Large Hadron Collider Experiments Research Articles

NLO QCD corrections to the Bc -pair hadroproduction

The Bc meson pair, including pairs of pseudoscalar states and vector states, productions in proton-proton collisions are investigated at the next-to-leading order (NLO) accuracy in the nonrelativistic quantum chromodynamics factorization formalism. The corresponding cross sections at the Large Hadron Collider (LHC) with s=14 TeV are evaluated. Numerical results indicate that the NLO corrections are substantial, and even dominate over the leading order contributions. Considering the predicted cross sections are sizable, the Bc-pair production is expected to be observable at the high-luminosity LHC experiment. Published by the American Physical Society 2024

Time-resolved synchrotron light source X-ray detection with Low-Gain Avalanche Diodes

Low Gain Avalanche Diodes (LGADs) represent the state-of-the-art in timing measurements and will instrument future timing detectors at the High Luminosity Large Hadron Collider (HL-LHC) experiments. While conceived as a sensor for charged particles, the intrinsic gain of LGADs makes it possible to detect low energy X-rays with good energy resolution and excellent timing (tens of picoseconds). Using the Stanford Synchrotron Radiation Lightsource (SSRL) at SLAC, several LGADs designs were characterized with energies from 5 to 35 keV. The SSRL provides 10 ps pulsed X-ray bunches separated by 2.1ns intervals, and with an energy dispersion (ΔE/E) of 1 × 10-4. LGADs fabricated by Hamamatsu Photonics (HPK) and Brookhaven National Laboratory (BNL) with different thicknesses ranging from 20µm to 50µm and different gain layer designs were read out a two stage fast amplification circuit and digitized with a high bandwidth, high sampling rate oscilloscope. PIN devices from HPK were characterized as well. A systematic and detailed characterization of the devices’ energy linearity, resolution and timing resolution as a function of X-ray energy was performed for different biasing voltages at room temperature.

Sibyll[formula omitted]

In the last decade, an increasing number of datasets have revealed a consistent discrepancy between the number of muons measured in ultra-high-energy extensive air showers (EAS) and the numbers predicted by simulations. This gap persists despite incorporating Large Hadron Collider (LHC) data into the tuning of current hadronic interaction models, leading to the phenomenon often termed the “muon puzzle”. To gain a deeper understanding of the potential origins of this muon puzzle, we have developed Sibyll★, a series of phenomenologically modified versions of Sibyll 2.3d. In these models, we have increased muon production by altering ρ0, baryon–antibaryon pair, or kaon production in hadronic multiparticle production processes. These variants remain within bounds from provided by accelerator measurements, including those from the LHC and fixed-target experiments, notably NA49 and NA61, showing a level of consistency comparable to Sibyll 2.3d. Our findings show that these modifications can increase the muon count in EAS by up to 35%, while minimally affecting the depth of shower maximum (Xmax) and other shower variables. Additionally, we assess the impact of these modifications on various observables, including inclusive muon and neutrino fluxes and the multiplicities of muon bundles in deep underground and water/ice Cherenkov detectors. We aim for at least one of these model variants to offer a more accurate representation of EAS data at the highest energies, thereby enhancing the quality of Monte Carlo predictions used in training neural networks. This improvement is crucial for achieving more reliable data analyses and interpretations.

High-rate tests on resistive plate chambers operated with eco-friendly gas mixtures

Results obtained by the RPC ECOgas@GIF++ Collaboration, using Resistive Plate Chambers operated with new, eco-friendly gas mixtures, based on tetrafluoropropene and carbon dioxide, are shown and discussed in this paper. Tests aimed to assess the performance of this kind of detectors in high-irradiation conditions, analogous to the ones foreseen for the coming years at the Large Hadron Collider experiments, were performed, and demonstrate a performance basically similar to the one obtained with the gas mixtures currently in use, based on tetrafluoroethane, which is being progressively phased out for its possible contribution to the greenhouse effect. Long term aging tests are also being carried out, with the goal to demonstrate the possibility of using these eco-friendly gas mixtures during the whole High Luminosity phase of the Large Hadron Collider.

LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics

This work presents a novel reconfigurable architecture for Low Latency Graph Neural Network (LL-GNN) designs for particle detectors, delivering unprecedented low latency performance. Incorporating FPGA-based GNNs into particle detectors presents a unique challenge since it requires sub-microsecond latency to deploy the networks for online event selection with a data rate of hundreds of terabytes per second in the Level-1 triggers at the CERN Large Hadron Collider experiments. This article proposes a novel outer-product based matrix multiplication approach, which is enhanced by exploiting the structured adjacency matrix and a column-major data layout. In addition, we propose a custom code transformation for the matrix multiplication operations, which leverages the structured sparsity patterns and binary features of adjacency matrices to reduce latency and improve hardware efficiency. Moreover, a fusion step is introduced to further reduce the end-to-end design latency by eliminating unnecessary boundaries. Furthermore, a GNN-specific algorithm-hardware co-design approach is presented which not only finds a design with a much better latency but also finds a high accuracy design under given latency constraints. To facilitate this, a customizable template for this low latency GNN hardware architecture has been designed and open-sourced, which enables the generation of low-latency FPGA designs with efficient resource utilization using a high-level synthesis tool. Evaluation results show that our FPGA implementation is up to 9.0 times faster and achieves up to 13.1 times higher power efficiency than a GPU implementation. Compared to the previous FPGA implementations, this work achieves 6.51 to 16.7 times lower latency. Moreover, the latency of our FPGA design is sufficiently low to enable deployment of GNNs in a sub-microsecond, real-time collider trigger system, enabling it to benefit from improved accuracy. The proposed LL-GNN design advances the next generation of trigger systems by enabling sophisticated algorithms to process experimental data efficiently.

Generative machine learning for detector response modeling with a conditional normalizing flow

In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the Large Hadron Collider (LHC) experiments. Our objective is to develop a generative model capable of efficiently simulating detector responses for specific particle observables, focusing on the correlations between detector responses of different particles in the same event and accommodating asymmetric detector responses. We present a conditional normalizing flow model (𝒞𝒩ℱ) based on a chain of Masked Autoregressive Flows, which effectively incorporates conditional variables and models high-dimensional density distributions. We assess the performance of the 𝒞𝒩ℱ model using a simulated sample of Higgs boson decaying to diphoton events at the LHC. We create reconstruction-level observables using a smearing technique. We show that conditional normalizing flows can accurately model complex detector responses and their correlation. This method can potentially reduce the computational burden associated with generating large numbers of simulated events while ensuring that the generated events meet the requirements for data analyses. We make our code available at https://github.com/allixu/normalizing_flow_for_detector_response.

Impact of beam–beam effects on absolute luminosity calibrations at the CERN Large Hadron Collider

At the Large Hadron Collider (LHC), absolute luminosity calibrations obtained by the van der Meer (vdM) method are affected by the mutual electromagnetic interaction of the two beams. The colliding bunches experience relative orbit shifts, as well as optical distortions akin to the dynamic-β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document} effect, that both depend on the transverse beam separation and must therefore be corrected for when deriving the absolute luminosity scale. In the vdM regime, the beam–beam parameter is small enough that the orbit shift can be calculated analytically. The dynamic-β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta $$\\end{document} corrections to the luminometer calibrations, however, had until the end of Run 2 been estimated in the linear approximation only. In this report, the influence of beam–beam effects on the vdM-based luminosity scale is quantified, together with the associated systematic uncertainties, by means of simulations that fully take into account the non-linearity of the beam–beam force, as well as the resulting non-Gaussian distortions of the transverse beam distributions. Two independent multiparticle simulations, one limited to the weak-strong approximation and one that models strong-strong effects in a self-consistent manner, are found in excellent agreement; both predict a percent-level shift of the absolute pp-luminosity values with respect to those assumed until recently in the physics publications of the LHC experiments. These results also provide guidance regarding further studies aimed at reducing the beam–beam-related systematic uncertainty on beam–beam corrections to absolute luminosity calibrations by the van der Meer method.

