Conveners
Track 1: Computing Technology for Physics Research
- Gordon Watts (University of Washington (US))
- Vladimir Loncar (Massachusetts Inst. of Technology (US))
Track 1: Computing Technology for Physics Research: Quantum Computing
- Vladimir Loncar (Massachusetts Inst. of Technology (US))
- Herschel Chawdry (University of Oxford)
Track 1: Computing Technology for Physics Research
- Vincenzo Eduardo Padulano (CERN)
- Philippe Canal (Fermi National Accelerator Lab. (US))
Track 1: Computing Technology for Physics Research: Quantum Computing
- Vladimir Loncar (Massachusetts Inst. of Technology (US))
- Herschel Chawdry (University of Oxford)
Track 1: Computing Technology for Physics Research
- Florine de Geus (CERN/University of Twente (NL))
- Benedikt Hegner (CERN)
Track 1: Computing Technology for Physics Research
- Vladimir Loncar (Massachusetts Inst. of Technology (US))
- Philippe Canal (Fermi National Accelerator Lab. (US))
Track 1: Computing Technology for Physics Research
- Philippe Canal (Fermi National Accelerator Lab. (US))
- Benedikt Hegner (CERN)
Track 1: Computing Technology for Physics Research
- Benedikt Hegner (CERN)
- Vladimir Loncar (Massachusetts Inst. of Technology (US))
Track 1: Computing Technology for Physics Research
- Benedikt Hegner (CERN)
- Vladimir Loncar (Massachusetts Inst. of Technology (US))
The Jiangmen Underground Neutrino Observatory (JUNO) is a multipurpose neutrino experiment. JUNO will start to take data in the fall of 2024 with 2PB data each year. It is important that raw data is copied to permanent storage and distributed to multiple data center storage system in time for backup. To make available for re-reconstruction among these data centers, raw data also need to be...
Quantum technologies are moving towards the development of novel hardware devices
based on quantum bits (qubits). In parallel to the development of quantum devices, efficient simulation tools are needed in order to design and benchmark quantum algorithms and applications before deployment on quantum hardware.
In this context, we present a first attempt to perform circuit-based quantum...
The growing complexity of high energy physics analysis often involves running a large number of different tools. This demands a multi-step data processing approach, with each step requiring different resources and carrying dependencies on preceding steps. It’s important and useful to have a tool to automate these diverse steps efficiently.
With the Production and Distributed Analysis (PanDA)...
High-energy physics relies on large and accurate samples of simulated events, but generating these samples with GEANT4 is CPU intensive. The ATLAS experiment has employed generative adversarial networks (GANs) for fast shower simulation, which is an important approach to solving the problem. Quantum GANs, leveraging the advantages of quantum computing, have the potential to outperform standard...
As the scientific community continues to push the boundaries of computing capabilities, there is a growing responsibility to address the associated energy consumption and carbon footprint. This responsibility extends to the Worldwide LHC Computing Grid (WLCG), encompassing over 170 sites in 40 countries, supporting vital computing, disk, and tape storage for LHC experiments. Ensuring efficient...
In High-Energy Physics (HEP) experiments, each measurement apparatus exhibit a unique signature in terms of detection efficiency, resolution, and geometric acceptance. The overall effect is that the distribution of each observable measured in a given physical process could be smeared and biased. Unfolding is the statistical technique employed to correct for this distortion and restore the...
Implementing a physics data processing application is relatively straightforward with the use of current containerization technologies and container image runtime services, which are prevalent in most high-performance computing (HPC) environments. However, the process is complicated by the challenges associated with data provisioning and migration, impacting the ease of workflow migration and...
The development of quantum computers as tools for computation and data analysis is continually increasing, even in the field of machine learning, where numerous routines and algorithms have been defined, leveraging the high expressiveness of quantum systems to process information. In this context, one of the most stringent limitations is represented by noise. In fact, the devices currently...
Thomas Jefferson National Accelerator Facility (JLab) has partnered with Energy Sciences Network (ESnet) to define and implement an edge to compute cluster data processing computational load balancing architecture. The ESnet-JLab FPGA Accelerated Transport (EJFAT) architecture focuses on FPGA acceleration to address compression, fragmentation, UDP packet destination redirection (Network...
Artificial intelligence has been used for the real and fake art identification and different machine learning models are being trained then employed with acceptable accuracy in classifying artworks. As the future revolutionary technology, quantum computing opens a grand new perspective in the art area. Using Quantum Machine Learning (QML), the current work explores the utilization of Normal...
