bluuebunny/crossref_metadata_2025 huggingface dataset:
A DNN for CMS track classification and selection
Authors: Giannini Leonardo
Published: May 2024

The upgrade of the track classification and selection step of the CMS tracking to a Deep Neural Network is presented. The CMS tracking follows an iterative approach: tracks are reconstructed in multiple passes starting from the ones that are easiest to find and moving to the ones with more complex characteristics (lower transverse momentum, high displacement). The track classification comes into play at the end of each iteration. A classifier using a multivariate analysis is applied after each iteration and several selection criteria are defined. If a track meets the high purity requirement, its hits are removed from the hit collection, thus simplifying the later iterations, and making the track classification an integral part of the reconstruction process. Tracks passing loose selections are also saved for physics analysis usage. The CMS experiment improved the track classification starting from a parametric selection used in Run 1, moving to a Boosted Decision Tree in Run 2, and finally to a Deep Neural Network in Run 3. An overview of the Deep Neural Network training and current performance is shown.
A multi-purpose reconstruction method based on machine learning for atmospheric neutrinos at JUNO
Authors: Duyang Hongyue , Li Teng , Liu Jiaxi , Liu Zhen , Luo Wuming , Ma Wing Yan , Tan Xiaohan , Yang Zekun , Zeng Fanrui
Published: May 2024

The Jiangmen Underground Neutrino Observatory (JUNO) experiment is designed to measure the neutrino mass ordering (NMO) using a 20-kton liquid scintillator (LS) detector. Besides the precise measurement of the reactor neutrino’s oscillation spectrum, an atmospheric neutrino oscillation measurement in JUNO offers independent sensitivity for NMO, which can potentially increase JUNO’s total sensitivity in a joint analysis. In this contribution, we present a novel multi-purpose reconstruction method for atmospheric neutrinos in JUNO at few-GeV based on a machine learning technique. This method extracts features related to event topology from PMT waveforms and uses them as inputs to machine learning models. A preliminary study based on the JUNO simulation shows good performances for event directionality reconstruction and neutrino flavor identification. This method also has a great application potential for similar LS detectors.
HEP@home: A Volunteer Computing Project to Run Fast Simulation with Delphes for CEPC
Authors: Du Ran , Shi Jingyan , Li Gang
Published: May 2024

Delphes is a C++ framework to perform a fast multipurpose detector response simulation. The Circular Electron Positron Collider (CEPC) experiment runs fast simulation with a modified Delphes based on its own scientific objectives. The CEPC fast simulation with Delphes is a High Throughput Computing (HTC) application with small input and output files. Besides, to compile and run Delphes, only ROOT software is necessary.Therefore, all these features make it appropriate to run CEPC fast simulation as a Volunteer Computing application. As a result, a BOINC project named HEP@home is developed to run fast simulation with Delphes for CEPC. This paper describes the internal structure of the project, pre and post data operations, and its development status.
Analysis Tools in Geant4
Authors: Hřivnáčová Ivana , Barrand Guy
Published: May 2024

The analysis category was introduced in Geant4 more than ten years ago (in 2011) with the aim to provide users with a lightweight analysis tool, available as part of the Geant4 installation without the need to link to an external analysis package. It helps capture statistical data in the form of histograms and n-tuples and store these in files in four various formats. It was already presented at CHEP multiple times, the last time five years ago. In this article we give an update on its evolution since then.

We will report on new functionalities: the connection of the analysis to visualization, flexibility in the selection of the output files and also saving data in multiple formats from the same simulation run, and new support for data object cycles in the latest version Geant4 11.1.

We will then present the evolution of its design including the major update in the past two years that allowed the introduction of a new Generic analysis manager. In particular, we will discuss the advantages of our design choice based on the so-called Non Virtual Interface pattern: the code robustness and stability in the context of the code evolution over more than ten years.

Finally, we will present the continuous code improvements using static code analysis and sanitizer tools.
Development of particle flow algorithms based on Neural Network techniques for the IDEA calorimeter at future colliders
Authors: D’Onofrio Adelina , Di Micco Biagio , Biglietti Michela
Published: May 2024

IDEA (Innovative Detector for an Electron-positron Accelerator) is an innovative general-purpose detector concept, designed to study electron-positron collisions at future e+e− circular colliders. The detector will be equipped with a dual read-out calorimeter able to measure separately the hadronic component and the electromagnetic component of the showers initiated by the impinging hadrons.

