bluuebunny/crossref_metadata_2025 huggingface dataset:
EOS deployment on Ceph RBD/CephFS with K8s
Authors: Fornari Federico , Costantini Alessandro , Cavalli Alessandro , Cesini Daniele , Duma Doina Cristina , Falabella Antonio , Fattibene Enrico , Mascetti Luca , Morganti Lucia , Peters Andreas-Joachim , Prosperini Andrea , Sapunenko Vladimir
Published: February 2023

The present activity focused on the integration of different storage systems (EOS [1] and Ceph [2]) with the aim to combine the high level scalability and stability of EOS services with the reliability and redundancy features provided by Ceph. The work has been carried out as part of the collaboration between the national center of INFN (Italian Institute for Nuclear Physics) dedicated to Research and Development on Information and Communication Technologies and the Conseil Européen pour la Recherche Nucléaire (CERN), with the goal of evaluating and testing different technologies for next-generation storage challenges. This work leverages the well-known open-source container orchestration system, Kubernetes [3], for managing file system services. The results obtained by measuring the performances of the different combined technologies, comparing for instance block device and file system as backend options provided by a Ceph cluster deployed on physical machines, are shown and discussed in the manuscript.
Evaluation and Implementation of Various Persistent Storage Options for CMSWEB Services in Kubernetes Infrastructure at CERN
Authors: Imran Muhammad , Kuznetsov Valentin , Paparrigopoulos Panos , Trigazis Spyridon , Pfeiffer Andreas
Published: February 2023

This paper summarizes the various storage options that we implemented for the CMSWEB cluster in Kubernetes infrastructure. All CMSWEB services require storage for logs, while some services also require storage for data. We also provide a feasibility analysis of various storage options and describe the pros/cons of each technique from the perspective of the CMSWEB cluster and its users. In the end, we also propose recommendations according to the service needs. The first option is the CephFS which can be mounted multiple times across various clusters and VMs and works very well with k8s. We use it both for data and the logs. The second option is the Cinder volume. It is the block storage that runs the filesystem on top of it. It can only be attached to one instance at a time. We use this option only for the data. The third option is S3 storage. It is object storage that offers a scalable storage service that can be used by applications compatible with the Amazon S3 protocol. It is used for the logs. For S3, we explored two mechanisms. For the first scenario, we consider fluentd that runs as a sidecar container in the service pods and sends logs to S3 bucket. For the second scenario, we considered filebeat that runs as a sidecar container in the service pod and scaps those logs to fluentd which runs as a daemonset in each node and sends those logs to S3 in the end. The fourth option is EOS. We configured EOS inside the pods of the CMSWEB services. The fifth option that we explored is to use dedicated VMs that have Ceph volume attached to them. In EOS and VM, the logs from the service pods are sent to EOS/VM using the rsync approach. The last option is to send service logs to Elasticsearch. It has been implemented using fluentd that runs as a daemonset in each node. In parallel to the sending logs to S3 fluentd also sends those logs to the Elasticsearch infrastructure at CERN.
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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
A First Application of Collaborative Learning In Particle Physics
Authors: Vergani Stefano , Bagoly Attila
Published: February 2023

Over the last ten years, the popularity of Machine Learning (ML) has grown exponentially in all scientific fields, including particle physics. The industry has also developed new powerful tools that, imported into academia, could revolutionise research. One recent industry development that has not yet come to the attention of the particle physics community is Collaborative Learning (CL), a framework that allows training the same ML model with different datasets. This work explores the potential of CL, testing the library Colearn with neutrino physics simulation. Colearn, developed by the British Cambridge-based firm Fetch.AI, enables decentralised machine learning tasks. Being a blockchain-mediated CL system, it allows multiple stakeholders to build a shared ML model without needing to rely on a central authority. A generic Liquid Argon Time-Projection Chamber (LArTPC) has been simulated and images produced by fictitious neutrino interactions have been used to produce several datasets. These datasets, called learners, participated successfully in training a Deep Learning (DL) Keras model using blockchain technologies in a decentralised way. This test explores the feasibility of training a single ML model using different simulation datasets coming from different research groups. In this work, we also discuss a framework that instead makes different ML models compete against each other on the same dataset. The final goal is then to train the most performant ML model across the entire scientific community for a given experiment, either using all of the datasets available or selecting the model which performs best among every model developed in the community.
GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response
Authors: Rogachev Alexander , Ratnikov Fedor
Published: February 2023