The ATLAS experiment at CERN’s Large Hadron Collider has been using ROOT TTree for over two decades to store all of its processed data. The ROOT team has developed a new I/O subsystem, called RNTuple, that will replace TTree in the near future. RNTuple is designed to adopt various technological advancements that happened in the last decade and be more performant from both the computational and...
The rise of parallel computing, in particular graphics processing units (GPU), and machine learning and artificial intelligence has led to unprecedented computational power and analysis techniques. Such technologies have been especially fruitful for theoretical and experimental physics research where the embarrassingly parallel nature of certain workloads — e.g., Monte Carlo event generation,...
Inspired by over 25 years of experience with the ROOT TTree I/O subsystem and motivated by modern hard- and software developments as well as an expected tenfold data volume increase with the HL-LHC, RNTuple is currently being developed as ROOT's new I/O subsystem. Its first production release is foreseen for late 2024, and various experiments have begun working on the integration of RNTuple...
As the High-Luminosity LHC era is approaching, the work on the next-generation ROOT I/O subsystem, embodied by the RNTuple, is advancing fast with demonstrated implementations of the LHC experiments' data models and clear performance improvements over the TTree. Part of the RNTuple development is to guarantee no change in the RDataFrame analysis flow despite the change in the underlying data...
Over the last 20 years, thanks to the development of quantum technologies, it has
been possible to deploy quantum algorithms and applications that before were only
accessible through simulation on real quantum hardware.
The current devices available are often referred to as noisy intermediate-scale
quantum (NISQ) computers, and they require calibration routines
in order to obtain...
As the role of High Performance Computers (HPC) increases in the High Energy Physics (HEP) experiments, the experiments will have to adopt HPC friendly storage format and data models to efficiently utilize these resources. In its first phase, the HEP-Center for Computational Excellence (HEP-CCE) has demonstrated that the complex HEP data products can be stored in the HPC native storage...
In recent years, the scope of applications for Machine Learning, particularly Artificial Neural Network algorithms, has experienced an exponential expansion. This surge in versatility has uncovered new and promising avenues for enhancing data analysis in experiments conducted at the Large Hadron Collider at CERN. The integration of these advanced techniques has demonstrated considerable...
Recently, machine learning has established itself as a valuable tool for researchers to analyze their data and draw conclusions in vari- ous scientific fields, such as High Energy Physics (HEP). Commonly used machine learning libraries, such as Keras and PyTorch, might provide functionality for inference, but they only support their own models, are constrained by heavy dependencies and often...
Track reconstruction is an essential element of modern and future collider experiments, including within the ATLAS detector. The HL-LHC upgrade of the ATLAS detector brings an unprecedented tracking challenge, both in terms of number of silicon hit cluster readouts, and throughput required for both high level trigger and offline track reconstruction. Traditional track reconstruction techniques...
The LHCb experiment at the Large Hadron Collider (LHC) is designed to perform high-precision measurements of heavy-hadron decays, which requires the collection of large data samples and a good understanding and suppression of multiple background sources. Both factors are challenged by a five-fold increase in the average number of proton-proton collisions per bunch crossing, corresponding to a...
The Fair Universe project is building a large-compute-scale AI ecosystem for sharing datasets, training large models and hosting challenges and benchmarks. Furthermore, the project is exploiting this ecosystem for an AI challenge series focused on minimizing the effects of systematic uncertainties in High-Energy Physics (HEP), and on predicting accurate confidence intervals. This talk will...
GPUs have become the dominant source of computing power for HPCs and are increasingly being used across the High Energy Physics computing landscape for a wide variety of tasks. Though NVIDIA is currently the main provider of GPUs, AMD and Intel are rapidly increasing their market share. As a result, programming using a vendor-specific language such as CUDA can significantly reduce deployment...
The CMSSW framework has been instrumental in data processing, simulation, and analysis for the CMS detector at CERN. It is expected to remain a key component of the CMS Offline Software for the foreseeable future. Consequently, CMSSW is under continuous development, with its integration system evolving to incorporate modern tools and keep pace with the latest software improvements in the High...
High Performance Computing resources are increasingly prominent in the plans of funding agencies, and the tendency of these resources is now to rely primarily on accelerators such as GPUs for the majority of their FLOPS. As a result, High Energy Physics experiments must make maximum use of these accelerators in our pipelines to ensure efficient use of the resources available to us.
The...
In the realm of scientific computing, both Julia and Python have established themselves as powerful tools. Within the context of High Energy Physics (HEP) data analysis, Python has been traditionally favored, yet there exists a compelling case for migrating legacy software to Julia.
This talk focuses on language interoperability, specifically exploring how Awkward Array data structures can...
