Conveners
Parallel (Track 5): Full Simulation
- Tobias Stockmanns
- Marilena Bandieramonte (University of Pittsburgh (US))
Parallel (Track 5): Analysis Tools
- Jonas Rembser (CERN)
- Giacomo De Pietro (Karlsruhe Institute of Technology)
Parallel (Track 5): Accelerated Simulation
- Jonas Rembser (CERN)
- Giacomo De Pietro (Karlsruhe Institute of Technology)
- Tobias Stockmanns
- Marilena Bandieramonte (University of Pittsburgh (US))
Parallel (Track 5): Analysis with ML
- Jonas Rembser (CERN)
- Marilena Bandieramonte (University of Pittsburgh (US))
- Tobias Stockmanns
- Giacomo De Pietro (Karlsruhe Institute of Technology)
Parallel (Track 5): Fast Simulation
- Tobias Stockmanns
- Jonas Rembser (CERN)
- Giacomo De Pietro (Karlsruhe Institute of Technology)
- Marilena Bandieramonte (University of Pittsburgh (US))
Parallel (Track 5): Event Generation
- Giacomo De Pietro (Karlsruhe Institute of Technology)
- Marilena Bandieramonte (University of Pittsburgh (US))
Parallel (Track 5): Software Libraries
- Jonas Rembser (CERN)
- Tobias Stockmanns
Parallel (Track 5): Fitting
- Marilena Bandieramonte (University of Pittsburgh (US))
- Giacomo De Pietro (Karlsruhe Institute of Technology)
- Jonas Rembser (CERN)
- Tobias Stockmanns
Parallel (Track 5): Frameworks
- Jonas Rembser (CERN)
- Tobias Stockmanns
- Giacomo De Pietro (Karlsruhe Institute of Technology)
- Marilena Bandieramonte (University of Pittsburgh (US))
Parallel (Track 5): Quantum+
- Jonas Rembser (CERN)
- Tobias Stockmanns
- Marilena Bandieramonte (University of Pittsburgh (US))
- Giacomo De Pietro (Karlsruhe Institute of Technology)
Description
Simulation and analysis tools
The Jiangmen Underground Neutrino Observatory (JUNO) is a neutrino experiment under construction in the Guangdong province of China. The experiment has a wide physics program with the most ambitious goal being the determination of the neutrino mass ordering and the high-precision measurement of neutrino oscillation properties using anti-neutrinos produced in the 50 km distant commercial...
At the LHC experiments, RNTuple is emerging as the primary data storage solution, and will be ready for production next year. In this context, we introduce the latest development in UnROOT.jl, a high-performance and thread-safe Julia ROOT I/O package that facilitates both the reading and writing of RNTuple data.
We briefly share insights gained from implementing RNTuple Reader twice: first...
The Fair Universe project is organising the HiggsML Uncertainty Challenge, which will/has run from June to October 2024.
This HEP and Machine Learning competition is the first to strongly emphasise uncertainties: mastering uncertainties in the input training dataset and outputting credible confidence intervals.
The context is the measurement of the Higgs to tau+ tau- cross section like...
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 is a major consumer of computing resources 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 performance of this simulation for...
The high luminosity LHC (HL-LHC) era will deliver unprecedented luminosity and new detector capabilities for LHC experiments, leading to significant computing challenges with storing, processing, and analyzing the data. The development of small, analysis-ready storage formats like CMS NanoAOD (4kB/event), suitable for up to half of physics searches and measurements, helps achieve necessary...
For the start of Run-3 CMS Full Simulation was based on Geant4 10.7.2. In this work we report on evolution of usage of Geant4 within CMSSW and adaptation of the newest Geant4 11.2.1, which is expected to be used for CMS simulation production in 2025. Physics validation results and results on CPU performance are reported.
For the Phase-2 simulation several R&D are carried out. A significant...
The software toolbox used for "big data" analysis in the last few years is rapidly changing. The adoption of software design approaches able to exploit the new hardware architectures and improve code expressiveness plays a pivotal role in boosting data processing speed, resources optimisation, analysis portability and analysis preservation.
