Conveners
Parallel (Track 3): Offline Computing
- Rosen Matev (CERN)
- Davide Valsecchi (ETH Zurich (CH))
Parallel (Track 3): Offline Computing
- Charis Kleio Koraka (University of Wisconsin Madison (US))
- Laura Cappelli (INFN Ferrara)
Parallel (Track 3): Offline Computing
- Rosen Matev (CERN)
- Davide Valsecchi (ETH Zurich (CH))
Parallel (Track 3): Offline Computing
- Davide Valsecchi (ETH Zurich (CH))
- Laura Cappelli (INFN Ferrara)
Parallel (Track 3): Offline Computing
- Rosen Matev (CERN)
- Charis Kleio Koraka (University of Wisconsin Madison (US))
Parallel (Track 3): Offline Computing
- Davide Valsecchi (ETH Zurich (CH))
- Rosen Matev (CERN)
Parallel (Track 3): Offline Computing
- Charis Kleio Koraka (University of Wisconsin Madison (US))
- Laura Cappelli (INFN Ferrara)
Parallel (Track 3): Offline Computing
- Charis Kleio Koraka (University of Wisconsin Madison (US))
- Rosen Matev (CERN)
Parallel (Track 3): Offline Computing
- Davide Valsecchi (ETH Zurich (CH))
- Laura Cappelli (INFN Ferrara)
Description
Offline Computing
RNTuple is the new columnar data format designed as the successor to ROOT's TTree format. It allows to make use of modern hardware capabilities and is expected to be used in production by the LHC experiments during the HL-LHC. In this contribution, we will discuss the usage of Direct I/O to fully exploit modern SSDs, especially in the context of the recent addition of parallel RNTuple writing....
Tracking charged particles in high-energy physics experiments is a computationally intensive task. With the advent of the High Luminosity LHC era, which is expected to significantly increase the number of proton-proton interactions per beam collision, the amount of data to be analysed will increase dramatically. As a consequence, local pattern recognition algorithms suffer from scaling...
Machine Learning (ML)-based algorithms play increasingly important roles in almost all aspects of the data analyses in ATLAS. Diverse ML models are used in detector simulations, event reconstructions, and data analyses. They are being deployed in the ATLAS software framework, Athena. The primary approach to perform ML inference in Athena is to use the ONNXRuntime. However, some ML models could...
With the future high-luminosity LHC era fast approaching high-energy physics faces large computational challenges for event reconstruction. Employing the LHCb vertex locator as our case study we are investigating a new approach for charged particle track reconstruction. This new algorithm hinges on minimizing an Ising-like Hamiltonian using matrix inversion. Performing this matrix inversion...
The KM3NeT collaboration is constructing two underwater neutrino detectors in the Mediterranean Sea sharing the same technology: the ARCA and ORCA detectors. ARCA is optimized for the observation of astrophysical neutrinos, while ORCA is designed to determine the neutrino mass hierarchy by detecting atmospheric neutrinos. Data from the first deployed detection units are being analyzed and...
The Super Tau Charm Facility (STCF) is a future electron-positron collider proposed with a center-of-mass energy ranging from 2 to 7 GeV and a peak luminosity of 0.5$\times10^{35}$ ${\rm cm}^{-2}{\rm s}^{-1}$. In STCF, the identification of high-momentum hadrons is critical for various physics studies, therefore two Cherenkov detectors (RICH and DTOF) are designed to boost the PID...
Noisy intermediate-scale quantum (NISQ) computers, while limited by imperfections and small scale, hold promise for near-term quantum advantages in nuclear and high-energy physics (NHEP) when coupled with co-designed quantum algorithms and special-purpose quantum processing units.
Developing co-design approaches is essential for near-term usability, but inherent challenges exist due to the...
The High Energy cosmic-Radiation Detection facility (HERD) is a scientific instrument planned for deployment on the Chinese Space Station, aimed at indirectly detecting dark matter and conducting gamma-ray astronomical research. HERD Offline Software (HERDOS) is developed for the HERD offline data processing, including Monte Carlo simulation, calibration, reconstruction and physics analysis...
Quantum computing can empower machine learning models by enabling kernel machines to leverage quantum kernels for representing similarity measures between data. Quantum kernels are able to capture relationships in the data that are not efficiently computable on classical devices. However, there is no straightforward method to engineer the optimal quantum kernel for each specific use case.While...
