bluuebunny/crossref_metadata_2025_split huggingface dataset:
Blaze: A High performance Big Data Computing System for High Energy Physics
Authors: Xia Libin , Sun Wei , Liu Xiaoyu , Sun Gongxing , Jiang Xiaowei 
Published: February 2023

Abstract
High energy physics (HEP) is moving towards extremely high statistical experiments and super-large-scale simulation of theory. In order to handle the challenge of rapid growth of data volumes, distributed computing and storage frameworks in Big Data area like Hadoop and Spark make computations easy to scale out. While the programming model based on in-memory RDD assumes that workload performs only local computation and rare message exchange, it’s inefficient at some HEP use cases, because several scientific computations, such as partial wave analysis (PWA) and lattice quantum chromodynamics (LQCD), are based on numerical linear algebra and iterative algorithms that rely on message passing between tasks. In this paper, we present a computing system (Blaze) that modifies Spark to support OpenMPI, and performs as a unified system that integrates MPI in DAG and provides task scheduling policy. The results indicate that the insufficient expressiveness in Spark model are supplemented by inter-task message passing. Additionally, Blaze also empowers MPI with the ability of data-locality computing and provides a solution of fault tolerance.
Comparing SYCL data transfer strategies for tracking use cases
Authors: Joube S , Grasland H , Chamont D , Brunet E 
Published: February 2023

Abstract
The aim of this work is to compare the performance and ease of programming of the various data transfer strategies provided by SYCL 2020: buffers/accessors on one hand and the different storage types exposed by Unified Shared Memory (USM) on the other hand. We measured the relative performance of USM exclusively located either on the host (USM host) or on the device (USM device), or automatically managed and moved (USM shared). We also tried to quantify the impact of formatting data in a GPU-friendly manner as opposed to using a regular pointer-based structure in which the C++ allocator was replaced by the SYCL allocator sycl::malloc. We first made a PCIe-bound micro-benchmark to test the SYCL memory models with a simple access pattern. We then switched to a real use-case provided by Traccc, a research project associated to the ACTS particle tracking library. The algorithm of interest is SparseCCL, a clustering algorithm used on the first step of track reconstruction. For consistency, all tests were made on a single hardware architecture: a recent server having a discrete GPU with dedicated VRAM, representative of the hardware currently used by the ACTS team.
Data and Analysis Preservation in the PHENIX Experiment at RHIC
Authors: Potekhin M 
Published: February 2023

Abstract
The PHENIX experiment (the Pioneering High Energy Nuclear Interaction eXperiment) is the largest of the four experiments that have operated at the Relativistic Heavy Ion Collider (RHIC), taking data in 2000-2016. PHENIX has made fundamental contributions to the discovery and study of the Quark-Gluon Plasma, advancement of spin physics and other areas. Currently, the PHENIX Collaboration is analyzing large data samples previously collected, while facing challenges in the area of Data and Analysis Preservation. We describe the strategy and practices employed by the PHENIX Collaboration to meet these challenges by leveraging state-of-the-art platforms and tools created and maintained by the High Energy and Nuclear Physics communities, including Zenodo, HEPData, OpenData, REANA and others.
Deployment of High Energy Physics software with a standard method
Authors: Hahn Thomas , Verbytskyi Andrii 
Published: February 2023

Abstract
The installation and maintenance of scientific software for research in experimental, phenomenological, and theoretical High Energy Physics (HEP) requires a considerable amount of time and expertise. While many tools are available to make the task of installation and maintenance much easier, many of these tools require maintenance on their own, have little documentation and very few are used outside of HEP community.
For the installation and maintenance of the software, we rely on the well tested, extensively documented, and reliable stack of software management tools with the RPM Package Manager (RPM) at its core. The precompiled HEP software packages can be deployed easily and without detailed Linux system knowledge and are kept up-to-date through the regular system update process. The precompiled packages were tested on multiple installations of openSUSE, RHEL clones, and Fedora. As the RPM infrastructure is adopted by many Linux distributions, the approach can be used on more systems.
In this contribution, we discuss our approach to software deployment in detail, present the software repositories for multiple RPM-based Linux distributions to a wider public and call for a collaboration for all the interested parties.
Evaluation of Portable Programming Models to Accelerate LArTPC Detector Simulations
Authors: Dong Zhihua , Knoepfel Kyle , Lin Meifeng , Viren Brett , Yu Haiwang 
Published: February 2023