Particle flow algorithms (PFAs) have become the paradigm of detector design for the high energy frontier and this work focuses on a project to build a particle flow algorithm for the IDEA detector using Machine Learning (ML) techniques. ML is used for particle reconstruction and identification profiting of the high granularity of the fiber-based dual-readout calorimeter. Neural Networks (NN) are built for electron reconstruction inside the calorimeter. The performance of several NN architectures is shown, with particular attention to the layer setup and the activation function choices. The performance is evaluated on the energy resolution function of the reconstructed particles. The algorithm is trained using both parallel CPUs and GPU, and the time performance and the memory usage of the two approaches are systematically compared.
EvtGen — recent developments and prospects
Authors: Abudinén Fernando , Back John , Kreps Michal , Latham Thomas
Published: May 2024

EvtGen is an event generator specialised for decays of heavy hadrons. Since its early development in the 90’s, the generator has been extensively used and has become an essential tool for heavy-flavour physics analyses. Throughout this time, its source code has remained mostly unchanged, except for additions of new decay models. In view of the upcoming boom of multi-threaded processing, we have launched a modernisation campaign with the chief goal of making EvtGen thread safe. We report on the challenges encountered in this endeavour and the milestones reached so far.
Kiwaku, a C++20 library for multidimensional arrays. Application to ACTS tracking
Authors: Joube Sylvain , Grasland Hadrien , Chamont David , Falcou Joël
Published: May 2024

C++ is a vital part of particle physics as it allows for high level abstractions while offering state of the art performance. In this article we will first introduce the C++ 20 concepts, a major C++ usability enhancement. We will then introduce Kiwaku, a new multidimensional arrays library taking advantage of the most advanced C++ features at the time of writing, providing the user with intuitive API while retaining state of the art performance. Multidimensional arrays are a basic building block for many scientific experiments and simulations, and particle physics is no exception. Using examples borrowed from Covfie and ACTS libraries, and using data from the ATLAS CERN experiment, we will show how Kiwaku offers good usability while having a negligible impact on performance compared to using classic C++ std::arrays.
Optimizing Geant4 Hadronic Models
Authors: Genser Krzysztof , Jun Soon Yung , Ribon Alberto , Uzhinsky Vladimir , Yarba Julia
Published: May 2024

Geant4, the leading detector simulation toolkit used in high energy physics, employs a set of physics models to simulate interactions of particles with matter across a wide range of energies. These models, especially the hadronic ones, rely largely on directly measured cross-sections and inclusive characteristics, and use physically motivated parameters. However, they generally aim to cover a broad range of possible simulation tasks and may not always be optimized for a particular process or a given material. The Geant4 collaboration recently made many parameters of the models accessible via a configuration interface. This opens a possibility to fit simulated distributions to the thin target experimental datasets and extract optimal values of the model parameters and the associated uncertainties. Such efforts are currently undertaken by the Geant4 collaboration with the goal of offering alternative sets of model parameters, also known as "tunes", for certain applications. The effort should subsequently lead to more accurate estimates of the systematic errors in physics measurements given the detector simulation role in performing the physics measurements. Results of the study are presented to illustrate how Geant4 model parameters can be optimized through applying fitting techniques, to improve the agreement between the Geant4 and the experimental data.
Recent Developments in the FullSimLight Simulation Tool from ATLAS
Authors: Khan Raees , Bandieramonte Marilena , Boudreau Joseph , Bianchi Riccardo Maria , Dell’Acqua Andrea , Kleklots Denys , Tsulaia Vakhtang
Published: May 2024

FullSimLight is a lightweight, Geant4-based command line simulation utility intended for studies of simulation performance. It is part of the GeoModel toolkit (geomodel.web.cern.ch) which has been stable for more than one year. The FullSimLight component has recently undergone renewed development aimed at extending its functionality. It has been endowed with a GUI for fast, transparent, and foolproof configuration and with a plugin mechanism allowing users and developers with diverse goals to extend and customize the simulation. Geometry and event input can be easily specified on the fly, allowing rapid evaluation of different geometry options and their effect on simulation performance. User actions and sensitive detectors can also be loaded through the new plugin mechanism, allowing for customization of Geant4 processing and hit production. The geometry explorer (gmex), in a parallel development, has been enhanced with the capability of visualizing FullSimLight track and hit output. FullSimLight, brought to you by the ATLAS collaboration at the LHC, is an experiment independent software tool.
Design and implementation of experimental data access security policy for HEPS container computing platform
Authors: Hu Qingbao , Xu Jiping , Cheng Yaosong , Luo Qi , Fu Shiyuan
Published: May 2024

China’s High-Energy Photon Source (HEPS), the first national highenergy synchrotron radiation light source, is under design and construction. In the future, at the first stage of HEPS, it is predicted that 24PB raw experimental data will be produced per month from 14 beamlines. Faced with such a huge scale of scientific data and diverse data analysis environments in light source disciplines, the HEPS scientific computing platform was designed and implemented based on container mirroring and dynamic orchestration technology to provide HEPS users with a data analysis environment. In this article, a data security access strategy is designed and evaluated for a scientific computing platform to ensure the security and efficiency of data access for users in the entire process of data analysis. First, the general situation of HEPS is introduced. Second, the challenges faced by the HEPS scientific computing system. Third, the architecture and service process of the scientific computing platform are described from the perspective of IT, some key technical implementations will be introduced in detail. Finally, the application effect of data access security policies on computing platforms will be demonstrated.
Dynamic scheduling using CPU oversubscription in the ALICE Grid
Authors: Bertran Ferrer Marta , Grigoras Costin , Badia Rosa M.
Published: May 2024