The scientific collaborations in the field of High...
The Compressed Baryonic Matter (CBM) is an under-construction heavy-ion physics experiment for exploring the QCD phase diagram at high $\mu_{B}$ which will use the new SIS-100 accelerator at the Facility for Anti-Proton and Ion Research (FAIR) in Darmstadt, Germany. The Silicon Tracking System (STS) is to be the main detector for tracking and momentum determination. A scaled-down prototype of...
The ATLAS experiment is in the process of developing a columnar analysis demonstrator, which takes advantage of the Python ecosystem of data science tools. This project is inspired by the analysis demonstrator from IRIS-HEP.
The demonstrator employs PHYSLITE OpenData from the ATLAS collaboration, the new Run 3 compact ATLAS analysis data format. The tight integration of ROOT features within...
The IceCube Neutrino Observatory instruments one cubic kilometer of glacial ice at the geographic South Pole. Cherenkov light emitted by charged particles is detected by 5160 photomultiplier tubes embedded in the ice. Deep antarctic ice is extremely transparent, resulting in absorption lengths exceeding 100m. However, yearly variations in snow deposition rates on the glacier over the last 100...
Over the past few decades, there has been a noticeable surge in muon tomography research, also referred to as muography. This method, falling under the umbrella of Non-Destructive Evaluation (NDE), constructs a three-dimensional image of a target object by harnessing the interaction between cosmic ray muons and matter, akin to how radiography utilizes X-rays. Essentially, muography entails...
The ATLAS Fast Chain represents a significant advancement in streamlining Monte Carlo (MC) production efficiency, specifically for the High-Luminosity Large Hadron Collider (HL-LHC). This project aims to simplify the production of Analysis Object Data (AODs) and potentially Derived Analysis Object Data (DAODs) from generated events with a single transform, facilitating rapid reproduction of...
Simulation of physics processes and detector response is a vital part of high energy physics research but also representing a large fraction of computing cost. Generative machine learning is successfully complementing full (standard, Geant4-based) simulation as part of fast simulation setups improving the performance compared to classical approaches.
A lot of attention has been given to...
Celeritas is a rapidly developing GPU-enabled detector simulation code aimed at accelerating the most computationally intensive problems in high energy physics. This presentation will highlight exciting new performance results for complex subdetectors from the CMS and ATLAS experiments using EM secondaries from hadronic interactions. The performance will be compared on both Nvidia and AMD GPUs...
An important alternative for boosting the throughput of simulation applications is to take advantage of accelerator hardware, by making general particle transport simulation for high-energy physics (HEP) single-instruction-multiple-thread (SIMT) friendly. This challenge is not yet resolved due to difficulties in mapping the complexity of Geant4 components and workflow to the massive...
The demands for Monte-Carlo simulation are drastically increasing with the high-luminosity upgrade of the Large Hadron Collider, and expected to exceed the currently available compute resources. At the same time, modern high-performance computing has adopted powerful hardware accelerators, particularly GPUs. AdePT is one of the projects aiming to address the demanding computational needs by...
Opticks is an open source project that accelerates optical photon simulation
by integrating NVIDIA GPU ray tracing, accessed via the NVIDIA OptiX API, with
Geant4 toolkit based simulations.
Optical photon simulation times of 14 seconds per 100 million photons
have been measured within a fully analytic JUNO GPU geometry
auto-translated from the Geant4 geometry when using a single...
In this work we present the Graph-based Full Event Interpretation (GraFEI), a machine learning model based on graph neural networks to inclusively reconstruct events in the Belle II experiment.
Belle II is well suited to perform measurements of $B$ meson decays involving invisible particles (e.g. neutrinos) in the final state. The kinematical properties of such particles can be deduced from...
In analyses conducted at Belle II, it is often beneficial to reconstruct the entire decay chain of both B mesons produced in an electron-positron collision event using the information gathered from detectors. The currently used reconstruction algorithm, starting from the final state particles, consists of multiple stages that necessitate manual configurations and suffers from low efficiency...