Run 4 of the LHC will yield an unprecedented volume of data. In order
to process this data, the ATLAS collaboration is evolving its offline
software to be able to use heterogenous resources such as GPUs and FPGAs.
To reduce conversion overheads, the event data model (EDM) should be
compatible with the requirements of these resources. While the
ATLAS EDM has long allowed representing data...
After two successful physics runs the LHCb experiment underwent a comprehensive upgrade to enable LHCb to run at five times the instantaneous luminosity for Run 3 of the LHC. With this upgrade, LHCb is now the largest producer of data at the LHC. A new offline dataflow was developed to facilitate fast time-to-insight whilst respecting constraints from disk and CPU resources. The Sprucing is an...
With the increasing amount of optimized and specialized hardware such as GPUs, ML cores, etc. HEP applications face the opportunity and the challenge of being enabled to take advantage of these resources, which are becoming more widely available on scientific computing sites. The Heterogenous Frameworks project aims at evaluating new methods and tools for the support of both heterogeneous...
The large increase in luminosity expected from Run 4 of the LHC presents the ATLAS experiment with a new scale of computing challenge, and we can no longer restrict our computing to CPUs in a High Throughput Computing paradigm. We must make full use of the High Performance Computing resources available to us, exploiting accelerators and making efficient use of large jobs over many nodes.
Here...
To achieve better computational efficiency and exploit a wider range of computing resources, the CMS software framework (CMSSW) has been extended to offload part of the physics reconstruction to NVIDIA GPUs. To support additional back-ends, as well to avoid the need to write, validate and maintain a separate implementation of the reconstruction algorithms for each back-end, CMS has adopted the...
As the Large Hadron Collider progresses through Run 3, the LHCb experiment has made significant strides in upgrading its offline analysis framework and associated tools to efficiently handle the increasing volumes of data generated. Numerous specialised algorithms have been developed for offline analysis, with a central innovation being FunTuple--a newly developed component designed to...
We summarize the status of the Deep Underground Neutrino Experiment (DUNE) software and computing development. The DUNE Collaboration has been successfully operating the DUNE prototype detectors at both Fermilab and CERN, and testing offline computing services, software, and infrastructure using the data collected. We give an overview of results from end-to-end testing of systems needed to...
Since the mid-2010s, the ALICE experiment at CERN has seen significant changes in its software, especially with the introduction of the Online-Offline (O²) computing system during Long Shutdown 2. This evolution required continuous adaptation of the Quality Control (QC) framework responsible for online Data Quality Monitoring (DQM) and offline Quality Assurance (QA).
After a general...
The imminent high-luminosity era of the LHC will pose unprecedented challenges to the CMS detector. To meet these challenges, the CMS detector will undergo several upgrades, including replacing the current endcap calorimeters with a novel High-Granularity Calorimeter (HGCAL). A dedicated reconstruction framework, The Iterative Clustering (TICL), is being developed within the CMS Software...
We present an ML-based end-to-end algorithm for adaptive reconstruction in different FCC detectors. The algorithm takes detector hits from different subdetectors as input and reconstructs higher-level objects. For this, it exploits a geometric graph neural network, trained with object condensation, a graph segmentation technique. We apply this approach to study the performance of pattern...
Particle identification (PID) is crucial in particle physics experiments. A promising breakthrough in PID involves cluster counting, which quantifies primary ionizations along a particle’s trajectory in a drift chamber (DC), rather than relying on traditional dE/dx measurements. However, a significant challenge in cluster counting lies in developing an efficient reconstruction algorithm to...
We present an end-to-end reconstruction algorithm for highly granular calorimeters that includes track information to aid the reconstruction of charged particles. The algorithm starts from calorimeter hits and reconstructed tracks, and outputs a coordinate transformation in which all shower objects are well separated from each other, and in which clustering becomes trivial. Shower properties...
In the recent years, high energy physics discoveries have been driven by the increasing of detector volume and/or granularity. This evolution gives access to bigger statistics and data samples, but can make it hard to process results with current methods and algorithms. Graph neural networks, particularly graph convolution networks, have been shown to be powerful tools to address these...