The ALICE Grid is designed to perform a realtime comprehensive monitoring of both jobs and execution nodes in order to maintain a continuous and consistent status of the Grid infrastructure. An extensive database of historical data is available and is periodically analyzed to tune the workflows and data management to optimal performance levels. This data, when evaluated in real time, has the power to trigger decisions for efficient resource management of the currently running payloads, for example to enable the execution of a higher volume of work per unit of time. In this article, we consider scenarios in which, through constant interaction with the monitoring agents, a dynamic adaptation of the running workflows is performed. The target resources are memory and CPU with the objective of using them in their entirety and ensuring optimal utilization fairness between executing jobs.

Grid resources are heterogeneous and of different generations, which means that some of them have better hardware characteristics than the minimum required to execute ALICE jobs. Our middleware, JAliEn, works on the basis of having at least 2 GB of RAM allocated per core (allowing up to 8 GB of virtual memory when including swap). Many of the worker nodes have higher memory per core ratios than these basic limits and in terms of available memory they therefore have free resources to accommodate extra jobs. The running jobs may have different behaviors and unequal resource usages depending on their nature. For example, analysis tasks are I/O bound while Monte-Carlo tasks are CPU intensive. Running additional jobs with complementary resource usage patterns on a worker node has a great potential to increase its total efficiency. This paper presents the methodology to exploit the different resource usage profiles by oversubscribing the worker nodes with extra jobs taking into account their CPU resource usage levels and memory capacity.
JUNO distributed computing system
Authors: Zhang Xiaomei
Published: May 2024

The Jiangmen Underground Neutrino Observatory (JUNO) [1] is a multipurpose neutrino experiment and the determination of the neutrino mass hierarchy is its primary physics goal. JUNO is going to start data taking in 2024 and plans to use distributed computing infrastructure for the data processing and analysis tasks. The JUNO distributed computing system has been designed and built based on DIRAC [2]. Since last year, the official Monte Carlo (MC) production has been running on the system, and petabytes of massive MC data have been shared among JUNO data centers through this system. In this paper, an overview of the JUNO distributed computing system will be presented, including workload management system, data management, and condition data access system. Moreover, the progress of adapting the system to support token-based AAI [3] and HTTP-TPC [4] will be reported. Finally, the paper will mention the preparations for the upcoming JUNO data-taking.
ATLAS Distributed Computing Evolution: Developments and Demonstrators Towards HL–LHC
Authors: Cameron David , Lassnig Mario , South David
Published: May 2024

The computing challenges at the HL–LHC require fundamental changes to the distributed computing models that have served experiments well throughout LHC. ATLAS planning for HL–LHC computing started back in 2020 with a Conceptual Design Report outlining various challenges to explore. This was followed in 2022 by a roadmap defining concrete milestones and associated effort required. Today, ATLAS is proceeding further with a set of "demonstrators" with focused R&D in specific topics described in the roadmap. The demonstrators cover areas such as optimised tape writing and access, data recreation on–demand and the use of commercial clouds.
The Platform-as-a-Service paradigm meets ATLAS: Developing an automated analysis workflow on the newly established INFN CLOUD
Authors: Marcon Caterina , Carminati Leonardo , Rebatto David , Turra Ruggero
Published: May 2024

The Worldwide LHC Computing Grid (WLCG) is a large-scale collaboration which gathers computing resources from more than 170 computing centers worldwide. To fulfill the requirements of new applications and to improve the long-term sustainability of the grid middleware, newly available solutions are being investigated. Like open-source and commercial players, the HEP community has also recognized the benefits of integrating cloud technologies into the legacy, grid-based workflows.

Since March 2021, INFN has entered the field of cloud computing establishing the INFN CLOUD infrastructure. This platform supports scientific computing, software development and training, and serves as an extension of local resources. Among available services, virtual machines, Docker-based deployments, HTCondor (deployed on Kubernetes) or general-purpose Kubernetes clusters can be deployed.

An ongoing R&D activity within the ATLAS experiment has the long-term objective to define an operation model which is efficient, versatile and scalable in terms of costs and computing power. As a part of this larger effort, this study investigates the feasibility of an automated, cloud-based data analysis workflow for the ATLAS experiment using INFN CLOUD resources. The scope of this research has been defined in a new INFN R&D project: the INfn Cloud based Atlas aNalysis faciliTy, or INCANT.