Subatomic particle track reconstruction (tracking) is a vital task in High-Energy Physics experiments. Tracking, in its current form, is exceptionally computationally challenging. Fielded solutions, relying on traditional algorithms, do not scale linearly and pose a major limitation for the HL-LHC era. Machine Learning (ML) assisted solutions are a promising answer.
Current ML model design...
Direct photons are unique probes to study and characterize the quark-gluon plasma (QGP) as they leave the collision medium mostly unscathed. Measurements at top Large Hadron Collider (LHC) energies at low pT reveal a very small thermal photon signal accompanied by considerable systematic uncertainties. Reduction of such uncertainties, which arise from the π0 and η measurements, as...
Particle flow reconstruction at colliders combines various detector subsystems (typically the calorimeter and tracker) to provide a combined event interpretation that utilizes the strength of each detector. The accurate association of redundant measurements of the same particle between detectors is the key challenge in this technique. This contribution describes recent progress in the ATLAS...
Accurate modeling of backgrounds for the development of analyses requires large enough simulated samples of background data. When searching for rare processes, a large fraction of these expensively produced samples is discarded by the analysis criteria that try to isolate the rare events. At the Belle II experiment, the event generation stage takes only a small fraction of the computational...
As we are approaching the high-luminosity era of the LHC, the computational requirements of the ATLAS experiment are expected to increase significantly in the coming years. In particular, the simulation of MC events is immensely computationally demanding, and their limited availability is one of the major sources of systematic uncertainties in many physics analyses. The main bottleneck in the...
Detector simulation is a key component of physics analysis and related activities in CMS. In the upcoming High Luminosity LHC era, simulation will be required to use a smaller fraction of computing in order to satisfy resource constraints. At the same time, CMS will be upgraded with the new High Granularity Calorimeter (HGCal), which requires significantly more resources to simulate than the...
In the realm of low-energy nuclear physics experiments, the Active Target Time Projection Chamber (AT-TPC) can be advantageous for studying nuclear reaction kinematics, such as the alpha cluster decay of $^{12}C$, by tracking the reaction products produced in the active gas medium of the TPC. The tracking capability of the TPC is strongly influenced by the homogeneity of the electric field...
In high energy physics, fast simulation techniques based on machine learning could play a crucial role in generating sufficiently large simulated samples. Transitioning from a prototype to a fully deployed model usable in a full scale production is a very challenging task.
In this talk, we introduce the most recent advances in the implementation of fast simulation for calorimeter showers in...
The event simulation is a key element for data analysis at present and future particle accelerators. We show [1] that novel machine learning algorithms, specifically Normalizing Flows and Flow Matching, can be effectively used to perform accurate simulations with several orders of magnitude of speed-up compared to traditional approaches when only analysis level information is needed. In such a...
Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments with ever increasing luminosities. Generative machine learning (ML) models have been shown to speed up and augment the traditional simulation chain. Many previous efforts were limited to models relying on fixed regular grid-like geometries leading to artifacts when applied to highly...
Within the ROOT/TMVA project, we have developed a tool called SOFIE, that takes externally trained deep learning models in ONNX format or Keras and PyTorch native formats and generates C++ code that can be easily included and invoked for fast inference of the model. The code has a minimal dependency and can be easily integrated into the data processing and analysis workflows of the HEP...
Non perturbative QED is used to predict beam backgrounds at the interaction point of colliders, in calculations of Schwinger pair creation and in precision QED tests with ultra-intense lasers. In order to predict these phenomena, custom built monte carlo event generators based on a suitable non perturbative theory have to be developed. One such suitable theory uses the Furry Interaction...
The HIBEAM-NNBAR experiment at the European Spallation Source is a multidisciplinary two-stage program of experiments that includes high-sensitivity searches for neutron oscillations, searches for sterile neutrons, searches for axions, as well as the search for exotic decays of the neutron. The computing framework of the collaboration includes diverse software, from particle generators to...
The effort to speed up the Madgraph5_aMC@NLO generator by exploiting CPU vectorization and GPUs, which started at the beginning of 2020, is expected to deliver the first production release of the code for QCD leading-order (LO) processes in 2024. To achieve this goal, many additional tests, fixes and improvements have been carried out by the development team in recent months, both to carry out...
