks to solve the same 3D image generation problem faster. Additionally, we increased the number of parameters and the neural networks representational power, obtaining a higher accuracy. We compare our best convolutional 2D neural network architecture and evaluate it versus the previous 3D architecture and Geant4 data. Our results demonstrate a high physics accuracy and further consolidate the use of GANs for fast detector simulations.
Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation
Authors: Blue John , Kronheim Braden , Kuchera Michelle , Ramanujan Raghuram
Published: August 2021

Detector simulation in high energy physics experiments is a key yet computationally expensive step in the event simulation process. There has been much recent interest in using deep generative models as a faster alternative to the full Monte Carlo simulation process in situations in which the utmost accuracy is not necessary. In this work we investigate the use of conditional Wasserstein Generative Adversarial Networks to simulate both hadronization and the detector response to jets. Our model takes the 4-momenta of jets formed from partons post-showering and pre-hadronization as inputs and predicts the 4-momenta of the corresponding reconstructed jet. Our model is trained on fully simulated tt events using the publicly available GEANT-based simulation of the CMS Collaboration. We demonstrate that the model produces accurate conditional reconstructed jet transverse momentum (pT) distributions over a wide range of pT for the input parton jet. Our model takes only a fraction of the time necessary for conventional detector simulation methods, running on a CPU in less than a millisecond per event.
Building HEP Software with Spack: Experiences from Pilot Builds for Key4hep and Outlook for LCG Releases
Authors: Volkl Valentin , Madlener Thomas , Lin Tao , Wang Joseph , Konstantinov Dmitri , Razumov Ivan , Sailer Andre , Ganis Gerardo
Published: August 2021

Consistent, efficient software builds and deployments are a common concern for all HEP experiments. This paper describes the evolution of the usage of the Spack package manager in HEP in the context of the LCG stacks and the current Spack-based management of Key4hep software. Whereas previously Key4hep software used Spack only for a thin layer of FCC experiment software on top of the LCG releases, it is now possible to build the complete stack, from system libraries to FCC-, iLCSoft- and CEPC software packages with Spack. This pilot build doubles as a prototype for a Spack-based LCG release. The workflows and mechanisms that can be used for this purpose, potential for improvement as well as the roadmap towards a complete LCG release in spack are discussed.
Analysis of heavy-flavour particles in ALICE with the O2 analysis framework
Authors: Kučera Vít , Innocenti Gian Michele , Prino Francesco , Rossi Andrea , Grosse-Oetringhaus Jan Fiete , Zardoshti Nima , Dello Stritto Luigi , Zampolli Chiara , Catalano Fabio , Zhang Biao , Jacazio Nicolo , Faggin Mattia , Palasciano Antonio , Colamaria Fabio , Mazzilli Marianna , Thomas Deepa , Spijkers Rik , Zhu Jianhui , Zanoli Henrique
Published: August 2021

Precise measurements of heavy-flavour hadrons down to very low pT represent the core of the physics program of the upgraded ALICE experiment in Run 3 [1]. These physics probes are characterised by a very small signal-to-background ratio requiring very large statistics of minimum-bias events. In Run 3, ALICE is expected to collect up to 13 nb−1 of lead–lead collisions, corresponding to about 1 × 1011 minimum-bias events. In order to analyse this unprecedented amount of data, which is about 100 times larger than the statistics collected in Run 1 and Run 2, the ALICE collaboration is developing a complex analysis framework that aims at maximising the processing speed and data volume reduction [2]. In this paper, the strategy of reconstruction, selection, skimming, and analysis of heavy-flavour events for Run 3 will be presented. Some preliminary results on the reconstruction of charm mesons and baryons will be shown and the prospects for future developments and optimisation discussed.
Intelligent compression for synchrotron radiation source image
Authors: Fu Shiyuan , Wang Lu , Cheng Yaodong , Chen Gang
Published: August 2021

Synchrotron radiation sources (SRS) produce a huge amount of image data. This scientific data, which needs to be stored and transferred losslessly, will bring great pressure on storage and bandwidth. The SRS images have the characteristics of high frame rate and high resolution, and traditional image lossless compression methods can only save up to 30% in size. Focus on this problem, we propose a lossless compression method for SRS images based on deep learning. First, we use the difference algorithm to reduce the linear correlation within the image sequence. Then we propose a reversible truncated mapping method to reduce the range of the pixel value distribution. Thirdly, we train a deep learning model to learn the nonlinear relationship within the image sequence. Finally, we use the probability distribution predicted by the deep leaning model combined with arithmetic coding to fulfil lossless compression. Test result based on SRS images shows that our method can further decrease 20% of the data size compared to PNG, JPEG2000 and FLIF.
FELIX: the Detector Interface for the ATLAS Experiment at CERN
Authors: Paramonov Alexander
Published: August 2021