The long-term objective of INCANT is to provide a cloud-based system to support data preparation and data analysis. As a first project milestone, a proofof-concept has been developed. A Kubernetes cluster equipped with 7 nodes (total 28 vCPU, 56 GB of RAM and 700 GB of non-shared block storage) hosts an HTCondor cluster, federated with INFN’s IAM authentication platform, running in specialized Kubernetes pods. HTCondor worker nodes have direct access to CVMFS and EOS (via XRootD) for provisioning software and data, respectively. They are also connected to a NFS shared drive which can optionally be backed by an S3-compatible 2 TB storage. Jobs are submitted to the HTCondor cluster from a satellite, Dockerized submit node which is also federated with INFN’s IAM and connected to the same data and software resources. This proof-of-concept is being tested with actual analysis workflows.
Darshan for HEP applications
Authors: Wang Rui , Snyder Shane , Benjamin Douglas , Dong Zhihua , Gartung Patrick , Herner Kenneth
Published: May 2024

Modern HEP workflows must manage increasingly large and complex data collections. HPC facilities may be employed to help meet these workflows’ growing data processing needs. However, a better understanding of the I/O patterns and underlying bottlenecks of these workflows is necessary to meet the performance expectations of HPC systems.

Darshan is a lightweight I/O characterization tool that captures concise views of HPC application I/O behavior. It intercepts application I/O calls at runtime, records file access statistics for each process, and generates log files detailing application I/O access patterns.

Typical HEP workflows include event generation, detector simulation, event reconstruction, and subsequent analysis stages. A study of the I/O behavior of the ATLAS simulation and filtering stage, and the CMS simulation workflow using Darshan is presented, including insights into the I/O operations and data access size.
Geant4 Monte-Carlo (GEMC) A database-driven simulation program
Authors: Ungaro Maurizio
Published: May 2024

GEMC[1] is an application that harnesses the power of databases to execute Geant4 Monte-Carlo simulations.

The databases (MYSQL, CSQL, TEXT) define the geometry, materials, digitization algorithms, readout electronics and output formats.

Implemented in C++, GEMC also boasts a user-friendly Python API that facilitates detector construction and database population.

GEMC can handle real-life scenarios such as geometry variations and the run number-dependent calibration constants and digitization parameters.

This abstract provides an overview of GEMC, accompanied by examples that showcase its versatility. We delve into the practical application of GEMC within the the CLAS12 experimental program at Jefferson Lab.
Glance Search Library
Authors: Souza e Silva Gabriel José , Ferreira Brito Filho Carlos Henrique , Corti Gloria , Closier Joel
Published: May 2024

The LHCb experiment is one of the 4 large LHC experiments at CERN. With more than 1500 members and tens of thousands of assets, the Collaboration requires systems that allow the extraction of data from many databases according to some very specific criteria. In LHCb there are 4 production web applications responsible for managing members and institutes, tracking assets and their current status, presenting radiological information about the cavern, and supporting the management of cables. A common requirement shared across all these systems is to allow searching information based on logical expressions. Therefore, in order to avoid rework, the Glance Search Library was created with the goal of providing components for applications to deploy frontend search interfaces capable of generating standardized queries based on users’ input, and backend utility functions that compile such queries into a SQL clause. The Glance Search Library is split into 2 smaller libraries maintained in different GitLab repositories. The first one only contains Vue components and JavaScript modules and, in LHCb, it is included as a dependency of the SPAs (Single Page Applications). The second is a PHP Object-Oriented library, mainly used by REST APIs that are required to expose large amounts of data stored in their relational databases. This separation provides greater flexibility and more agile deployments. It also enables lighter applications with no graphical interface to build command line tools solely on top of the backend classes and predefined queries.
Is Julia ready to be adopted by HEP?
Authors: Gál Tamás , Gras Philippe , Hegner Benedikt , Hernandez Acosta Uwe , Kluth Stefan , Ling Jerry , Mato Pere , Moreno Alexander , Pivarski Jim , Schulz Oliver , Stewart Graeme , Strube Jan , Vasilev Vasil
Published: May 2024

The Julia programming language was created 10 years ago and is now a mature and stable language with a large ecosystem including more than 8,000 third-party packages. It was designed for scientific programming to be a high-level and dynamic language as Python is, while achieving runtime performances comparable to C/C++ or even faster. With this, we ask ourselves if the Julia language and its ecosystem is ready now for its adoption by the High Energy Physics community. We will report on a number of investigations and studies of the Julia language that have been done for various representative HEP applications, ranging from computing intensive initial data processing of experimental data and simulation, to final interactive data analysis and plotting. Aspects of collaborative code development of large software within a HEP experiment has also been investigated: scalability with large development teams, continuous integration and code test, code reuse, language interoperability to enable an adiabatic migration of packages and tools, software installation and distribution, training of the community, benefit from development from industry and academia from other fields.
Key4hep: Progress Report on Integrations
Authors: Brondolin Erica , Carceller Juan Miguel , Deconinck Wouter , Fang Wenxing , Francois Brieuc , Gaede Frank-Dieter , Ganis Gerardo , Hegner Benedikt , Helsens Clement , Huang Xingtao , Joosten Sylvester , Ko Sang Hyun , Lin Tao , Li Teng , Li Weidong , Madlener Thomas , Reichenbach Leonhard , Sailer André , Sasikumar Swathi , Smiesko Juraj , Stewart Graeme A. , Tolosa-Delgado Alvaro , Volkl Valentin , Zhang Xiaomei , Zou Jiaheng
Published: May 2024

