bluuebunny/crossref_metadata_2025_split huggingface dataset:
Extending the distributed computing infrastructure of the CMS experiment with HPC resources
Authors: Adelman-McCarthy J , Boccali T , Caspart R , Delgado Peris A , Fischer M , Flix Molina J , Giffels M , Hernández J M , Hufnagel D , Kühn E , Madlener T , Mohapatra A K , Ozturk H , Pérez-Calero Yzquierdo A , Spiga D , Wissing C 
Published: February 2023

Abstract
Particle accelerators are an important tool to study the fundamental properties of elementary particles. Currently the highest energy accelerator is the LHC at CERN, in Geneva, Switzerland. Each of its four major detectors, such as the CMS detector, produces dozens of Petabytes of data per year to be analyzed by a large international collaboration. The processing is carried out on the Worldwide LHC Computing Grid, that spans over more than 170 compute centers around the world and is used by a number of particle physics experiments. Recently the LHC experiments were encouraged to make increasing use of HPC resources. While Grid resources are homogeneous with respect to the used Grid middleware, HPC installations can be very different in their setup. In order to integrate HPC resources into the highly automatized processing setups of the CMS experiment a number of challenges need to be addressed. For processing, access to primary data and metadata as well as access to the software is required. At Grid sites all this is achieved via a number of services that are provided by each center. However at HPC sites many of these capabilities cannot be easily provided and have to be enabled in the user space or enabled by other means. At HPC centers there are often restrictions regarding network access to remote services, which is again a severe limitation. The paper discusses a number of solutions and recent experiences by the CMS experiment to include HPC resources in processing campaigns.
Flexible visualization of a 3rd party Intrusion Prevention (Security) tool: A use case with the ELK stack
Authors: Poat M D , Lauret J , Fedele D 
Published: February 2023

Abstract
A difficult aspect of cyber security is the ability to achieve automated real time intrusion prevention across various sets of systems. To this extent, several companies are offering comprehensive solutions that leverage an “accuracy of scale” and moving much of the intelligence and detection on the Cloud, relying on an ever-growing set of data and analytics to increase decision accuracy. Often, they provide tools to visualize the decision workflows in attack prevention (as well as tune the algorithm) but those solutions are not always practical as companies see the problem as “global” that is, from a unified Cyber-security standpoint. However, a key to a successful Cyber-security program is transparency and trust: from an experimental team viewpoint, this specifically means having the ability to immediately see what and from where, who has been blocked and being able to inform the community in case of a revoked access without the need for filing a “ticket” (that may eventually be answered) – in other words, rapid response to their user-base is essential but solutions targeting “sub-groups” in an organization are not often available.
We have come up with a versatile solution leveraging the ELK stack (Elasticsearch, Logstash, & Kibana) and an IPS (Intrusion Prevention System) based WAF (Web Application Firewall) from Signal Sciences. Signal Science allows the streaming of detailed logs in a Logstash format suitable for custom solutions for visualization. By combining these two tools, we have strengthened our security posture and enabled individual experiments to monitor their own traffic. Specifically, the IPS WAF provides unique data such as country of origin, protocol, response code, source IP, and paths accessed.
In this contribution, we will show how we engineered a visualization solution so experiment groups could access a dashboard with predefined graphs but also, where they can create individual customizable dashboards used to display blocked traffic and troubleshoot latency issues. We will discuss the details and procedures for developing and configuring these tools and how it benefits cyber security postures across our scientific based environment.
From design to production: State-of-the-art web user interfaces to operate ALICE offline and online system
Authors: Raduta G C 
Published: February 2023