As the quality of experimental measurements increases, so does the need for Monte Carlo-generated simulated events — both with respect to total amount, and to their precision. In perturbative methods this involves the evaluation of higher order corrections to the leading order (LO) scattering amplitudes, including real emissions and loop corrections. Although experimental uncertainties today...
The ROOT software framework is widely used in HENP for storage, processing, analysis and visualization of large datasets. With the large increase in usage of ML for experiment workflows, especially lately in the last steps of the analysis pipeline, the matter of exposing ROOT data ergonomically to ML models becomes ever more pressing. This contribution presents the advancements in an...
Quantum computers may revolutionize event generation for collider physics by allowing calculation of scattering amplitudes from full quantum simulation of field theories. Although rapid progress is being made in understanding how best to encode quantum fields onto the states of quantum registers, most formulations are lattice-based and would require an impractically large number of qubits when...
With the large data volume increase expected for HL-LHC and the even more complex computing challenges set by future colliders, the need for more elaborate data access patterns will become more pressing. ROOT’s next-generation data format and I/O subsystem, RNTuple, is designed to address those challenges, currently already showing a clear improvement in storage and I/O efficiency with respect...
Uproot is a Python library for ROOT I/O that uses NumPy and Awkward Array to represent and perform computations on bulk data. However, Uproot uses pure Python to navigate through ROOT's data structures to find the bulk data, which can be a performance issue in metadata-intensive I/O: (a) many small files, (b) many small TBaskets, and/or (c) low compression overhead. Worse, these performance...
The generation of large event samples with Monte Carlo Event Generators is expected to be a computational bottleneck for precision phenomenology at the HL-LHC and beyond. This is due in part to the computational cost incurred by negative weights in 'matched' calculations combining NLO perturbative QCD with a parton shower: for the same target uncertainty, a larger sample must be...
The generation of Monte Carlo events is a crucial step for all particle collider experiments. Accurately simulating the hard scattering processes is the foundation for subsequent steps, such as QCD parton showering, hadronization, and detector simulations. A major challenge in event generation is the efficient sampling of the phase spaces of hard scattering processes due to the potentially...
Representing HEP and astrophysics data as graphs (i.e. networks of related entities) is becoming increasingly popular. These graphs are not only useful for structuring data storage but are also increasingly utilized within various machine learning frameworks.
However, despite their rising popularity, numerous unused opportunities exist, particularly concerning the utilization of graph...
Model fitting using likelihoods is a crucial part of many analyses in HEP.
zfit started over five years ago with the goal of providing this capability within the Python analysis ecosystem by offering a variety of advanced features and high performance tailored to the needs of HEP.
After numerous iterations with users and a continuous development, zfit reached a maturity stage with a stable...
NIFTy[1], a probabilistic programming framework developed for astrophysics,
has recently been adapted to be used in partial wave analyses (PWA) at the
COMPASS [2] experiment located in CERN. A non-parametric model, described
as a correlated field, is used to characterize kinematically-smooth complex-
binned amplitudes. Parametric models, like a Breit-Wigner distribution, can
also be mixed...
With the growing datasets of HE(N)P experiments, statistical analysis becomes more computationally demanding, requiring improvements in existing statistical analysis algorithms and software. One way forward is to use Machine Learning (ML) techniques to approximate the otherwise untractable likelihood ratios. Likelihood fits in HEP are often done with RooFit, a C++ framework for statistical...
The Bayesian Analysis Toolkit in Julia (BAT.jl) is an open source software package that provides user-friendly tooling to tackle statistical problems encountered in Bayesian (an not just Bayesian) inference.
BAT.jl succeeds the very successful BAT-C++ (over 500 citations) using modern Julia language. We chose Julia because of its high performance, native automatic differentiation, support...
Neural Simulation-Based Inference (NSBI) is a powerful class of machine learning (ML)-based methods for statistical inference that naturally handle high dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements at the Large Hadron Collider, where no single observable may be optimal to scan over...
