bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparison of MHD stability properties between QH-mode and ELMy H-mode plasmas by considering plasma rotation and ion diamagnetic drift effects
Authors: Aiba N. , Chen X. , Osborne T.H. , Burrell K.H. 
Published: February 2023

Abstract
Magnetohydrodynamic (MHD) stability at tokamak edge pedestal in a quiescent H-mode (QH-mode) and type-I ELMy H-mode plasmas in DIII-D experiment was analyzed by considering plasma rotation and ion diamagnetic drift effects. QH-mode plasma is marginally stable to kink/peeling mode (K/PM), but ELMy H-mode one is almost unstable to peeling-ballooning mode (PBM). It was identified that there are three physics features responsible for the difference in the MHD stability properties between QH-mode plasma and ELMy H-mode one. These are the distance of pedestal foot from the last closed flux surface (LCFS), the amount of the ion diamagnetic drift frequency at pedestal, and impact of coupled rotation and ion diamagnetic drift effects. These features were confirmed through the numerical experiments that the stability properties of the QH-mode plasma can be changed to that of the ELMy H-mode one by shifting the plasma profiles inward in the radial direction and halving the ion diamagnetic drift frequency. The reasons of the change in the stability properties are thought as that K/PM is stabilized due to the inward shift of the bootstrap current profile, and PBM is destabilized due to the reduction of the coupled rotation and ion diamagnetic drift stabilizing effect. Importance of these features was validated through numerical experiments with experimental data of other QH-mode plasmas in DIII-D. All the results show that MHD stability properties of QH-mode plasma can be obtained in case that pedestal foot is close to LCFS, ion diamagnetic drift frequency is large due to high ion temperature, and strong rotation shear exists near pedestal.
Generators and the (Accelerated) Future
Authors: Isaacson J 
Published: February 2023

Abstract
With the High Luminosity LHC coming online in the near future, event generators will need to provide very large event samples to match the experimental precision. Currently, the estimated cost to generate these events exceeds the computing budget of the LHC experiments. To address these issues, the computing efficiency of event generators need to be improved. Many different approaches are being taken to achieve this goal. I will cover the ongoing work on implementing event generators on the GPUs, machine learning the matrix element, machine learning the phase space, and minimizing the number of negative weight events.
C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA
Authors: An Sitong , Moneta Lorenzo , Sengupta Sanjiban , Hamdan Ahmat , Sossai Federico , Saxena Aaradhya 
Published: February 2023

Abstract
We report the latest development in ROOT/TMVA, a new tool that takes trained ONNX deep learning models and emits C++ code that can be easily included and invoked for fast inference of the model, with minimal dependency. An introduction to SOFIE (System for Optimized Fast Inference code Emit) is presented, with examples of interface and generated code. We discuss the latest expanded support of a variety of neural network operators, including convolutional and recurrent layers, as well as the integration with RDataFrame. We demonstrate the latest performance of this framework with a set of benchmarks.
Demonstration of FPGA Acceleration of Monte Carlo Simulation
Authors: Barbone M. , Howard A. , Tapper A. , Chen D. , Novak M. , Luk W. 
Published: February 2023

Abstract
We present results from a stand-alone simulation of electron single Coulomb scattering as implemented completely on an Field Programmable Gate Array (FPGA) architecture and compared with an identical simulation on a standard CPU. FPGA architectures offer unprecedented speed-up capability for Monte Carlo simulations, however with the caveats of lengthy development cycles and resource limitation, particularly in terms of on-chip memory and DSP blocks. As a proof of principle of acceleration on an FPGA, we chose a single scattering process of electrons in water at an energy of 6 MeV. The initial code-base was implemented in C++ and optimised for CPU processing. To measure the potential performance gains of FPGAs compared to modern multi-core CPUs we computed 100M histories of a 6 MeV electron interacting in water. Without performing any hardware-specific optimisation, the results show that the FPGA implementation is over 110 times faster than an optimised parallel implementation running on 12 CPU-cores, and over 270 times faster than a sequential single-core CPU implementation. The results on both architectures were statistically equivalent. The successful implementation and acceleration results are very encouraging for the future exploitation of more sophisticated Monte Carlo simulation on FPGAs for High Energy Physics applications.
Evaluating Awkward Arrays, uproot, and coffea as a query platform for High Energy Physics Data
Authors: Gray L. , Smith N. 
Published: February 2023