Detector studies for future experiments rely on advanced software tools to estimate performance and optimize their design and technology choices. The Key4hep project provides a flexible turnkey solution for the full experiment life-cycle based on established community tools such as ROOT, Geant4, DD4hep, Gaudi, podio and spack. Members of the CEPC, CLIC, EIC, FCC, and ILC communities have joined to develop this framework and have merged, or are in the progress of merging, their respective software environments into the Key4hep stack.

These proceedings will give an overview over the recent progress in the Key4hep project: covering the developments towards adaptation of state-of-theart tools for simulation (DD4hep, Gaussino), track and calorimeter reconstruction (ACTS, CLUE), particle flow (PandoraPFA), analysis via RDataFrame, and visualization with Phoenix, as well as tools for testing and validation.
Managing software build infrastructure at ALICE using Hashicorp Nomad
Authors: Wilken Timo , Eulisse Giulio
Published: May 2024

The ALICE experiment at CERN uses a cluster consisting of virtual and bare-metal machines to build and test proposed changes to the ALICE Online–Offline (O2) software in addition to building and publishing regular software releases.

Nomad is a free and open-source job scheduler for containerised and noncontainerised applications developed by Hashicorp. It is integrated into an ecosystem of related software, including Consul and Vault, providing a consistent interface to orchestration, monitoring and secret storage. At ALICE, it recently replaced Apache Mesos, Aurora and Marathon as the primary tool for managing our computing resources.

First, we will describe the architecture of the build cluster at the ALICE experiment. After giving an overview of the advantages that Nomad gives us in managing our computing workload, and our reasons for switching away from the Mesos software stack, we will present concrete examples of improvements in monitoring and automatic configuration of web services that we are already benefiting from. Finally, we will discuss where we see opportunities for future work in integrating the ALICE build infrastructure more deeply with Nomad, in order to take advantage of its larger feature set compared to Mesos.
Binning high-dimensional classifier output for HEP analyses through a clustering algorithm
Authors: Diekmann Svenja , Eich Niclas , Erdmann Martin
Published: May 2024

The usage of Deep Neural Networks (DNNs) as multi-classifiers is widespread in modern HEP analyses. In standard categorisation methods, the high-dimensional output of the DNN is often reduced to a one-dimensional distribution by exclusively passing the information about the highest class score to the statistical inference method. Correlations to other classes are hereby omitted. Moreover, in common statistical inference tools, the classification values need to be binned, which relies on the researcher’s expertise and is often nontrivial. To overcome the challenge of binning multiple dimensions and preserving the correlations of the event-related classification information, we perform K-means clustering on the high-dimensional DNN output to create bins without marginalising any axes. We evaluate our method in the context of a simulated cross section measurement at the CMS experiment, showing an increased expected sensitivity over the standard binning approach.
Boosting RDataFrame performance with transparent bulk event processing
Authors: Guiraud Enrico , Blomer Jakob , Canal Philippe , Naumann Axel
Published: May 2024

RDataFrame is ROOT’s high-level interface for Python and C++ data analysis. Since it first became available, RDataFrame adoption has grown steadily and it is now poised to be a major component of analysis software pipelines for LHC Run 3 and beyond. Thanks to its design inspired by declarative programming principles, RDataFrame enables the development of highperformance, highly parallel analyses without requiring expert knowledge of multi-threading and I/O: user logic is expressed in terms of self-contained, small computation kernels tied together by a high-level API. This design completely decouples analysis logic from its actual execution, and opens several interesting avenues for workflow optimization. In particular, in this work we explore the benefits of moving internal data processing from an event-by-event to a bulkby-bulk loop. This refactoring dramatically reduces the framework’s runtime overheads; in collaboration with the I/O layer it improves data access patterns; it exposes information that optimizing compilers might use to auto-vectorize the invocation of user-defined computations; finally, while existing user-facing interfaces remain unaffected, it becomes possible to additionally offer interfaces that explicitly expose bulks of events, useful e.g. for the injection of GPU kernels into the analysis workflow. In order to inform similar future R&D, design challenges will be presented, as well as an investigation of the relevant timememory trade-off backed by novel performance benchmarks.
Efficient Parallelization of RooFit Computations for Accelerated Higgs Combination Fits
Authors: Wolffs Zef , Bos Patrick , Brenner Lydia , Verkerke Wouter , van Vulpen Ivo
Published: May 2024