Abstract
The Liquid Argon Time Projection Chamber (LArTPC) technology is widely used in high energy physics experiments, including the upcoming Deep Underground Neutrino Experiment (DUNE). Accurately simulating LArTPC detector responses is essential for analysis algorithm development and physics model interpretations. Accurate LArTPC detector response simulations are computationally demanding, and can become a bottleneck in the analysis workflow. Compute devices such as General-Purpose Graphics Processing Units (GPGPUs) have the potential to substantially accelerate simulations compared to traditional CPU-only processing. The software development for these compute accelerators often carries the cost of specialized code refactorization and porting to match the target hardware architecture. With the rapid evolution and increased diversity of the computer architecture landscape, it is highly desirable to have a portable solution that also maintains reasonable performance. We report our ongoing effort in evaluating Kokkos as a basis for this portable programming model using LArTPC simulations in the context of the Wire-Cell Toolkit, a C++ library for LArTPC simulations, data analysis, reconstruction and visualization.
GPU Accelerated Automatic Differentiation With Clad
Authors: Ifrim Ioana , Vassilev Vassil , Lange David J 
Published: February 2023

Abstract
Automatic Differentiation (AD) is instrumental for science and industry. It is a tool to evaluate the derivative of a function specified through a computer program. The range of AD application domain spans from Machine Learning to Robotics to High Energy Physics. Computing gradients with the help of AD is guaranteed to be more precise than the numerical alternative and have a low, constant factor more arithmetical operations compared to the original function. Moreover, AD applications to domain problems typically are computationally bound. They are often limited by the computational requirements of high-dimensional parameters and thus can benefit from parallel implementations on graphics processing units (GPUs).
Clad aims to enable differential analysis for C/C++ and CUDA and is a compiler-assisted AD tool available both as a compiler extension and in ROOT. Moreover, Clad works as a plugin extending the Clang compiler; as a plugin extending the interactive interpreter Cling; and as a Jupyter kernel extension based on xeus-cling.
We demonstrate the advantages of parallel gradient computations on GPUs with Clad. We explain how to bring forth a new layer of optimization and a proportional speed up by extending Clad to support CUDA. The gradients of well-behaved C++ functions can be automatically executed on a GPU. The library can be easily integrated into existing frameworks or used interactively. Furthermore, we demonstrate the achieved application performance improvements, including (≈10x) in ROOT histogram fitting and corresponding performance gains from offloading to GPUs.
The JSUB task submission and management software
Authors: Yang Yifan , Zhang Xiaomei , Zhao Xianghu , Li Weidong 
Published: February 2023

Abstract
In near future, new high energy physics experiments with challenging data volume are coming into operations or are planned in IHEP, China. A DIRAC-based distributed computing system is being enabled to process the huge amount of data to be obtained in these experiments. To encourage better utilization of computing resources, it’s essential to give users handy tools that saves them from troubles of mastering various technical details of distributed computing and let them have comfortable control over their tasks.
In this conference, we present the design and development of a extensible task submission and management system called JSUB, which provides users from different experiments with consistent solutions of managing massive jobs. JSUB covers functionalities including user interface, task creation and submission, run-time workflow control, task monitoring and management. Major features of this software include friendliness with extensions into different user scenarios, flexible task setting, and parametric job submission.
Currently, the JSUB software has been developed and put into use for JUNO and CEPC experiments. The software is also highly extensible to other HEP experiments. The designs and techniques could be interesting to other experiments which also use DIRAC as workload management system. The software has only been previously reported inside JUNO collaboration, and this is the first time to be presented to a wider audience.
Operation of the Neural z-Vertex Track Trigger for Belle II in 2021 - a Hardware Perspective
Authors: Unger Kai Lukas , Bähr Steffen , Becker Jürgen , Knoll Alois C. , Kiesling Christian , Meggendorfer Felix , Skambraks Sebastian 
Published: February 2023

Abstract
To reduce the background the z-Vertex Track Trigger estimates the collision origin in the Belle II experiment using neural networks. The main part is a pre-trained multilayer perceptron. The task of this perceptron is to estimate the z-vertex of the collision to suppress background from outside the interaction point. For this, a low latency real-time FPGA implementation is needed. We present an overview of the architecture and the FPGA implementation of the neuronal network and the preprocessing. We also show the handling of missing input data through preprocessing with specially trained neuronal networks implemented in hardware. For this, we will show the results of the z-vertex estimation and the latency for the implementation in the Belle II trigger system. Major update for the preprocessing stage utilizing a 3D Hough transformation processing step is ongoing.
Performance portability for the CMS Reconstruction with Alpaka
Authors: Bocci Andrea , Czirkos Angela , Pilato Antonio Di , Pantaleo Felice , Hugo Gabrielle , Kortelainen Matti , Redjeb Wahid 
Published: February 2023