Abstract
Query languages for High Energy Physics (HEP) are an ever present topic within the field. A query language that can efficiently represent the nested data structures that encode the statistical and physical meaning of HEP data will help analysts by ensuring their code is more clear and pertinent. As the result of a multi-year effort to develop an in-memory columnar representation of high energy physics data, the NumPy, Awkward Array, and uproot Python packages present a mature and efficient interface to HEP data. Atop that base, the coffea package adds functionality to launch queries at scale, manage and apply experiment-specific transformations to data, and present a rich object-oriented columnar data representation to the analyst. Recently, a set of Analysis Description Language (ADL) benchmarks has been established to compare HEP queries in multiple languages and frameworks. In this paper we present these benchmark queries implemented within the coffea framework and discuss their readability and performance characteristics. We find that the columnar queries perform as well or better than the implementations given in previous studies.
GPU acceleration of the ATLAS calorimeter clustering algorithm
Authors: Fernandes Nuno 
Published: February 2023

Abstract
Given the upcoming High-Luminosity LHC Upgrade, the performance requirements for the trigger systems associated with the LHC experiments will increase due to the larger volume of data to be processed. One of the possibilities that the ATLAS Collaboration is evaluating for upgrading the software-based portion of its trigger system is the use of Graphical Processing Units as hardware accelerators. The present work focuses on the GPU acceleration of the Topological Clustering algorithm, which is used to reconstruct calorimeter showers by grouping cells according to their signal-to-noise ratio. A more GPU parallelizable version of the Topological Clustering, called Topo-Automaton Clustering, was implemented within AthenaMT, the software framework of the ATLAS trigger, and its results were compared to those of the standard CPU algorithm to ensure physical validity is maintained. Time measurements suggest an average improvement of the event processing time by a factor between 3.5 and 5.5 (depending on the kind of the event), though less than 20% of that time corresponds to the algorithm itself, suggesting that the main bottleneck lies in data transfers and conversions.
Graph-based algorithm for the understanding of failures in the ATLAS infrastructure
Authors: Uribe Gustavo A , Tortajada Ignacio Asensi , Solans Sánchez Carlos , Rummler André , Oyulmaz Kaan Yüksel , Denizli Haluk 
Published: February 2023

Abstract
The ATLAS Technical Coordination Expert System is a knowledge-based application which describes and simulates the ATLAS experiment based on its components and their relationships with differing levels of granularity but with an emphasis on general infrastructure. It facilitates the sharing of knowledge and improves the communication among experts with different backgrounds and domains of expertise. The developed software has become essential for the planning of interventions as it gives easily insight into their consequences. Furthermore, it has also proven to be useful for exploring the most effective ways to improve the ATLAS operation and reliability by identifying points of failure with significant impact. The underlying database describes more than 13,000 elements with 89,000 relationships among them. It combines information from diverse domains such as detector control and safety systems, gas and water supplies, cooling, ventilation, cryogenics, and electricity distribution. As the most recent addition, a tool to identify the most probable cause of a failure state has been developed. This paper discusses the graph-based algorithm currently implemented by that tool and shows its behaviour based on the parameters entered by the user. An example in form of a real failure event is given which demonstrates the potential of the Expert System for understanding major failures faster in urgent situations.
Key4hep Software Stack for Detector Studies
Authors: Fang Wenxing , Declara Placido Fernandez , Gaede Frank-Dieter , Ganis Gerardo , Hegner Benedikt , Helsens Clement , Huang Xingtao , Hyun Ko Sang , Madlener Thomas , Li Teng , Li Weidong , Lin Tao , Sailer Andre , Stapf Birgit Sylvia , Volkl Valentin , Wang Joseph , Zou Jiaheng 
Published: February 2023