In the context of High Energy Physics (HEP) analyses the advent of large-scale combination fits forms an increasing computational challenge for the underlying software frameworks on which these fits rely. RooFit, being the central tool for HEP statistical model creation and fitting, intends to address this challenge through an efficient and versatile parallelisation framework on top of which two parallel implementations were developed in the present research. The first implementation, the parallelisation of the gradient, shows good scaling behaviour and is sufficiently robust to consistently minimize real large-scale fits. The latter, the parallelisation of the line search, is still work in progress for some specific likelihood components but shows promising results in realistic testcases. Enabling just gradient parallelisation speeds up the full fit of a recently published Higgs combination from the ATLAS experiment by a factor of 4.6 with sixteen workers. As the improvements presented in this research are currently publicly available in ROOT 6.28, we invite users to enable at least gradient parallelisation for robust accelerated fitting with RooFit.
High-performance end-user analysis in pure Julia programming language
Authors: Ling Jerry , Gál Tamás
Published: May 2024

We present tools for high-performance analysis written in pure Julia, a just-in-time (JIT) compiled dynamic programming language with a high-level syntax and performance. The packages we present center around UnROOT.jl, a pure Julia ROOT file I/O package that is optimized for speed, lazy reading, flexibility and thread safety.

We discuss what affects performance in Julia, the challenges, and their solutions during the development of UnROOT.jl. We highlight type stability as a challenge and discuss its implication whenever any “compilation” happens (incl. Numba, Jax, C++) as well as Julia’s specific ones.

We demonstrate the performance and “easy to use” claim by comparing UnROOT.jl against popular alternatives (RDataFrame, Uproot, etc.) in medium-size realistic benchmarks, comparing both performance and code complexity.

Finally, we also showcase real ATLAS analysis workflows both locally and on an HPC system, highlighting the composability of UnROOT.jl with multithread/process and out-of-core distributed computing libraries.
Analyzing, Identifying & Alerting on Network Issues
Authors: Vasileva Petya , Babik Marian , McKee Shawn , Vukotic Ilija
Published: May 2024

The Worldwide LHC Computing Grid (WLCG) relies on the network as a critical part of its infrastructure and therefore needs to guarantee effective network usage and prompt detection and resolution of any network issues, including connection failures, congestion, and traffic routing. In this paper, we will describe our ongoing work to proactively analyze, correlate and alert on various network and infrastructure issues. We will discuss the methods and techniques applied, the systems developed, and the challenges with the measurements that make it difficult to easily identify problems or assign those problems to the appropriate location(s).
CPU Performance Study for HEP Workloads with Respect to the Number of Single-core Slots
Authors: Schnepf Matthias J. , Fischer Max , Petzold Andreas
Published: May 2024

Many common CPU architectures provide simultaneous multithreading (SMT). The operating system sees multiple logical CPU cores per physical CPU core and can schedule several processes to one physical CPU core. This overbooking of physical cores enables a better usage of parallel pipelines and doubled components within a CPU core. On systems with several applications running in parallel, such as batch jobs on worker nodes, the usage of SMT can increase the overall performance. In high energy physics (HEP) batch/Grid jobs are accounted for in units of single-core jobs. One single-core job is designed to utilize one logical CPU core fully. As a result, Grid sites often configure their worker nodes to provide as many single-core slots as physical or logical CPU cores. However, due to memory and disk space constraints, not all logical CPU cores can be used. Therefore, it can be useful to configure more single-core slots than physical CPU cores but fewer than logical CPU cores per worker node. We have extensively used and studied this strategy at the GridKa Tier 1 center. In this contribution, we show benchmark results for different overbooking factors of physical cores on various CPU models for different HEP workflows and Benchmarks.
I/O performance studies of analysis workloads on production and dedicated resources at CERN
Authors: Sciabà Andrea , Blomer Jakob , Canal Philippe , Duellmann Dirk , Guiraud Enrico , Naumann Axel , Padulano Vincenzo Eduardo , Panzer-Steindel Bernd , Peters Andreas , Schulz Markus , Smith David
Published: May 2024

The recent evolutions of the analysis frameworks and physics data formats of the LHC experiments provide the opportunity of using central analysis facilities with a strong focus on interactivity and short turnaround times, to complement the more common distributed analysis on the Grid. In order to plan for such facilities, it is essential to know in detail the performance of the combination of a given analysis framework, of a specific analysis and of the installed computing and storage resources. This contribution describes performance studies performed at CERN, using the EOS disk-based storage, either directly or through an XCache instance, from both batch resources and highperformance compute nodes which could be used to build an analysis facility. A variety of benchmarks, both synthetic and based on real-world physics analyses and their corresponding input datasets, are utilized. In particular, the RNTuple format from the ROOT project is put to the test and compared to the latest version of the TTree format, and the impact of caches is assessed. In addition, we assessed the difference in performance between the use of storage system specific protocols, like XRootd, and FUSE. The results of this study are intended to be a valuable input in the design of analysis facilities, at CERN and elsewhere.
Modular toolsets for integrating HPC clusters in experiment control systems
Authors: Manafov Anar , Rybalchenko Alexey , Klein Dennis , Al-Turany Mohammad
Published: May 2024

New particle/nuclear physics experiments require a massive amount of computing power that is only achieved by using high performance clusters directly connected to the data acquisition systems and integrated into the online systems of the experiments. However, integrating an HPC cluster into the online system of an experiment means: Managing and synchronizing thousands of processes that handle the huge throughput.