Abstract
The ALICE Experiment at CERN’s Large Hadron Collider is currently undergoing a major upgrade during Long Shutdown; this entails a new Online-Offline computing system and a new set of reliable and performant graphical user interfaces. After deployment, the updated system is expected to read an estimated throughput of 27 Tb/s of raw data and index 800 of Gb/s reconstructed data. The newly developed graphical interfaces are to be used 24 hours a day, 365 days a year in the control room by the subsystem experts and on-call support staff. The interfaces are being built based on a common library developed in-house whose goal is to provide the core functionalities and building blocks, and on modern web technologies meant to ensure support for both Runs 3 and 4. This approach secures a consolidated user experience that is efficient, resilient, easy to maintain, and will bring live, straightforward control of the experiment and useful information to the collaboration. This, in turn, will help in our mission to increase the usage of the LHC beam, maximizing the recorded data and potentially leading to new discoveries. Furthermore, in order to guarantee the stability of the platforms, state-of-the-art technologies and practices were applied to build a continuous-integration and continuous-deployment environment to ensure high-quality code, faster releases, and reduced mean time to resolution. This paper provides an in-depth look at the newly developed web-based components including their features and architecture as well as the automated deployment workflow used for software quality assurance.
Going fast on a small-size computing cluster
Authors: Eich Niclas Steve , Erdmann Martin , Diekmann Svenja , Fackeldey Manfred Peter , Fischer Benjamin , Noll Dennis , Rath Yannik Alexander 
Published: February 2023

Abstract
Fast turnaround times for LHC physics analyses are essential for scientific success. The ability to quickly perform optimizations and consolidation studies is critical. At the same time, computing demands and complexities are rising with the upcoming data taking periods and new technologies, such as deep learning. We present a show-case of the HH→bbWW analysis at the CMS experiment, where we process 𝒪(1 − 10)TB of data on 100 threads in a few hours. This analysis is based on the columnar NanoAOD data format, makes use of the NumPy ecosystem and HEP specific tools, in particular Coffea and Dask. Data locality, especially IO latency, is optimized by employing a multi-level caching structure using local file storage and on-worker SSD caches. We process thousands of events simultaneously within a single thread, thus enabling straightforward use of vectorized operations. Resource intensive computing tasks, such as GPU accelerated DNN inference and histogram aggregation in the 𝒪(10)GB regime, are offloaded to dedicated workers. The analysis consists of hundreds of distinctly different workloads and is steered through a workflow management tool ensuring reproducibility throughout the development process up to journal publication.
Implementation of the Event Metadata System for physics analysis in the NICA experiments
Authors: Alexandrov E , Alexandrov I , Chebotov A , Degtyarev A , Filozova I , Gertsenberger K , Klimai P , Yakovlev A 
Published: February 2023

Abstract
NICA (Nuclotron-based Ion Collider fAcility) is a new accelerator complex, which is under construction at the Joint Institute for Nuclear Research in Dubna to study properties of dense baryonic matter. The experiments of the NICA projects have already generated and obtained substantial volumes of event data, and it is expected that the overall number of stored events will increase from the current value of hundreds of millions to several billion events per year. The developed Event Metadata System is required to store and index experimental event records obtained at NICA facilities, allowing to quickly search for only necessary events based on various criteria for further processing and physics analyses. The main aim of the information system that is described is to speed up desired physics analyses and data quality checks. In addition, it allows reducing the load of the NICA computing infrastructure. The Event Metadata System, which is presented in the paper, is integrated with other experimental and software systems, in particular, NICA ROOT-based frameworks and implemented Condition Database. An access and management of the event metadata in the Event Catalogue being used to select only events for a particular physics analysis are provided via several interfaces, such as REST API, Web UI, and a dedicated C++/ROOT interface. The detailed architecture of the system and developed services including monitoring are also presented.
Nanosecond Jet Classification at LHC
Authors: Sznajder Andre 
Published: February 2023

Abstract
We investigate the possibility of using Deep Learning algorithms for jet identification in the L1 trigger at HL-LHC. We perform a survey of architectures (MLP, CNN, Graph Networks) and benchmark their performance and resource consumption on FPGAs using a QKeras for quantization aware training and HLS4ML to convert the neural network model into a FPGA firmware. We use the HLS4ML jet dataset to compare the results obtained in this study to previous literature on Fast Machine Learning applications on FPGAs.
Managing heterogeneous device memory using C++17 memory resources
Authors: Swatman S N , Krasznahorkay A , Gessinger P 
Published: February 2023