Abstract
Detector optimisation and physics performance studies are an integral part of the development of future collider experiments. The Key4hep project aims to design a common set of software tools for future, or even present, High Energy Physics projects. Based on the iLCSoft and FCCSW frameworks an integrated solution for detector simulation, reconstruction and analyses is being developed. This presentation will give a short overview of the major ingredients of the Key4hep turnkey software; the common event datamodel – EDM4hep, the Gaudi based event processing framework, some dedicated packages developed on top of these, and the use of the Spack package manager, which enables installations on a variety of platforms. A first example highlights the synergies that were already achieved by the collaboration of the different collider communities (CEPC, CLIC, FCC, ILC). It shows the seamless integration of fast simulation with Delphes and the LCFIplus vertexing processor from iLCSoft. This combination is possible due to EDM4hep, the processor wrapper (k4MarlinWrapper), and the k4SimDelphes framework integration in Key4hep. As a second example, the multi-threaded execution of the iLCSoft processors in the Gaudi framework making use of the k4MarlinWrapper will be showcased. The third example will demonstrate the execution of a multi-threaded simulation of a drift chamber in the CEPC experiment. The chosen application will take information of primary ionizations generated by an incident charged particle as input and will create the waveform as the final output, which represents the signal collected by the chamber’s signal wire. The simulation algorithm is based on a neural network model which can be used to simulate the effects from the ionization electron’s drift and avalanches. Using these examples we review some of the challenges and issues that we encountered as well as how they were addressed in the end. We close with a brief discussion of the next steps for the Key4hep project.
Offline Software Framework for the Super Tau Charm Facility
Authors: Huang W. H. , Li T. , Li Q. Y. , Li H. , Liu D. , Huang X. T. 
Published: February 2023

Abstract
The Super Tau Charm Facility (STCF) is a high-luminosity electron-positron collider proposed to study the tau and charm physics, precisely test Standard Model (SM) and hunt for new physics beyond SM. The Offline Software of Super Tau ChARm Facility (OSCAR) is designed and developed based on SNiPER for the whole offline data processing, including detector simulation, calibration, reconstruction as well as physics analysis. Several state-of-art software and tools in the HEP community are adopted, such as the Detector Description Toolkit for High Energy Physics (DD4hep) for the consistent detector description, the plain-old-data I/O (podio) for the efficient implementation of event data model (EDM), etc. This paper focuses on the design and implementation of OSCAR, particularly the way to integrate Geant4, DD4hep and podio into SNiPER to provide the unified computing environment and platform for detector simulation, reconstruction and visualization. Now OSCAR is used to facilitate the design of STCF detectors, conduct detector performance study as well as physics potential study. OSCAR also provides a potential solution for other light-weighted HEP experiments.
Offloading electromagnetic shower transport to GPUs
Authors: Amadio G , Apostolakis J , Buncic P , Cosmo G , Dosaru D , Gheata A , Hageboeck S , Hahnfeld J , Hodgkinson M , Morgan B , Novak M , Petre A A , Pokorski W , Ribon A , Stewart G A , Vila P M 
Published: February 2023

Abstract
Making general particle transport simulation for high-energy physics (HEP) single-instruction-multiple-thread (SIMT) friendly, to take advantage of accelerator hardware, is an important alternative for boosting the throughput of simulation applications. To date, this challenge is not yet resolved, due to difficulties in mapping the complexity of Geant4 components and workflow to the massive parallelism features exposed by graphics processing units (GPU). The AdePT project is one of the R&D initiatives tackling this limitation and exploring GPUs as potential accelerators for offloading some part of the CPU simulation workload. Our main target is to implement a complete electromagnetic shower demonstrator working on the GPU. The project is the first to create a full prototype of a realistic electron, positron, and gamma electromagnetic shower simulation on GPU, implemented as either a standalone application or as an extension of the standard Geant4 CPU workflow. Our prototype currently provides a platform to explore many optimisations and different approaches. We present the most recent results and initial conclusions of our work, using both a standalone GPU performance analysis and a first implementation of a hybrid workflow based on Geant4 on the CPU and AdePT on the GPU.
Predicting ALICE Grid throughput using recurrent neural networks
Authors: Popa Mircea , Grigoras Costin , Vallecorsa Sofia 
Published: February 2023