JUNO (Jiangmen Underground Neutrino Observatory) is a neutrino experiment being built in South China. Its primary goals are to resolve the order of the neutrino mass eigenstates and to precisely measure the oscillation parameters $\sin^2\theta_{12}$, $\Delta m^2_{21}$, and $\Delta m^2_{31 (32)}$ by observing the oscillation pattern of electron antineutrinos produced in eight reactor cores of...
For high-energy physics experiments, the generation of Monte Carlo events, and in particular the simulation of the detector response, is a very computationally intensive process. In many cases, the primary bottleneck in detector simulation is the detailed simulation of the electromagnetic and hadronic showers in the calorimeter system. For the ATLAS experiment, about 80% of the total CPU usage...
Gaussino is an experiment-independent simulation package built upon the Gaudi software framework. It provides generic core components and interfaces for a complete HEP simulation application: event generation, detector simulation, geometry, monitoring and output of the simulated data. This makes it suitable for use as a standalone application for early prototyping, testbeam setups etc as well...
Reconfigurable detector for the measurement of spatial radiation dose distribution for applications in the preparation of individual patient treatment plans [1] was a research and development project aimed at improving radiation dose distribution measurement techniques for therapeutic applications. The main idea behind the initiative was to change the current radiation dose distribution...
The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose experiment under construction in southern China. JUNO is designed to determine the mass ordering of neutrinos and precisely measure neutrino oscillation parameters by detecting reactor neutrinos from the Yangjiang and Taishan Nuclear Power Plants. Atmospheric neutrinos, solar neutrinos, geo-neutrinos, supernova burst...
The common and shared event data model EDM4hep is a core part of the Key4hep project. It is the component that is used to not only exchange data between the different software pieces, but it also serves as a common language for all the components that belong to Key4hep. Since it is such a central piece, EDM4hep has to offer an efficient implementation. On the other hand, EDM4hep has to be...
The software description of the ATLAS detector is based on the GeoModel toolkit, developed in-house for the ATLAS experiment but released and maintained as a separate package with few dependencies. A compact SQLite-based exchange format permits the sharing of geometrical information between applications including visualization, clash detection, material inventory, database browsing, and...
Gravitational Waves (GW) were first predicted by Einstein in 1918, as a consequence of his theory of General Relativity published in 1915. The first direct GW detection was announced in 2016 by the LIGO and Virgo collaborations. Both experiments consist of a modified Michelson-Morley interferometer that can measure deformations of the interferometer arms of about 1/1,000 the width of a proton....
This paper presents the innovative HPCNeuroNet model, a pioneering fusion of Spiking Neural Networks (SNNs), Transformers, and high-performance computing tailored for particle physics, particularly in particle identification from detector responses. Drawing from the intrinsic temporal dynamics of SNNs and the robust attention mechanisms of Transformers, our approach capitalizes on these...
Hamiltonian moments in Fourier space—expectation values of the unitary evolution operator under a Hamiltonian at various times—provide a robust framework for understanding quantum systems. They offer valuable insights into energy distribution, higher-order dynamics, response functions, correlation information, and physical properties. Additionally, Fourier moments enable the computation of...
Jets are key observables to measure the hadronic activities at high energy colliders such as the Large Hadron Collider (LHC) and future colliders such as the High Luminosity LHC (HL-LHC) and the Circular Electron Positron Collider (CEPC). Yet jet reconstruction is a computationally expensive task especially when the number of final-state particles is large. Such a clustering task can be...
Machine learning, particularly deep neural networks, has been widely used in high-energy physics, demonstrating remarkable results in various applications. Furthermore, the extension of machine learning to quantum computers has given rise to the emerging field of quantum machine learning. In this paper, we propose the Quantum Complete Graph Neural Network (QCGNN), which is a variational...
Built on algorithmic differentiation (AD) techniques, differentiable programming allows to evaluate derivatives of computer programs. Such derivatives are useful across domains for gradient-based design optimization and parameter fitting, among other applications. In high-energy physics, AD is frequently used in machine learning model training and in statistical inference tasks such as maximum...