The Front-End Link eXchange (FELIX) system is an interface between the trigger and detector electronics and commodity switched networks for the ATLAS experiment at CERN. In preparation for the LHC Run 3, to start in 2022, the system is being installed to read out the new electromagnetic calorimeter, calorimeter trigger, and muon components being installed as part of the ongoing ATLAS upgrade programme. The detector and trigger electronic systems are largely custom and fully synchronous with respect to the 40.08 MHz clock of the Large Hadron Collider (LHC). The FELIX system uses FPGAs on server-hosted PCIe boards to pass data between custom data links connected to the detector and trigger electronics and host system memory over a PCIe interface then route data to network clients, such as the Software Readout Drivers (SW ROD), via a dedicated software platform running on these machines. The SW RODs build event fragments, buffer data, perform detector-specific processing and provide data for the ATLAS High Level Trigger. The FELIX approach takes advantage of modern FPGAs and commodity computing to reduce the system complexity and effort needed to support data acquisition systems in comparison to previous designs. Future upgrades of the experiment will introduce FELIX to read out all other detector components.
Design of a Resilient, High-Throughput, Persistent Storage System for the ATLAS Phase-II DAQ System
Authors: Abed Abud Adam , Bonaventura Matias , Farina Edoardo , Le Goff Fabrice
Published: August 2021

The ATLAS experiment will undergo a major upgrade to take advantage of the new conditions provided by the upgraded High-Luminosity LHC. The Trigger and Data Acquisition system (TDAQ) will record data at unprecedented rates: the detectors will be read out at 1 MHz generating around 5 TB/s of data. The Dataflow system (DF), component of TDAQ, introduces a novel design: readout data are buffered on persistent storage while the event filtering system analyses them to select 10000 events per second for a total recorded throughput of around 60 GB/s. This approach allows for decoupling the detector activity from the event selection process. New challenges then arise for DF: design and implement a distributed, reliable, persistent storage system supporting several TB/s of aggregated throughput while providing tens of PB of capacity. In this paper we first describe some of the challenges that DF is facing: data safety with persistent storage limitations, indexing of data at high-granularity in a highly-distributed system, and high-performance management of storage capacity. Then the ongoing R&D to address each of the them is presented and the performance achieved with a working prototype is shown.
Simple and Scalable Streaming: The GRETA Data Pipeline
Authors: Cromaz Mario , Dart Eli , Pouyoul Eric , Jansen Gustav R.
Published: August 2021

The Gamma Ray Energy Tracking Array (GRETA) is a state of the art gamma-ray spectrometer being built at Lawrence Berkeley National Laboratory to be first sited at the Facility for Rare Isotope Beams (FRIB) at Michigan State University. A key design requirement for the spectrometer is to perform gamma-ray tracking in near real time. To meet this requirement we have used an inline, streaming approach to signal processing in the GRETA data acquisition system, using a GPU-equipped computing cluster. The data stream will reach 480 thousand events per second at an aggregate data rate of 4 gigabytes per second at full design capacity. We have been able to simplify the architecture of the streaming system greatly by interfacing the FPGA-based detector electronics with the computing cluster using standard network technology. A set of highperformance software components to implement queuing, flow control, event processing and event building have been developed, all in a streaming environment which matches detector performance. Prototypes of all high-performance components have been completed and meet design specifications.
Daisy: Data analysis integrated software system for X-ray experiments
Authors: Hu Yu , Li Ling , Tian Haolai , Liu Zhibing , Huang Qiulan , Zhang Yi , Hu Hao , Qi Fazhi
Published: August 2021

Daisy (Data Analysis Integrated Software System) has been designed for the analysis and visualisation of X-ray experiments. To address the requirements of the Chinese radiation facilities community, spanning an extensive range from purely algorithmic problems to scientific computing infrastructure, Daisy sets up a cloud-native platform to support on-site data analysis services with fast feedback and interaction. Furthermore, the plug-in based application is convenient to process the expected high throughput data flow in parallel at next-generation facilities such as the High Energy Photon Source (HEPS). The objectives, functionality and architecture of Daisy are described in this article.
An automated tool to facilitate consistent test-driven development of trigger selections for LHCb’s Run 3
Authors: Hunter Ross , Lupton Olli , Matev Rosen , Stahl Sascha , Vesterinen Mika
Published: August 2021