High energy physics experiments essentially rely on simulated data for physics analyses. However, running detailed simulation models requires a tremendous amount of computation resources. New approaches to speed up detector simulation are therefore needed. The generation of calorimeter responses is often the most expensive component of the simulation chain for HEP experiments. It was shown that deep learning techniques, especially Generative Adversarial Networks, may be used to reproduce the calorimeter response. However, those applications are challenging, as the generated responses need evaluation not only in terms of image consistency: different physics-based quality metrics should be also taken into consideration.

In our work, we develop a multitask GAN-based framework with the goal to speed up the response generation of the Electromagnetic Calorimeter (ECAL) of the LHCb detector at LHC. We introduce the Auxiliary Regressor as a second task to evaluate a proxy metric of the given input that is used by the Discriminator of the GAN. We show that this approach improves the stability of GAN and the model produces samples with better physics distributions.
Generating muonic force carriers events with classical and quantum neural networks
Authors: Ramazyan T. , Kiss O. , Grossi M. , Kajomovitz E. , Vallecorsa S.
Published: February 2023

Generative models (GM) are promising applications for near-term quantum computers due to the probabilistic nature of quantum mechanics. This work compares a classical conditional generative adversarial network (CGAN) with a quantum circuit Born machine while addressing their strengths and limitations to generate muonic force carriers (MFCs) events. The former uses a neural network as a discriminator to train the generator, while the latter takes advantage of the stochastic nature of measurements in quantum mechanics to generate samples. We consider a muon fixed-target collision between muons produced at the high-energy collisions of the LHC and the detector material of the ForwArd Search ExpeRiment (FASER) or the ATLAS calorimeter. In the ATLAS case, independent muon measurements performed by the inner detector (ID) and muon system (MS) can help observe new force carriers coupled to muons, which are usually not detected. We numerically observed that CGANs could reproduce the complete data set and interpolate to different regimes. Moreover, we show on a simplified problem that Born machines are promising generative models for near-term quantum devices.
Hyperparameter optimization of data-driven AI models on HPC systems
Authors: Wulff Eric , Girone Maria , Pata Joosep
Published: February 2023

In the European Center of Excellence in Exascale Computing ”Research on AI- and Simulation-Based Engineering at Exascale” (CoE RAISE), researchers develop novel, scalable AI technologies towards Exascale. This work exercises High Performance Computing resources to perform large-scale hyperparameter optimization using distributed training on multiple compute nodes. This is part of RAISE’s work on data-driven use cases which leverages AI- and HPC cross-methods developed within the project. In response to the demand for parallelizable and resource efficient hyperparameter optimization methods, advanced hyperparameter search algorithms are benchmarked and compared. The evaluated algorithms, including Random Search, Hyperband and ASHA, are tested and compared in terms of both accuracy and accuracy per compute resources spent. As an example use case, a graph neural network model known as MLPF, developed for Machine Learned Particle-Flow reconstruction, acts as the base model for optimization. Results show that hyperparameter optimization significantly increased the performance of MLPF and that this would not have been possible without access to large-scale High Performance Computing resources. It is also shown that, in the case of MLPF, the ASHA algorithm in combination with Bayesian optimization gives the largest performance increase per compute resources spent out of the investigated algorithms.
Interrelation of equivariant Gaussian processes and convolutional neural networks
Authors: Demichev Andrey , Kryukov Alexander
Published: February 2023