Developments of the new Level-1 Trigger at CMS for the High-Luminosity Operation of the LHC are in full swing. The Global Trigger, the final stage of this new Level-1 Trigger pipeline, is foreseen to evaluate a menu of over 1000 cut-based algorithms, each of which targeting a specific physics signature or acceptance region. Automating the task of tailoring individual algorithms to specific...
Searching for anomalous data is especially important in rare event searches like that of the LUX-ZEPLIN (LZ) experiment's hunt for dark matter. While LZ's data processing provides analyzer-friendly features for all data, searching for anomalous data after minimal reconstruction allows one to find anomalies which may not have been captured by reconstructed features and allows us to avoid any...
The upcoming upgrades of LHC experiments and next-generation FCC (Future Circular Collider) machines will again change the definition of big data for the HEP environment. The ability to effectively analyse and interpret complex, interconnected data structures will be vital. This presentation will delve into the innovative realm of Graph Neural Networks (GNNs). This powerful tool extends...
The BESIII at the BEPCII electron-positron accelerator, which is located at IHEP, Beijing, China, is an experiment for the studies of hadron physics and $\tau$-charm physics with the highest accuracy achieved until now. It has collected several world's largest $e^+e^-$ samples in $\tau$-charm region. Anomaly detection on BESIII detectors is an important segment of improving data quality,...
During LHC High-Luminosity phase, the LHCb RICH detector will face challenges due to increased particle multiplicity and high occupancy. Introducing sub-100ps time information becomes crucial for maintaining excellent particle identification (PID) performance. The LHCb RICH collaboration plans to anticipate the introduction of timing through an enhancement program during the third LHC Long...
Jet reconstruction remains a critical task in the analysis of data from HEP colliders. We describe in this paper a new, highly performant, Julia package for jet reconstruction, JetReconstruction.jl, which integrates into the growing ecosystem of Julia packages for HEP. With this package users can run sequential reconstruction algoritms for jets, In particular, for LHC events, the...
Key4hep, a software framework and stack for future accelerators, integrates all the steps in the typical offline pipeline: generation, simulation, reconstruction and analysis. The different components of Key4hep use a common event data model, called EDM4hep. For reconstruction, Key4hep leverages Gaudi, a proven framework already in use by several experiments at the LHC, to orchestrate...
LUX-ZEPLIN (LZ) is a dark matter direct detection experiment using a dual-phase xenon time projection chamber with a 7-ton active volume. In 2022, LZ collaboration published a world leading limit on WIMP dark matter interactions with nucleons. The success of the LZ experiment hinges both on the resilient design of its hardware and software infrastructures. This talk will give an overview of...
ACTS is an experiment independent toolkit for track reconstruction, which is designed from the ground up for thread-safety and high performance. It is built to accommodate different experiment deployment scenarios, and also serves as community platform for research and development of new approaches and algorithms.
A fundamental component of ACTS is the geometry library. It models a...
To increase the automation to convert Computer-Aided-Design detector components as well as entire detector systems into simulatable ROOT geometries, TGeoArbN, a ROOT compatible geometry class, was implemented allowing the use of triangle meshes in VMC-based simulation. To improve simulation speed a partitioning structure in form of an Octree can be utilized. TGeoArbN in combination with a...
Electrons are one of the key particles that are detected by the CMS experiment and are reconstructed using the CMS software (CMSSW). Reconstructing electrons in CMSSW is a computational intensive task that is split into several steps, seeding being the most time consuming one. During the electron seeding process, the collection of tracker hits (seeds) is significantly reduced by selecting only...
GPUs are expected to be a key solution to the data challenges posed by track reconstruction in future high energy physics experiments. traccc, an R&D project within the ACTS track reconstruction toolkit, aims to demonstrate tracking algorithms in GPU programming models including CUDA and SYCL without loss of physical accuracy such as tracking efficiency and fitted parameter resolution. We...
The Circular Electron Positron Collider (CEPC) is a future experiment mainly designed to precisely measure the Higgs boson’s properties and search for new physics beyond the Standard Model. In the design of the CEPC detector, the VerTeX detector (VTX) is the innermost tracker playing a dominant role in determining the vertexes of a collision event. The VTX detector is also responsible for...