Upon its restart in 2022, the LHCb experiment at the LHC will run at higher instantaneous luminosity and utilize an unprecedented full-software trigger, promising greater physics reach and efficiency. On the flip side, conforming to offline data storage constraints becomes far more challenging. Both of these considerations necessitate a set of highly optimised trigger selections. We therefore present HltEfficiencyChecker: an automated extension to the LHCb trigger application, facilitating trigger development before data-taking driven by trigger rates and efficiencies. Since the default in 2022 will be to persist only the event’s signal candidate to disk, discarding the rest of the event, we also compute efficiencies where the decision was due to the true MC signal, evaluated by matching it to the trigger candidate hit-by-hit. This matching procedure – which we validate here – demonstrates that the distinction between a “trigger” and a “trigger-on-signal” is crucial in characterising the performance of a trigger selection.
Proximeter CERN’s detecting device for personnel
Authors: Merscher Christoph , Sierra Rodrigo , Zimmaro Alessandro , Giordano Marco , Danzeca Salvatore
Published: August 2021

he SARS COV 2 virus, the cause of the better known COVID-19 disease, has greatly altered our personal and professional lives. Many people are now expected to work from home but this is not always possible and, in such cases, it is the responsibility of the employer to implement protective measures. One simple such measure is to require that people maintain a distance of 2 metres but this places responsibility on employees and leads to two problems. Firstly, the likelihood that safety distances are not maintained and secondly that someone who becomes infected does not remember with whom they may have been in contact. To address both problems, CERN has developed the “proximeter”, a device that, when worn by employees, detects when they are in close proximity to others. Information about any such close contacts is sent securely over a Low Power Wide Area Network (LPWAN) and stored in a manner that respects confidentiality and privacy requirements. In the event that an employee becomes infected with COVID-19 CERN can thus identify all the possible contacts and so prevent the spread of the virus. We describe here the details of the proximeter device, the LPWAN infrastructure deployed at CERN, the communication mechanisms and the protocols used to respect the confidentiality of personal data.
Free-running data acquisition system for the AMBER experiment
Authors: Zemko Martin , Frolov Vladimir , Huber Stefan , Jary Vladimir , Konorov Igor , Kveton Antonin , Levit Dmytro , Novy Josef , Steffen Dominik , Veit Benjamin Moritz , Virius Miroslav
Published: August 2021

Triggered data acquisition systems provide only limited possibilities of triggering methods. In our paper, we propose a novel approach that completely removes the hardware trigger and its logic. It introduces an innovative free-running mode instead, which provides unprecedented possibilities to physics experiments. We would like to present such system, which is being developed for the AMBER experiment at CERN. It is based on an intelligent data acquisition framework including FPGA modules and advanced software processing. The system provides a triggerless mode that allows more time for data filtering and implementation of more complex algorithms. Moreover, it utilises a custom data protocol optimized for needs of the free-running system. The filtering procedure takes place in a server farm playing the role of the highlevel trigger. For this purpose, we introduce a high-performance filtering framework providing optimized algorithms and load balancing to cope with excessive data rates. Furthermore, this paper also describes the filter pipeline as well as the simulation chain that is being used for production of artificial data, for testing, and validation.
End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data
Authors: Andrews Michael , Burkle Bjorn , Chaudhari Shravan , DiCroce Davide , Gleyzer Sergei , Heintz Ulrich , Narain Meenakshi , Paulini Manfred , Usai Emanuele
Published: August 2021

We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning technique combines deep learning algorithms and low-level detector representation of the high-energy collision event. In this study, we use lowlevel detector information from the simulated CMS Open Data samples to construct the top jet classifiers. To optimize classifier performance we progressively add low-level information from the CMS tracking detector, including pixel detector reconstructed hits and impact parameters, and demonstrate the value of additional tracking information even when no new spatial structures are added. Relying only on calorimeter energy deposits and reconstructed pixel detector hits, the end-to-end classifier achieves a ROC-AUC score of 0.975±0.002 for the task of classifying boosted top quark jets. After adding derived track quantities, the classifier ROC-AUC score increases to 0.9824±0.0013, serving as the first performance benchmark for these CMS Open Data samples.
THE COMPOSITION OF THE POSITIVE COLUMN OF A HELIUM GLOW DISCHARGE AT INTERMEDIATE PRESSURES
Authors: Dothan F. , Kagan Yu. M.
Published: August 2021

The concentration of atomic and molecular ions and metastables is investigated for the positive column of a helium glow discharge. Recently (1,2) the system of differential equations for the helium afterglow describing the time change of these plasma parameters was written and solved. In the stationary positive column we can neglect some processes which are importent in the afterglow. On the other side we must take into account processes of excitation and ionization by electron collisions which can be neglected in the afterglow.
TEST RESULTS OF 60-cm BORE Nb3Sn TEST MODULE COIL (TMC-I) IN THE CLUSTER TEST FACILITY
Authors: Ando T. , Shimamoto S. , Hiyama T. , Tsuji H. , Takahashi Y. , Nishi M. , Tada E. , Yoshida K. , Okuno K. , Koizumi K. , Kato T. , Nakajima H. , Takahashi O. , Oshikiri M. , Yasukochi K.
Published: August 2021