Abstract
The Worldwide LHC Computing Grid (WLCG) is the infrastructure enabling the storage and processing of the large amount of data generated by the LHC experiments, and in particular the ALICE experiment. With the foreseen increase in the computing requirements of the future High Luminosity LHC experiments, a data placement strategy which increases the efficiency of the WLCG computing infrastructure becomes extremely relevant for the scientific success of the LHC scientific programme. Currently, the data placement at the ALICE Grid computing sites is determined by heuristic algorithms. Optimisation of the data storage could yield substantial benefits in terms of efficiency and time-to-result. This has however proven to be arduous due to the complexity of the problem. In this work we propose a modelisation of the behaviour of the system via principal component analysis, time series analysis and deep learning, starting from the detailed data collected by the MonALISA monitoring system. We show that it is possible to analyse and model the throughput of the ALICE Grid to a level that has not been possible before, comparing the performance of different deep learning architectures based on recurrent neural networks. Analyzing about six weeks of activity, the Grid I/O throughput trend is successfully predicted with a mean relative error of 4%, while the prediction of the throughput itself performs at 5%.
The unseen: revealing the blind production procedure and experience for NP data
Authors: Van Buren G , Lauret J 
Published: February 2023

Abstract
A unique experiment was conducted by the STAR Collaboration in 2018 to investigate differences between collisions of nuclear isobars, a potential key to unraveling one of the physics mysteries in our field: why the universe is made predominantly of matter. Enhancing the credibility of findings was deemed to hinge on blinding analyzers from knowing which dataset they were examining, necessitating efforts by the data production team to investigate and implement new (in our field) blinding practices. With nearly two decades of established machinery intended to provide open data and metadata access in STAR, the breadth of details to consider for a successful blinding process was substantial.
In this paper, we will review the experience of the first-ever species-blind data production in high energy collider physics. Considerations of what needed blinding, how to blind it, and from whom, will be discussed. Practical impositions on the selection of data to produce and how to produce it will also be highlighted as tell-tales can arise even where there were operational efforts to avoid them. With the appropriate blinding achieved, analyzers and reviewers have been empowered to focus on the physics of interest.
Ensemble Models for Calorimeter Simulations
Authors: Jaruskova K , Vallecorsa S 
Published: February 2023

Abstract
Foreseen increasing demand for simulations of particle transport through detectors in High Energy Physics motivated the search for faster alternatives to Monte Carlo-based simulations. Deep learning approaches provide promising results in terms of speed up and accuracy, among which generative adversarial networks (GANs) appear to be particularly successful in reproducing realistic detector data. However, the GANs tend to suffer from different issues such as not reproducing the full variability of the training data, missing modes problem, and unstable convergence. Various ensemble techniques applied to image generation proved that these issues can be moderated either by deploying multiple generators or multiple discriminators. This work follows a development of a GAN with two-dimensional convolutions that reproduces 3D images of an electromagnetic calorimeter. We build on top of this model and construct an ensemble of generators. With each new generator, the ensemble shows better agreement with the Monte Carlo images in terms of shower shapes and the sampling fraction.
Explainability of High Energy Physics events classification using SHAP
Authors: Pezoa R , Salinas L , Torres C 
Published: February 2023