Abstract
Programmers using the C++ programming language are increasingly taught to manage memory implicitly through containers provided by the C++ standard library. However, heterogeneous programming platforms often require explicit allocation and deallocation of memory. This discrepancy in memory management strategies can be daunting and problematic for C++ developers who are not already familiar with heterogeneous programming. The C++17 standard introduces the concept of memory resources, which allow the user to control how standard library containers allocate memory; we believe that this addition to the C++17 standard is a powerful tool towards the unification of memory management for heterogeneous systems with best-practice C++ development. In this paper, we present vecmem, a library of memory resources which allows efficient and user-friendly allocation of memory on CUDA, HIP, and SYCL devices through standard C++ containers. We investigate the design and use cases of such a library, the potential performance gains over naive memory allocation, and the limitations of this memory allocation model.
A new Software Training Model at Belle II
Authors: Lieret Kilian , Luca Pinna Angioni Gian , Bauer Moritz , Bertemes Michel , Bilokin Sviatoslav , Di Canto Angelo , Casarosa Giulia , Cheaib Racha , Cunliffe Sam , Eberlein Nathalie , Eliachevitch Michael , Garcia Marcela , Grace Phil , Greenwald Daniel , Hartbrich Oskar , Hsu Chia-Ling , Komarov Ilya , Kuhr Thomas , Kulii Yaroslav , Meier Frank , Milesi Marco , Mora Alejandro , De Pietro Giacomo , Prim Markus , Ritter Martin , Schmolz Pascal , Tamponi Umberto , Tenchini Francesco , Villanueva Michel , Marie Wakeling Hannah , Zhou Xing-Yu 
Published: February 2023

Abstract
The physics output of modern experimental HEP collaborations hinges not only on the quality of its software but also on the ability of the collaborators to make the best possible use of it. With the COVID-19 pandemic making in-person training impossible, the training paradigm at Belle II was shifted from periodic workshops towards guided self-study.
To that end, the study material was rebuilt from scratch as a series of modular and hands-on lessons tightly integrated with the software documentation using Sphinx. Each lesson contains multiple exercises that are supplemented with hints and complete solutions. Rather than duplicating information, students are systematically taught to work with the technical reference documentation to find the important sections for themselves. Unit tests ensure that all examples work with different software versions, and feedback buttons make it easy to submit comments for improvements.
New storage and data access solution for CMS experiment in Spain towards HL-LHC era
Authors: Dengra Carlos Pérez , Molina José Flix , Barcelona Anna Sikora 
Published: February 2023

Abstract
The Large Hadron Collider (LHC) will enter a new era for data acquisition by 2029 within the High-Luminosity Large Hadron Collider (HL-LHC) program, where the LHC will raise the proton and ion collisions up to unprecedented levels. This increase will imply a factor 10 in terms of luminosity as compared to the current values, having an impact in the way the experimental data is stored and analyzed. This work focuses on the research and development of new data cache solutions that are adopted towards the new era, studying its effects at the Spanish WLCG sites that support CMS activities, namely the PIC Tier-1 and CIEMAT Tier-2. We propose a model to access and cache the most popular CMS datasets in Spain by deploying XCache cache systems in both PIC and CIEMAT sites. This work is complemented with dedicated studies to better understand and optimize these new solutions in the region.
Progress toward Accelogic compression in ROOT
Authors: Canal Ph. , Lauret J. , Gonzalez J. , Buren G. Van , Cali I.A. , Nunez R. , Ying Y. , Burtscher M. 
Published: February 2023