Abstract
For CMS, Heterogeneous Computing is a powerful tool to face the computational challenges posed by the upgrades of the LHC, and will be used in production at the High Level Trigger during Run 3. In principle, to offload the computational work on non-CPU resources, while retaining their performance, different implementations of the same code are required. This would introduce code-duplication which is not sustainable in terms of maintainability and testability of the software. Performance portability libraries allow to write code once and run it on different architectures with close-to-native performance. The CMS experiment is evaluating performance portability libraries for the near term future.
Pyrate: a novel system for data transformations, reconstruction and analysis
Authors: Scutti Federico 
Published: February 2023

Abstract
The Pyrate framework provides a dynamic, versatile, and memory-efficient approach to data format transformations, object reconstruction and data analysis in particle physics. The system is implemented with the Python programming language, allowing easy access to the scientific Python ecosystem and commodity big data technologies. Developed within the context of the SABRE South experiment for dark matter direct detection, Pyrate relies on a blackboard design pattern where algorithmic trees are dynamically generated throughout a run where root nodes are managed by a central control unit. The system guarantees an economical usage of memory allocated by algorithms where individual algorithmic instances can be reused for multiple objects. The framework is intended to improve upon the user experience, portability and scalability of offline software systems currently available in the particle physics community with particular attention to medium to small-scale experiments.
Study on Detector Geometry Transformation and Visualization in Unity
Authors: Huang Kaixuan , Zhang Yumei , You Zhengyun 
Published: February 2023

Abstract
In High Energy Physics (HEP) experiments, it is useful for physics analysis and outreach if the event display software can provide sophisticated visualization effects. Unity is professional software that provides 3D modeling and animation production. GDML format files are commonly used for detector descriptions in HEP experiments. In this work, we present a method for automating the import of GDML files to the Unity engine modeling through geometric transformation. With the conversion of the file format, the complicated geometric description of the HEP detector in a GDML file can be imported directly into the Unity engine using an FBX file. This format conversion was applied to BESIII experiments to achieve an automated geometry conversion to build geometry models in the Unity engine.
Analysing arrival directions of ultra-high-energy cosmic rays with convolutional neural networks
Authors: Kalashev Oleg , Pshirkov Maxim , Zotov Mikhail 
Published: February 2023

Abstract
The problem of identification of ultra-high-energy cosmic ray (UHECR) sources is greatly complicated by the fact that even the highest energy cosmic rays may be deflected by tens of degrees in the galactic magnetic fields. We show that arrival directions of UHECRs from several nearest active galaxies form specific patterns in the sky, which can be effectively recognized by convolutional neural networks. We use one of the recently developed convnet implementations for images defined on the sphere to train the classifier that is able to detect patterns that can be present in the experimental data. We calculate the minimal detectable from-source event fractions for several realistic source candidates and discuss the method limitations.
The Analysis of High-Frequency Finance Data using ROOT
Authors: Debie P , Verhulst M E , Pennings J M E , Tekinerdogan B , Catal C , Naumann A , Demirel S , Moneta L , Alskaif T , Rembser J , van Leeuwen P 
Published: February 2023

Abstract
High-frequency financial market data is conceptually distinct from high energy physics (HEP) data. Market data is a time series generated by market participants, while HEP data is a set of independent events generated by collisions between particles. However, there are similarities within the data structure and required tools for data analysis, and both fields share a similar set of problems facing the increasing size of data generated. This paper describes some of the core concepts of financial markets, discusses the data similarities and differences with HEP, and provides an implementation to use ROOT, an open-source data analysis framework in HEP, with financial market data. This implementation makes it possible to take advantage of the rich set of features available in ROOT and extends research in finance.
Generative Adversarial Networks for the fast simulation of the Time Projection Chamber responses at the MPD detector
Authors: Maevskiy A , Ratnikov F , Zinchenko A , Riabov V , Sukhorosov A , Evdokimov D 
Published: February 2023

Abstract
Detailed detector simulation models are vital for the successful operation of modern high-energy physics experiments. In most cases, running detailed models requires a significant amount of computing resources. It is desired to have approaches that are less resource-intensive. In this work, we demonstrate the applicability of Generative Adversarial Networks (GAN) as a basis for such fast-simulation models for the case of the Time Projection Chamber (TPC) at the MPD detector at the NICA accelerator complex. Our prototype GAN-based model of TPC works faster than the detailed simulation in an order of magnitude without any noticeable drop in the quality of the high-level reconstruction characteristics for the generated data. Approaches with direct and indirect quality metrics optimization are compared.
GNN-based end-to-end reconstruction in the CMS Phase 2 High-Granularity Calorimeter
Authors: Bhattacharya S , Chernyavskaya N , Ghosh S , Gray L , Kieseler J , Klijnsma T , Long K , Nawaz R , Pedro K , Pierini M , Pradhan G , Qasim S R , Viazlo O , Zehetner P 
Published: February 2023