Currently there exists rather promising new trend in machine leaning (ML) based on the relationship between neural networks (NN) and Gaussian processes (GP), including many related subtopics, e.g., signal propagation in NNs, theoretical derivation of learning curve for NNs, QFT methods in ML, etc. An important feature of convolutional neural networks (CNN) is their equivariance (consistency) with respect to the symmetry transformations of the input data. In this work we establish a relationship between the many-channel limit for CNNs equivariant with respect to two-dimensional Euclidean group with vector-valued neuron activations and the corresponding independently introduced equivariant Gaussian processes (GP).
Leveraging HPC resources with distributed RDataFrame
Authors: Padulano V. E. , Kabadzhov I. D. , Saavedra E. T. , Guiraud E.
Published: February 2023

The declarative approach to data analysis provides high-level abstractions for users to operate on their datasets in a much more ergonomic fashion compared to imperative interfaces. ROOT offers such a tool with RDataFrame, which has been tested in production environments and used in real-world analyses with optimal results. Its programming model acts by creating a computation graph with the operations issued by the user and executing it lazily only when the final results are queried. It has always been oriented towards parallelisation, with native support for multi-thread execution on a single machine. Recently, RDataFrame has been extended with a Python layer that is capable of steering and executing the RDataFrame computation graph over a set of distributed resources. In addition, such a layer requires minimal code changes for an RDataFrame application to run distributedly. The new tool effectively allows running a C++ event loop based on RDataFrame while leveraging common industry tools like Dask to schedule the usage of resources. This work presents results and insights gathered through the distributed RDataFrame tool running a physics analysis connecting multiple nodes with a Dask scheduler that requests resources from a Slurm cluster.
neos: End-to-End-Optimised Summary Statistics for High Energy Physics
Authors: Simpson Nathan , Heinrich Lukas
Published: February 2023

The advent of deep learning has yielded powerful tools to automatically compute gradients of computations. This is because training a neural network equates to iteratively updating its parameters using gradient descent to find the minimum of a loss function. Deep learning is then a subset of a broader paradigm; a workflow with free parameters that is end-to-end optimisable, provided one can keep track of the gradients all the way through.

This work introduces neos: an example implementation following this paradigm of a fully differentiable high-energy physics workflow, capable of optimising a learnable summary statistic with respect to the expected sensitivity of an analysis. Doing this results in an optimisation process that is aware of the modelling and treatment of systematic uncertainties.
A neural-network-defined Gaussian mixture model for particle identification applied to the LHCb fixed-target programme
Authors: Mariani S , Anderlini L , Di Nezza P , Franzoso E , Graziani G , Pappalardo L L
Published: February 2023

Particle identification at high-energy physics experiments typically relies on classifiers combining different experimental observables. In this document, an innovative approach employing machine learning techniques to describe their dependence from the relevant features is presented. The proposed method is applied to the fixed-target programme at the LHCb experiment, where the sample size of the particle identification calibration channels affects the experimental performance. It is demonstrated to perform better than a model based on the LHCb detailed simulation and to be fast and suitable to a large variety of use cases.
New software technologies in the LHCb Simulation
Authors: Corti Gloria , Davis Adam , Kreps Michał , Mazurek Michal , Popov Dmitry , Gianluca Siddi Benedetto
Published: February 2023

Monte Carlo simulations are key to the design and commissioning of new detectors as well as the interpretation of physics measurements. The amount of simulated samples required for the Run 3 physics program of the LHCb experiment will increase significantly from 2022 onward to match the increase in the amount of data collected with respect to Run 1 and 2 operation. A new version of the LHCb Gauss simulation framework has been developed to better accommodate new simulation techniques and software technologies to produce the necessary samples within the computing resources allocated for the next few years. It provides the LHCb specific functionality while the generic simulation infrastructure has been encapsulated in an experiment-independent framework, GAUSSINO. The latter combines the GAUDI core software framework and the GEANT4 simulation toolkit and fully exploits their multi-threading capabilities. Fast simulation interface is the latest feature being developed in GAUSSINO to interact with GEANT4 giving the possibility of replacing its detailed description of physics processes with an extensive palette of fast simulation models for a specific LHCb sub-detector. A facility to ease the production of training datasets for fast simulations models has also been introduced.
New web based event data and geometry visualization for LHCb
Authors: Pappas A , Couturier B , Ponce S
Published: February 2023