Abstract
For the last 7 years, Accelogic pioneered and perfected a radically new theory of numerical computing codenamed “Compressive Computing”, which has an extremely profound impact on real-world computer science [1]. At the core of this new theory is the discovery of one of its fundamental theorems which states that, under very general conditions, the vast majority (typically between 70% and 80%) of the bits used in modern large-scale numerical computations are absolutely irrelevant for the accuracy of the end result. This theory of Compressive Computing provides mechanisms able to identify (with high intelligence and surgical accuracy) the number of bits (i.e., the precision) that can be used to represent numbers without affecting the substance of the end results, as they are computed and vary in real time. The bottom-line outcome will be to provide state-of-the-art compression algorithms --and accompanying software libraries-- able to surpass the performance of the compression engines currently available in the ROOT [7] framework. The resulting technology has the capability to enable substantial economic and operational gains (including speedup) for High Energy and Nuclear Physics data storage/analysis. In our initial studies, a factor of nearly x4 (3.9) compression was achieved with RHIC/STAR data where ROOT compression managed only x1.4 [6].
As a collaboration of experimental scientists, private industry, and the ROOT Team, our aim is to capitalize on the substantial success delivered by the initial effort and produce a robust technology properly packaged as an open-source tool that could be used by virtually every experiment around the world as means for improving data management and accessibility.
In this contribution, we will present our efforts integrating our concepts of “functionally lossless compression” within the ROOT framework implementation, with the purpose of producing a basic solution readily integrated into HENP applications. We will also present our progress applying this compression through realistic examples of analysis from both the STAR and CMS experiments.
Running the Dual-PQC GAN on noisy simulators and real quantum hardware
Authors: Chang Su Yeon , Agnew Edwin , Combarro Elías , Grossi Michele , Herbert Steven , Vallecorsa Sofia 
Published: February 2023

Abstract
In an earlier work [1], we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of quantum GAN. We applied the model on a realistic High-Energy Physics (HEP) use case: the exact theoretical simulation of a calorimeter response with a reduced problem size. This paper explores the dual-PQC GAN for a more practical usage by testing its performance in the presence of different types of quantum noise, which are the major obstacles to overcome for successful deployment using near-term quantum devices. The results propose the possibility of running the model on current real hardware, but improvements are still required in some areas.
CAGMon: A Novel Tool for Identifying and Diagnosing Coherent Associations and Causalities between Multi-channels of the Gravitational Wave Detector
Authors: Jung Piljong , Hoon Oh Sang , Kim Young-Min , Son Edwin J. , Yokozawa Takaaki , Washimi Tatsuki , Oh John J. 
Published: February 2023

Abstract
The ground-based gravitational-wave telescopes such as LIGO, Virgo, and KAGRA are very complicated and sensitive compositions of advanced devices. Therefore, they are influenced not only by the mutual interaction among mechanical and electronics systems but also by the surrounding environment. To categorize and reduce noises from many channels interconnected by such instruments and environment for achieving the detection of gravitational waves, it needs to increase a signal-to-noise ratio and reduce false alarm rate from coincident spurious events. Therefore, it is of great importance to identify associations between inter-correlated channels. In this work, we present a novel tool called CAGMon for identifying (non-) linear couplings between inter-correlated channels, which can be applied to the practical cases of the gravitational-wave detector.
CLUE: a clustering algorithm for current and future experiments
Authors: Brondolin E 
Published: February 2023

Abstract
CLUE (CLUstering of Energy) is a fast parallel clustering algorithm for High Granularity Calorimeters in High Energy Physics. In these types of detectors, the standard clustering algorithms using combinatorics are expected to fail due to large number of digitized energy deposits (hits) in the reconstruction stage bringing to a consequent memory/timing explosion. This innovative algorithm uses a grid spatial index for fast querying of neighbors and its timing scales linearly with the number of hits within the range considered. Initially CLUE was developed in a standalone repository that allows performance benchmarking with respect to its CPU and GPU implementations, demonstrating the power of algorithmic parallelization in the coming era of heterogeneous computing. CLUE has been successfully used in simulation and beam tests of the High Granularity Calorimeter to be installed for the upgrade of the CMS detector in Phase-2 of the HL-LHC. Recently CLUE was also imported in the key4hep framework and first results will be shown for detectors proposed in future collider projects.
Deep learning particle identification in LHCb RICH
Authors: Blago Michele Piero 
Published: February 2023