Abstract
We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each HGCAL endcap. The ML algorithm is trained to predict clusters of hits originating from the same incident particle by labeling the hits with the same cluster index. We impose simple criteria to assess whether the hits associated as a cluster by the prediction are matched to those hits resulting from any particular individual incident particles. The algorithm is studied by simulating two tau leptons in each of the two HGCAL endcaps, where each tau may decay according to its measured standard model branching probabilities. The simulation includes the material interaction of the tau decay products which may create additional particles incident upon the calorimeter. Using this varied multiparticle environment we can investigate the application of this reconstruction technique and begin to characterize energy containment and performance.
Machine Learning applications for Data Quality Monitoring and Data Certification within CMS
Authors: Wachirapusitan Vichayanun 
Published: February 2023

Abstract
The Compact Muon Solenoid (CMS) detector is getting ready for datataking in 2022, after a long shutdown period. LHC Run-3 is expected to deliver an ever-increasing amount of data. To ensure that the recorded data has the best quality possible, the CMS Collaboration has dedicated Data Quality Monitoring (DQM) and Data Certification (DC) working groups. These working groups are made of human shifters and experts who carefully watch and investigate histograms generated from different parts of the detector. However, the current workflow is not granular enough and prone to human errors. On the other hand, several techniques in Machine Learning (ML) can be designed to learn from large collections of data and make predictions for the data quality at an unprecedented speed and granularity. Hence, the data certification process can be considered as a perfect problem for ML techniques to tackle. With the help of ML, we can increase the granularity and speed of the DQM workflow and assist the human shifters and experts in detecting anomalies during data-taking. In this presentation, we present preliminary results from incorporating ML to highly granular DQM information for data certification.
Machine Learning for Particle Flow Reconstruction at CMS
Authors: Pata Joosep , Duarte Javier , Mokhtar Farouk , Wulff Eric , Yoo Jieun , Vlimant Jean-Roch , Pierini Maurizio , Girone Maria 
Published: February 2023

Abstract
We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy. We have studied a possible evolution of particle flow towards heterogeneous computing platforms such as GPUs using a graph neural network. The machine-learned PF model reconstructs particle candidates based on the full list of tracks and calorimeter clusters in the event. For validation, we determine the physics performance directly in the CMS software framework when the proposed algorithm is interfaced with the offline reconstruction of jets and missing transverse energy. We also report the computational performance of the algorithm, which scales approximately linearly in runtime and memory usage with the input size.
Meta-learning for multiple detector geometry modeling
Authors: Salamani Dalila , Zaborowska Anna , Pokorski Witold 
Published: February 2023

Abstract
The simulation of the passage of particles through the detectors of High Energy Physics (HEP) experiments is a core component of any physics analysis. A detailed and accurate simulation of the detector response using the Geant4 toolkit is a time and CPU consuming process. With the upcoming high luminosity LHC upgrade, with more complex events and a much increased trigger rate, the amount of required simulated events will increase. Several research directions investigated the use of Machine Learning based models to accelerate particular detector response simulation. This results in a specifically tuned simulation and generally these models require a large amount of data for training. Meta learning has emerged recently as fast learning algorithm using small training datasets. In this paper, we propose a meta-learning model that “learns to learn” to generate electromagnetic showers using a first-order gradient based algorithm. This model is trained on multiple detector geometries and can rapidly adapt to a new geometry using few training samples.
Studying quantum algorithms for particle track reconstruction in the LUXE experiment
Authors: Funcke Lena , Hartung Tobias , Heinemann Beate , Jansen Karl , Kropf Annabel , Kühn Stefan , Meloni Federico , Spataro David , Tüysüz Cenk , Chinn Yap Yee 
Published: February 2023

Abstract
The LUXE experiment (LASER Und XFEL Experiment) is a new experiment in planning at DESY Hamburg, which will study Quantum Electrodynamics (QED) at the strong-field frontier. In this regime, QED is non-perturbative. This manifests itself in the creation of physical electron-positron pairs from the QED vacuum. LUXE intends to measure the positron production rate in this unprecedented regime by using, among others, a silicon tracking detector. The large number of expected positrons traversing the sensitive detector layers results in an extremely challenging combinatorial problem, which can become computationally very hard for classical computers. This paper presents a preliminary study to explore the potential of quantum computers to solve this problem and to reconstruct the positron trajectories from the detector energy deposits. The reconstruction problem is formulated in terms of a quadratic unconstrained binary optimisation. Finally, the results from the quantum simulations are discussed and compared with traditional classical track reconstruction algorithms.