An extended test of a 60-cm-bore Nb3Sn coil (TMC-I), constructed as a development of superconducting toroidal coil in tokamak machine, has been carried out in the cluster test facility. A 192-cm-length (one turn) normal zone, nucleated by a heat-input in the innermost turn, is recovered to superconducting state at 6 kA and 10 T. For the manual dump with a decay time constant of 6.6 sec (B = 1.0 T/sec), no damage is found on the TMC-I. In addition, a out-of-plane force mode operation, using one of the cluster test coils, is done with no trouble. With these good results, the first stage in TMC-I test was completed. And as the next stage, up-grading the cluster test facility for further TMC-I test in 11 T is now going ahead.
First confirmed record of an established population of green swordtail (Xiphophorus hellerii Heckel, 1848) in Europe
Authors: Pandakov Pencho , Barzov Zhivko , Moldovanski Radoslav , Huđek Helena
Published: August 2021

The green swordtail (Xiphophorus hellerii) is a freshwater and brackish water fish native to North and Central America. It experiences wide human-traced introduction around the world, mostly due to aquarium release. Individuals of the green swordtail population from the warm spring with artificial origin located in Asparuhovo district, Bulgaria, were collected. The invasiveness screening tool for non-native freshwater fishes (FISK v2) calibrated for the Balkan region was used to assess potential invasion risk of the green swordtail to the coastal freshwater bodies of the Eastern Balkans. This study is the first confirmation on an existing self-sustained population of X. hellerii in Bulgaria and respectively in Europe. Climate change and the trend of warmer winters are prerequisites for the acclimatization of this species to the temperature out of the studied thermal spring. Therefore, the potential of the green swordtail to colonise new habitats is increasing with such facilitated acclimatization. We conclude that the green swordtail poses a high risk to become the next new invasive species in Southern Europe.
Methodology for the study of the conceptual foundations of pension legislation in the United Kingdom, France, and Russia
Authors: Aleksandrova Anna Viktorovna , Lapaeva Angelina Vyacheslavovna , Ryzhova Anastasia Andreevna , Serebryakova Elena Alekseevna
Published: August 2021

The study’s objective is to develop a methodology for studying the conceptual foundations of pension legislation in three countries: the United Kingdom, France, and Russia. The following methods were used to perform the tasks set out in the introduction: analysis and synthesis, induction and deduction, systematic, sociological, statistical, formal-legal, comparative-legal, historical, and legal methods. The result of this work was the author’s methodology for studying the conceptual foundations of pension legislation. This methodology includes the following elements: definition and justification of the subject of research, establishing its spatial and temporal framework, development and justification of the author’s periodization of the conceptual foundations of pension legislation. In addition, as part of this study, the need to apply the conceptual foundations of pension legislation such methods of knowledge as dialectical approach, general scientific methods (analysis, synthesis, systematic), private scientific methods (sociological, statistical, mathematical), and private legal methods (formal-legal, comparative-legal, historical and legal) is justified. The choice of three specific countries is since they represent different models of pension provision and different legal families. The study proves the special methodological significance of considering pension as a combination of two interrelated elements: pension insurance and budget support. The study’s novelty is due to the problem statement (the conceptual foundations of the pension legislation of the United Kingdom, France, and Russia are the first to appear as the subject of the study), as well as the interdisciplinary nature of the proposed approaches. The developed methodology can be used to study pension and other social security legislation and be extended to other countries as objects of study.
Behavioral modelling of subjects in legal cases-games with imperfect rationality
Authors: Nikitina Valentina Sergeevna
Published: August 2021

Game theory, applied in various fields, including in law, studies conflict game models and searches for their formatted solutions, makes it possible to solve many tasks and problems, predict the behaviour of subjects in various situations, thereby explaining the behavior logic of the individuals in conflict of interests. However, human behavior is not always rational (reasonable), moreover, it can be difficult to predict which strategy the player will choose when making his move in the game. In this regard, it is advisable to consider rational behavior as an accepted and reasonable behavior of game participants and irrational behavior as deviant, unpredictable behavior of players, which relies on the internal regulation of a person (beliefs, intuition, creativity, feelings, emotions, etc.) and which studies one of the developing directions of game theory in modern science – evolutionary game theory. The study aims to determine the types of irrational behavior of players, which puts other players in a difficult position and precludes from determining each other’s optimal strategies for mutual benefit and stability, using the example of legal incidents modeling through games with imperfect rationality. The authors believe, that to achieve this goal using the methods of analysis and modeling, the imperfections of rationality can be considered and studied, the types of irrational behavior of players can be defined using the games with imperfect calculation of the game, imperfect information and changes in goals during the game (with a multipersonal representation of the game) and a complete absence of rationality.