In this work, modular components that can be used to build and integrate such a HPC cluster in the experiment control systems (ECS) will be introduced.

The Online Device Control library (ODC) [1] in combination with the Dynamic Deployment System (DDS) [2, 3] and FairMQ [4] message queuing library offers a sustainable solution for integrating HPC cluster controls into an ECS. DDS as part of the ALFA framework [5] is a toolset that automates and significantly simplifies a dynamic deployment of user-defined processes and their dependencies on any resource management system (RMS) using a given process graph (topology).

ODC, in this architecture, is the tool to control and communicate with a topology of FairMQ processes using DDS. ODC is designed to act as a broker between a high level experiment control system and a low level task management system e.g.: DDS.

In this work the architecture of both DDS and ODC will be discussed, as well as the design decisions taken based on the experience gained of using these tools in production by the ALICE experiment at CERN to deploy and control thousands of processes (tasks) on the Event Processing Nodes cluster (EPN) during Run3 as a part of the ALICE O2 software ecosystem [6].
Next generation Linux Applications Gateway for CERN accelerator control systems
Authors: Rey Regúlez Mario , Oulevey Thomas
Published: May 2024

CERN has been providing central Windows remote desktops via the Windows Terminal Infrastructure service for several years and aims to provide a similar experience for Linux graphical environments. Different communities and experiments offer a series of tools to their users with this goal in mind, but the solutions are far from ideal and generate a support overhead for their respective providers. The Linux Applications Gateway project (LAG) was born to provide this functionality centrally from the IT department. After an extensive market research, the tool FastX was identified as an enabler, and to set up a closed, internal pilot for evaluation. These efforts led to the creation of the Remote Operations Gateway (ROG) service with a high approval rate. We aim to further extend the usage of FastX at CERN, reaching out to other communities and experiments, and to provide a better support coverage for them all.
Preparing for a new Data Center: Automated Management of a 10’000 node bare-metal fleet in CERN IT
Authors: Savchenko Maryna , Atzori Luca , Papakyprianou Nikos , Piszczek Michał , Wiebalck Arne
Published: May 2024

CERN IT has consolidated all life-cycle management of its physical server fleet on the Ironic bare-metal API. From the initial registration upon the first boot, over the inventory checking, the burn-in and the benchmarking for acceptance, the provisioning to the end users and the repairs during its service, up to the retirement at the end of the servers’ life, all stages can be managed within this framework. This article will walk you through the server’s life in the CERN data center, and explain how this enables us to handle a fleet of almost 10,000 nodes in an automated and efficient way and to prepare for the new data center which is currently being built. Eventually, we will round up things with the top challenges we faced when moving to this system, like the transparent adoption of already in-production nodes, after-the-fact inventory updates, or scaling issues.
Software Citation in HEP: Current State and Recommendations for the Future
Authors: Feickert Matthew , Katz Daniel S. , Neubauer Mark S. , Sexton-Kennedy Elizabeth , Stewart Graeme A.
Published: May 2024

In November 2022, the HEP Software Foundation and the Institute for Research and Innovation for Software in High-Energy Physics organized a workshop on the topic of Software Citation and Recognition in HEP. The goal of the workshop was to bring together different types of stakeholders whose roles relate to software citation, and the associated credit it provides, in order to engage the community in a discussion on: the ways HEP experiments handle citation of software, recognition for software efforts that enable physics results disseminated to the public, and how the scholarly publishing ecosystem supports these activities. Reports were given from the publication board leadership of the ATLAS, CMS, and LHCb experiments and HEP open source software community organizations (ROOT, Scikit-HEP, MCnet), and perspectives were given from publishers (Elsevier, JOSS) and related tool providers (INSPIRE, Zenodo). This paper summarizes key findings and recommendations from the workshop as presented at the 26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023).
A method for inferring signal strength modifiers by conditional invertible neural networks
Authors: Farkas Máté Zoltán , Diekmann Svenja , Eich Niclas , Erdmann Martin
Published: May 2024

The continuous growth in model complexity in high-energy physics (HEP) collider experiments demands increasingly time-consuming model fits. We show first results on the application of conditional invertible networks (cINNs) to this challenge. Specifically, we construct and train a cINN to learn the mapping from signal strength modifiers to observables and its inverse. The resulting network infers the posterior distribution of the signal strength modifiers rapidly and for low computational cost. We present performance indicators of such a setup including the treatment of systematic uncertainties. Additionally, we highlight the features of cINNs estimating the signal strength for a vector boson associated Higgs production analysis of simulated samples of events, which include a simulation of the CMS detector.
BESIII track reconstruction algorithm based on machine learning
Authors: Jia Xiaoqian , Qin Xiaoshuai , Li Teng , Huang Xingtao , Zhang Xueyao , Yin Na , Zhang Yao , Yuan Ye
Published: May 2024