The LHCb detector is undergoing a comprehensive upgrade for data taking in the LHC’s Run 3, which is scheduled to begin in 2022. The new Run 3 detector has a different, upgraded geometry and uses new tools for its description, namely DD4hep [2] and ROOT [12]. Besides, the visualization technologies have evolved quite a lot since Run 1, with the introduction of ubiquitous web based solutions or Augmented Reality (AR) for example. The LHCb collaboration has thus started the development of a new visualization solution, based on the Phoenix [6] framework, developed jointly by several experiments in the context of the HEP Software Foundation (HSF) [10]. We present here the architecture and implementation of this new solution, as well as the different contributions made to the Phoenix ecosystem. In particular we discuss a generic tool for exporting ROOT geometries to the visualization application, which can be used to display in a browser either the whole detector or subparts of it. Extensions to the Phoenix visualization primitives regarding calorimeters and performance improvements are also presented.
The Open Data Detector Tracking System
Authors: Gessinger-Befurt Paul , Salzburger Andreas , Niermann Joana
Published: February 2023

Charged particle reconstruction in High Energy Physics experiments is a significant part of overall event reconstruction. Depending on the physics environment, for instance in collider experiments with high multiplicities or luminosities, the tracking problem increases in complexity and often poses not only an algorithmic, but also a computational challenge. With the high-luminosity phase of the LHC at CERN approaching, research for new approaches and algorithms for track reconstruction has seen an increased interest. Both new technological approaches like hardware accelerators, as well as machine learning are being developed. However, testing and developing these new approaches against the existing experiments’ software stacks can prove to be challenging, as they typically focus on stable data taking, discouraging disruptive changes. This document presents a virtual tracking detector that is designed to be a simplified, but realistic model of a real-world detector, that can serve as a robust testbed for new developments.
Particle identification with the Belle II calorimeter using machine learning
Authors: Narimani Charan Abtin
Published: February 2023

I present an application of a convolutional neural network (CNN) to separate muons and pions in the Belle II electromagnetic calorimeter (ECL). The ECL is designed to measure the energy deposited by charged and neutral particles. It also provides important contributions to the particle identification (PID) system. Identification of low-momenta muons and pions in the ECL is crucial if they do not reach the outer muon detector. Track-seeded cluster energy images provide the maximal possible information. The shape of the energy depositions for muons and pions in the crystals around an extrapolated track at the entering point of the ECL is used together with crystal positions in θ − ϕ plane and transverse momentum of the track to train a CNN. The CNN exploits the difference between the dispersed energy depositions from pion hadronic interactions and the more localized muon electromagnetic interactions. Using simulation, the performance of the CNN algorithm is compared with other PID methods at Belle II which are based on track-matched clustering information. The results show that the CNN PID method improves muon-pion separation in low momentum.
Precise simulation of drift chamber in the CEPC experiment
Authors: Fang Wenxing , Huang Xingtao , Li Weidong , Lin Tao , Liu Mengyao , Yuan Ye , Zhang Xueyao , Zhang Yao
Published: February 2023