Detector studies for future experiments rely on advanced software tools to estimate performance and optimize their design and technology choices. Similarly, machine learning techniques require realistic data sets that allow estimating their performance beyond simplistic toy-models. The Key4hep software stack provides tools to perform detailed full simulation studies for a number of different...
To increase the number of Monte Carlo simulated events that can be produced with the limited CPU resources available, the ATLAS experiment at CERN uses a variety of fast simulation tools in addition to the detailed simulation of the detector response with Geant4. The tools are deployed in a heterogeneous simulation infrastructure known as the Integrated Simulation Framework (ISF), which was...
Recently, transformers have proven to be a generalised architecture for various data modalities, i.e., ranging from text (BERT, GPT3), time series (PatchTST) to images (ViT) and even a combination of them (Dall-E 2, OpenAI Whisper). Additionally, when given enough data, transformers can learn better representations than other deep learning models thanks to the absence of inductive bias, better...
The ATLAS experiment at the LHC heavily depends on simulated event samples produced by a full Geant4 detector simulation. This Monte Carlo (MC) simulation based on Geant4 was a major consumer of computing resources during the 2018 data-taking year and is anticipated to remain one of the dominant resource users in the HL-LHC era. ATLAS has continuously been working to improve the computational...
The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows us to look for rare deviations that can be due to new phenomena not previously observed. We show that novel machine learning algorithms, specifically Normalizing Flows and Flow Matching, can be effectively...
Recent advancements in track finding within the challenging environments expected in the High-Luminosity Large Hadron Collider (HL-LHC) have showcased the potential of Graph Neural Network (GNN)-based algorithms. These algorithms exhibit high track efficiency and reasonable resolutions, yet their computational burden on CPUs hinders real-time processing, necessitating the integration of...
The scientific program of the future FAIR accelerator covers a broad spectrum of topics in modern nuclear and atomic physics. This diversity leads to a multitude of use cases and workflows for the analysis of experimental data and simulations. To meet the needs of such a diverse user group, a flexible and transparent High-Performance Computing (HPC) system is required to accommodate all FAIR...
With the increasing usage of Machine Learning (ML) in High Energy Physics (HEP), the breadth of new analyses with a large spread in compute resource requirements, especially when it comes to GPU resources. For institutes, like the Karlsruhe Institute of Technology (KIT), that provide GPU compute resources to HEP via their batch systems or the Grid, a high throughput, as well as energy...
The NA61/SHINE experiment is a prominent venture in high-energy physics, located at the SPS accelerator within CERN. Recently, the experiment's physics program has been extended, which necessitated the upgrade of detector hardware and software for new physics purposes.
The upgrade included a fundamental modification of the readout electronics (front-end) in the detecting system core of the...
AI generative models, such as generative adversarial networks (GANs), variational auto-encoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific simulations, such as Geant4. However, they have several drawbacks such as training instability and unable to cover the entire data distribution especially for the region where data are...
FASER, the ForwArd Search ExpeRiment, is an LHC experiment located 480 m downstream of the ATLAS interaction point along the beam collision axis. FASER has been taking collision data since the start of LHC Run3 in July 2022. The first physics results were presented in March 2023 [1,2], including the first direct observation of collider neutrinos. FASER includes four identical tracker stations...
The ATLAS experiment at CERN will be upgraded for the "High Luminosity LHC", with collisions due to start in 2029. In order to deliver an order of magnitude more data than previous LHC runs, 14 TeV protons will collide with an instantaneous luminosity of up to 7.5 x 10e34 cm^-2s^-1, resulting in higher pileup and data rates. This increase brings new requirements and challenges for the trigger...
The ATLAS trigger system will be upgraded for the Phase 2 period of LHC operation. This system will include a Level-0 (L0) trigger based on custom electronics and firmware, and a high-level software trigger running on off-the-shelf hardware. The upgraded L0 trigger system uses information from the calorimeters and the muon trigger detectors. Once information from all muon trigger sectors has...
Since 2022, the LHCb detector is taking data with a full software trigger at the LHC proton-proton collision rate, implemented in GPUs in the first stage and CPUs in the second stage. This setup allows to perform the alignment & calibration online and to perform physics analyses directly on the output of the online reconstruction, following the real-time analysis paradigm.
This talk will give...
Finding track segments downstream of the magnet is an important and computationally expensive task, that LHCb has recently ported to the first stage of its new GPU-based trigger of the LHCb Upgrade I. These segments are essential to form all good physics tracks with precision momentum measurement, when combined with those reconstructed in the vertex track detector, and to reconstruct...