Track reconstruction is one of the most important and challenging tasks in the offline data processing of collider experiments. For the BESIII detector working in the tau-charm energy region, plenty of efforts were made previously to improve the tracking performance with traditional methods, such as template matching and Hough transform etc. However, for difficult tracking tasks, such as the tracking of low momentum tracks, tracks from secondary vertices and tracks with high noise level, there is still large room for improvement. In this contribution, we demonstrate a novel tracking algorithm based on machine learning method. In this method, a hit pattern map representing the connectivity between drift cells is established using an enormous MC sample, based on which we design an optimal method of graph construction, then an edgeclassifying Graph Neural Network is trained to distinguish the hit-on-track from noise hits. Finally, a clustering method based on DBSCAN and RANSAC is developed to cluster hits from multiple tracks. Track fitting algorithm based on GENFIT2 is also studied to obtain the track parameters, where deterministic annealing filter are implemented to deal with ambiguities and potential noises. The preliminary results on BESIII MC sample presents promising performance, showing potential to apply this method to other trackers based on drift chamber as well, such as the CEPC and STCF detectors under pre-study.
Particle identification with machine learning in ALICE Run 3
Authors: Karwowska Maja , Jakubowska Monika , Graczykowski Łukasz , Deja Kamil , Kasak Miłosz
Published: May 2024

The main focus of the ALICE experiment, quark–gluon plasma measurements, requires accurate particle identification (PID). The ALICE subdetectors allow identifying particles over a broad momentum interval ranging from about 100 MeV/c up to 20 GeV/c. However, a machine learning (ML) model can explore more detector information. During LHC Run 2, preliminary studies with Random Forests obtained much higher efficiencies and purities for selected particles than standard techniques.

For Run 3, we investigate Domain Adaptation Neural Networks that account for the discrepancies between the Monte Carlo simulations and the experimental data. Preliminary studies show that domain adaptation improves particle classification. Moreover, the solution is extended with Feature Set Embedding and attention to give the network more flexibility to train on data with various sets of detector signals. PID ML is already integrated with the ALICE Run 3 Analysis Framework. Preliminary results for the PID of selected particle species, including real-world analyzes, are discussed as well as the possible optimizations.
Deployment of ML in Changing Environments
Authors: Barbone Marco , Brown Christopher , Radburn-Smith Benjamin , Tapper Alexander
Published: May 2024

The High-Luminosity LHC upgrade of the CMS experiment will utilise a large number of Machine Learning (ML) based algorithms in its hardware-based trigger. These ML algorithms will facilitate the selection of potentially interesting events for storage and offline analysis. Strict latency and resource requirements limit the size and complexity of these models due to their use in a high-speed trigger setting and deployment on FPGA hardware. It is envisaged that these ML models will be trained on large, carefully tuned, Monte Carlo datasets and subsequently deployed in a real-world detector environment. Not only is there a potentially large difference between the MC training data and real-world conditions but these detector conditions could change over time leading to a shift in model output which could degrade trigger performance. The studies presented explore different techniques to reduce the impact of this effect, using the CMS track finding and vertex trigger algorithms as a test case. The studies compare a baseline retraining and redeployment of the model and episodic training of a model as new data arrives in a continual learning context. The results show that a continually learning algorithm outperforms a simple retrained model when degradation in detector performance is applied to the training data and is a viable option for maintaining performance in an evolving environment such as the High-Luminosity LHC.
Charged Track Reconstruction with Artificial Intelligence for CLAS12
Authors: Gavalian Gagik , Thomadakis Polykarpos , Angelopoulos Angelos , Chrisochoides Nikos
Published: May 2024

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run at higher luminosity without degradation of the data quality. This in turn will lead to significant benefits for the CLAS12 physics program.
CaTS: Integration of Geant4 and Opticks
Authors: Wenzel Hans , Yung Jun Soon , Genser Krzysztof , De Figueiredo Felipe
Published: May 2024

CaTS [6]is an advanced example that is part of Geant4 since version 11.0. It demonstrates the use of Opticks to offload the simulation of optical photons to GPUs. Opticks interfaces with the Geant4 toolkit to collect all the necessary information to generate and trace optical photons, re-implements the optical physics processes to be run on the GPU, and automatically translates the Geant4 geometry into a GPU appropriate format. To trace the photons, Opticks uses NVIDIA OptiX®. In this report, we describe CaTS and the integration of Opticks with Geant4. We demonstrate that the generation and tracing of optical photons represents an ideal application to be offloaded to GPUs, fully utilizing the high degree of available parallelism. In a typical liquid argon TPC simulation, a speedup of several hundred times is observed compared to an equivalent simulation using single threaded Geant4.