Abstract
Complex machine learning models have been fundamental for achieving accurate results regarding events classification in High Energy Physics (HEP). However, these complex models or black-box systems lack transparency and interpretability. In this work, we use the SHapley Additive exPlanations (SHAP) method for explaining the output of two event machine learning classifiers, based on eXtreme Gradient Boost (XGBoost) and deep neural networks (DNN). We compute SHAP values to interpret the results and analyze the importance of individual features, and the experiments show that SHAP method has high potential for understanding complex machine learning model in the context of high energy physics.
The Fast Simulation Chain in The ATLAS Experiment
Authors: Aitbenchikh Brahim 
Published: February 2023

Abstract
The physics program in the ATLAS experiment at the Large Hadron Collider has achieved high-quality and competitive physics results since its inception, based on large and accurate datasets of simulated Monte Carlo events. Generating these billions of Monte Carlo using the Geant4 framework requires significant computing resources. As such the ATLAS CPU is limited and taking into account the expected increase of the average number of proton-proton collisions during the high luminosity LHC phase, a very fast detector simulation is critical when high accuracy is not needed. In order to solve this problem and to meet the challenge of simulated sample statistics requirements, fast alternatives approaches have been developed by ATLAS to replace the algorithms currently used in the standard MC production chain. This document provides a description of the Fast simulation chain tools and also presents the latest physics performance studies: Fast tracking detector simulation, Fast digitization and Pile-up simulation.
Generative models uncertainty estimation
Authors: Anderlini L , Chimpoesh C , Kazeev N , Shishigina A 
Published: February 2023

Abstract
In recent years fully-parametric fast simulation methods based on generative models have been proposed for a variety of high-energy physics detectors. By their nature, the quality of data-driven models degrades in the regions of the phase space where the data are sparse. Since machine-learning models are hard to analyse from the physical principles, the commonly used testing procedures are performed in a data-driven way and can’t be reliably used in such regions. In our work we propose three methods to estimate the uncertainty of generative models inside and outside of the training phase space region, along with data-driven calibration techniques. A test of the proposed methods on the LHCb RICH fast simulation is also presented.
Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers
Authors: Gumpula K , Koloskov N , Grzenda D , Hewes V , Aurisano A , Cerati G , Day A , Kowalkowski J , Lee C , Wang K , Liao W , Spiropulu M , Agrawal A , Vlimant J , Gray L , Klijnsma T , Calafiura P , Conlon S , Farrell S , Ju X , Murnane D 
Published: February 2023

Abstract
The Exa.TrkX project presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar reconstruction tasks in the Large Hadron Collider (LHC). Graphs describing particle interactions are formed by treating each detector hit as a node, with edges describing the relationships between hits. We utilise a multi-head attention message passing network which performs graph convolutions in order to label each node with a particle type.
We present an updated variant of our GNN architecture, with several improvements. After testing the model on more realistic simulation with regions of unresponsive wires, the target was modified from edge classification to node classification in order to increase robustness. Removing edges as a classification target opens up a broader possibility space for edge-forming techniques; we explore the model’s performance across a variety of approaches, such as Delaunay triangulation, kNN, and radius-based methods. We also extend this model to the 3D context, sharing information between detector views. By using reconstructed 3D spacepoints to map detector hits from each wire plane, the model naively constructs 2D representations that are independent yet fully consistent.
Robust Neural Particle Identification Models
Authors: Ryzhikov Artem , Temirkhanov Aziz , Derkach Denis , Hushchyn Mikhail , Kazeev Nikita , Mokhnenko Sergei 
Published: February 2023

Abstract
The volume of data processed by the Large Hadron Collider experiments demands sophisticated selection rules typically based on machine learning algorithms. One of the shortcomings of these approaches is their profound sensitivity to the biases in training samples. In the case of particle identification (PID), this might lead to degradation of the efficiency for some decays not present in the training dataset due to differences in input kinematic distributions. In this talk, we propose a method based on the Common Specific Decomposition that takes into account individual decays and possible misshapes in the training data by disentangling common and decay specific components of the input feature set. We show that the proposed approach reduces the rate of efficiency degradation for the PID algorithms for the decays reconstructed in the LHCb detector.