Abstract
The use of Ring Imaging Cherenkov detectors (RICH) offers a powerful technique for identifying the particle species in particle physics. These detectors produce 2D images formed by rings of individual photons superimposed on a background of photon rings from other particles.
The RICH particle identification (PID) is essential to the LHCb experiment at CERN. While the current PID algorithm has performed well during LHC data-taking periods between 2010 to 2018, its complexity poses a challenge for LHCb computing infrastructure upgrades towards multi-core architectures. The high particle multiplicity environment of future LHC runs strongly motivates shifting towards high-throughput computing for the online event reconstruction.
In this contribution, we introduce a convolutional neural network (CNN) approach to particle identification in LHCb RICH. The CNN takes binary input images from the two RICH detectors to classify particle species. The input images are polar-transformed sub-sections of the RICH photon-detection planes. The model is hyperparameter-optimised and trained on classification accuracy with simulated collision data for the upcoming LHC operation starting in 2022. The PID performance of the CNN is comparable to the conventional algorithm, and its simplicity renders it suitable for fast online reconstruction through parallel processing.
We show that under conditions of reduced combinatorial background, as expected from the introduction of timing resolution to the RICH detectors in future upgrades, the network achieves a particle identification performance close to 100 %, with simultaneous misclassification of the most prevalent particle species approaching 0 %.
Deep learning techniques for energy clustering in the CMS ECAL
Authors: Valsecchi Davide 
Published: February 2023

Abstract
The reconstruction of electrons and photons in CMS depends on topological clustering of the energy deposited by an incident particle in different crystals of the electromagnetic calorimeter (ECAL). These clusters are formed by aggregating neighbouring crystals according to the expected topology of an electromagnetic shower in the ECAL. The presence of upstream material (beampipe, tracker and support structures) causes electrons and photons to start showering before reaching the calorimeter. This effect, combined with the 3.8T CMS magnetic field, leads to energy being spread in several clusters around the primary one. It is essential to recover the energy contained in these satellite clusters in order to achieve the best possible energy resolution for physics analyses. Historically satellite clusters have been associated to the primary cluster using a purely topological algorithm which does not attempt to remove spurious energy deposits from additional pileup interactions (PU). The performance of this algorithm is expected to degrade during LHC Run 3 (2022+) because of the larger average PU levels and the increasing levels of noise due to the ageing of the ECAL detector. New methods are being investigated that exploit state-of-the-art deep learning architectures like Graph Neural Networks (GNN) and self-attention algorithms. These more sophisticated models improve the energy collection and are more resilient to PU and noise, helping to preserve the electron and photon energy resolution achieved during LHC Runs 1 and 2. This work will cover the challenges of training the models as well the opportunity that this new approach offers to unify the ECAL energy measurement with the particle identification steps used in the global CMS photon and electron reconstruction.
An event display for the Transition Radiation Detector in ALICE
Authors: Perumal S , Dietel T , Kuttel M M 
Published: February 2023

Abstract
Particle collider experiments generate huge volumes of complex data, and event displays provide a useful visual representation to accelerate the learning process towards physics results. Event displays are also used to verify expected behaviour, identify anomalous data, or explain important results. They often have a steep learning curve, a high barrier to entry, or are tightly bound to a specific environment. The upgrade of ALICE in preparation for Run 3 of the LHC requires modifications to existing event displays. We present here a cross-platform, browser-based event display, focused on interactive 2-dimensional projections of collision data from ALICE, specifically focused on the operation of the Transition Radiation Detector (TRD). It is driven by a flexible intermediate JSON data format suitable for web-based displays, and a generic task to convert existing data acquired in previous runs to this format. The relationship between raw and reconstructed data in the TRD is illustrated through a novel pairing of raw and reconstructed data in a unified interactive view. A formal design study methodology was used to guide these choices, and the display was evaluated by both scientists and the public, through a series of case studies.