The Circular Electron Positron Collider (CEPC) is one of the future experiments which aims to precisely study the properties of the Higgs boson and to search for new physics beyond the Standard Model. To achieve that, it puts strict requirements on the particle reconstruction and identification ability of the CEPC detector. In order to obtain better particle identification performance, the drift chamber is chosen for the outer tracking detector which can provide information used by the cluster counting method for getting excellent particle identification performance. To evaluate the performance of the cluster counting method, it is necessary to develop a simulation tool that can be used to precisely simulate the response of the gaseous particle detector. This work proposes a new way to combine Geant4 and Garfield++ at the Geant4 step level for the drift chamber simulation. Due to the extreme time consumption of simulating the avalanche process in Garfield++, a fast signal response simulation method based on a neural network has been developed to replace detailed simulation. It shows the fast simulation can give consistent results with Garfield++ simulation and achieve the speedup by a factor of 200.
Probing highly collimated photon-jets with deep learning
Authors: Ai Xiaocong , Hsu Shih-Chieh , Li Ke , Lu Chih-Ting
Published: February 2023

Many extensions of the standard model (SM) predict the existence of axion-like particles and/or dark Higgs in the sub-GeV scale. Two new sub-GeV particles, a scalar and a pseudoscalar, produced through the Higgs boson exotic decays, are investigated. The decay signatures of these two new particles with highly collimated photons in the final states are discriminated from the ones of SM backgrounds using the Convolutional Neural Networks and Boosted Decision Trees techniques. The sensitivities of searching for such new physics signatures at the Large Hadron Collider are obtained.
RNTuple performance: Status and Outlook
Authors: Lopez-Gomez Javier , Blomer Jakob
Published: February 2023

Upcoming HEP experiments, e.g. at the HL-LHC, are expected to increase the volume of generated data by at least one order of magnitude. In order to retain the ability to analyze the influx of data, full exploitation of modern storage hardware and systems, such as low-latency high-bandwidth NVMe devices and distributed object stores, becomes critical.

To this end, the ROOT RNTuple I/O subsystem has been designed to address performance bottlenecks and shortcomings of ROOT’s current state of the art TTree I/O subsystem. RNTuple provides a backwards-incompatible redesign of the TTree binary format and access API that evolves the ROOT event data I/O for the challenges of the upcoming decades. It focuses on a compact data format, on performance engineering for modern storage hardware, for instance through making parallel and asynchronous I/O calls by default, and on robust interfaces that are easy to use correctly.

In this contribution, we evaluate the RNTuple performance for typical HEP analysis tasks. We compare the throughput delivered by RNTuple to popular I/O libraries outside HEP, such as HDF5 and Apache Parquet. We demonstrate the advantages of RNTuple for HEP analysis workflows and provide an outlook on the road to its use in production.
Self-Organizing Maps in High Energy Physics
Authors: Habermann Kai , Toerne Eckhard von
Published: February 2023

The Self-Organizing-Map (SOM) is a widely used neural network for dimensional reduction and clustering. It has yet to find its use in high energy physics. This paper discusses two applications of SOM: first, we find map regions with a high relative content of a rare process (H → WW*). Second we obtain Monte Carlo normalization factors for different physics processes by fitting the dimensionally reduced representation. Analysis and training are performed on ATLAS open data.
Simulation and Software Integration of the CMS MIP Timing Detector for High-Luminosity LHC
Authors: Sorrentino Giulia
Published: February 2023

The Compact Muon Solenoid detector at the Large Hadron Collider is undertaking an upgrade program in order to face the harsh conditions foreseen by the High-Luminosity era. This program comprises the installation of a new timing detector whose aim is to measure the time of MIPs, the minimum ionizing particles, with a resolution of around 30-40 ps. The time information provided by this new MIP Timing Detector will improve the rejection of spurious tracks and vertices, will enable particle identification based on the time of flight, and will bring unique physics opportunities for interesting signatures such as those including long-lived particles. All these capabilities require a full software infrastructure to simulate the timing detector and its digitization process, for locally reconstructing the time information associated with tracks, propagating it to the beam line, and contributing to the vertex building. In this paper, the main characteristics of this infrastructure and its integration into the offline software chain are discussed.