During Run 3, ALICE has enhanced its data processing and reconstruction chain by integrating GPUs, a leap forward in utilising high-performance computing at the LHC.
The initial 'synchronous' phase engages GPUs to reconstruct and compress data from the TPC detector. Subsequently, the 'asynchronous' phase partially frees GPU resources, allowing further offloading of additional reconstruction...
Efficient and precise track reconstruction is critical for the results of the Compact Muon Solenoid (CMS) experiment. The current CMS track reconstruction algorithm is a multi-step procedure based on the combinatorial Kalman filter as well as a Cellular Automaton technique to create track seeds. Multiple parameters regulate the reconstruction steps, populating a large phase space of possible...
Track reconstruction, a.k.a., tracking, is a crucial part of High Energy Physics experiments. Traditional methods for the task, relying on Kalman Filters, scale poorly with detector occupancy. In the context of the upcoming High Luminosity-LHC, solutions based on Machine Learning (ML) and deep learning are very appealing. We investigate the feasibility of training multiple ML architectures to...
In view of the High-Luminosity LHC era the ATLAS experiment is carrying out an upgrade campaign which foresees the installation of a new all-silicon Inner Tracker (ITk) and the modernization of the reconstruction software.
Track reconstruction will be pushed to its limits by the increased number of proton-proton collisions per bunch-crossing and the granularity of the ITk detector. In order...
The Super Tau-Charm Facility (STCF) proposed in China is an electron-positron collider designed to operate in a center-of-mass energy range from 2 to 7 GeV with peak luminosity above $0.5 × 10^{35}$cm$^{-2}s^{-1}$. The STCF will provide a unique platform for studies of hadron physics, strong interactions and searches for new physics beyond the Standard Model in the tau-charm region. To fulfill...
The upgrade of the CMS apparatus for the HL-LHC will provide unprecedented timing measurement capabilities, in particular for charged particles through the Mip Timing Detector (MTD). One of the main goals of this upgrade is to compensate the deterioration of primary vertex reconstruction induced by the increased pileup of proton-proton collisions by separating clusters of tracks not only in...
Mu2e will search for the neutrinoless coherent $\mu^-\rightarrow e^-$ conversion in the field of an Al nucleus, a Charged Lepton Flavor Violation (CLFV) process. The experiment is expected to start in 2026 and will improve the current limit by 4 orders of magnitude.
Mu2e consists of a straw-tube tracker and crystal calorimeter in a 1T B field complemented by a plastic scintillation counter...
Graph neural networks and deep geometric learning have been successfully proven in the task of track reconstruction in recent years. The GNN4ITk project employs these techniques in the context of the ATLAS upgrade ITk detector to produce similar physics performance as traditional techniques, while scaling sub-quadratically. However, one current bottleneck in the throughput and physics...
High quality particle reconstruction is crucial to data acquisition at large CERN experiments. While the classical algorithms have been successful so far, in recent years, the use of pattern recognition has become more and more necessary due to increasing complexity of the modern detectors. Graph Neural Network based approaches have been recently proposed to tackle challenges such as...
Track reconstruction is an essential element of modern and future collider experiments, including the ATLAS detector. The HL-LHC upgrade of the ATLAS detector brings an unprecedented tracking reconstruction challenge, both in terms of the large number of silicon hit cluster readouts and the throughput required for budget-constrained track reconstruction. Traditional track reconstruction...
The reconstruction of particle trajectories is a key challenge of particle physics experiments, as it directly impacts particle identification and physics performances while also representing one of the primary CPU consumers of many high-energy physics experiments. As the luminosity of particle colliders increases, this reconstruction will become more challenging and resource-intensive. New...
Track reconstruction is a crucial task in particle experiments and is traditionally very computationally expensive due to its combinatorial nature. Recently, graph neural networks (GNNs) have emerged as a promising approach that can improve scalability. Most of these GNN-based methods, including the edge classification (EC) and the object condensation (OC) approach, require an input graph that...
Graph neural networks represent a potential solution for the computing challenge posed by the reconstruction of tracks at the High Luminosity LHC [1, 2, 3]. The graph concept is convenient to organize the data and to split up the tracking task itself into the subtasks of identifying the correct hypothetical connections (edges) between the hits, subtasks that are easy to parallelize and process...