Automated Network Services for Exascale Data Movement

The Large Hadron Collider (LHC) experiments distribute data by leveraging a diverse array of National Research and Education Networks (NRENs), where experiment data management systems treat networks as a “blackbox” resource. After the High Luminosity upgrade, the Compact Muon Solenoid (CMS) experiment alone will produce roughly 0.5 exabytes of data per year. NREN Networks are a critical part of the success of CMS and other LHC experiments. However, during data movement, NRENs are unaware of data priorities, importance, or need for quality of service, and this poses a challenge for operators to coordinate the movement of data and have predictable data flows across multi-domain networks. The overarching goal of SENSE (The Software-defined network for End-to-end Networked Science at Exascale) is to enable National Labs and universities to request and provision end-to-end intelligent network services for their application workflows leveraging SDN (Software-Defined Networking) capabilities. This work aims to allow LHC Experiments and Rucio, the data management software used by CMS Experiment, to allocate and prioritize certain data transfers over the wide area network. In this paper, we will present the current progress of the integration of SENSE, Multi-domain end-to-end SDN Orchestration with QoS (Quality of Service) capabilities, with Rucio, the data management software used by CMS Experiment.

Optimizing Pb beam losses at the LHCb for maximum luminosity

In addition to the physics program with proton beams, the Large Hadron Collider (LHC) at CERN also provides collisions of fully-stripped Pb beams for about one month per year. When colliding Pb nuclei, electromagnetic interactions are the dominating processes because of the intense Coulomb field produced by the ions. These ’ultra-peripheral’ interactions give rise to ions with a changed magnetic rigidity. This causes losses in the machine that can impose limits on the luminosity. Among them, the bound-free pair production (BFPP) causes a localised power deposition downstream of each collision point, which could induce superconducting magnet quenches if not well controlled. These losses were studied and successfully mitigated for most LHC experiments, however the recent request by LHCb to increase the Pb-Pb luminosity requires a revision of BFPP collisional loss limitations. In this paper, the simulation of BFPP losses from Pb-Pb collisions around LHCb is presented. The loss patterns are discussed for different beam parameters. Finally, a mitigation strategy by means of an orbit bump is studied.

A holistic study of the WLCG energy needs for the LHC scientific program

A holistic study of the WLCG energy needs for the LHC scientific program

Acceleration beyond lowest order event generation. An outlook on further parallelism within MadGraph5_aMC@NLO

An important area of high energy physics studies at the Large Hadron Collider (LHC) currently concerns the need for more extensive and precise comparison data. Important tools in this realm are event reweighing and evaluation of more precise next-to-leading order (NLO) processes via Monte Carlo event generators, especially in the context of the upcoming High Luminosity LHC. Current event generators need to improve throughputs for these studies. MadGraph5_aMC@NLO (MG5aMC) is an event generator being used by LHC experiments which has been accelerated considerably with a port to GPU and vector CPU architectures, but as of yet only for leading order processes. In this contribution a prototype for event reweighing using the accelerated MG5aMC software, as well as plans for an NLO implementation, are presented.

General purpose data streaming platform for log analysis, anomaly detection and security protection

INFN-CNAF is one of the Worldwide LHC Computing Grid (WLCG) Tier-1 data centres, providing computing, networking and storage resources to a wide variety of scientific collaborations, not limited to the four LHC (Large Hadron Collider) experiments. The INFN-CNAF data centre will move to a new location next year. At the same time, the requirements from our experiments and users are becoming increasingly challenging and new scientific communities have started or will soon start exploiting our resources. Currently, we are reengineering several services, in particular our monitoring infrastructure, in order to improve the day-by-day operations and to cope with the increasing complexity of the use cases and with the future expansion of the centre. This scenario led us to implement a data streaming infrastructure designed to enable log analysis, anomaly detection, threat hunting, integrity monitoring and incident response. Such data streaming platform has been organised to manage different kinds of data coming from heterogeneous sources, to support multi-tenancy and to be scalable. Moreover, we will be able to provide an on demand end-to-end data streaming application to those users/communities requesting such kind of facility. The infrastructure is based on the Apache Kafka platform, which provides streaming of events at large scale, with authorization and authentication configured at the topic level for ensuring data isolation and protection. Data can be consumed by different applications, such as those devoted to log analysis, which provide the capability to index large amounts of data and implement appropriate access policies to inspect and visualise information. In this contribution we will present and motivate our technological choices for the definition of the infrastructure, we will describe its components and we will depict use cases which can be addressed with this platform.

First results of running the LHC with lead ions at a beam energy of 6.8 Z TeV

A two-day test of operation with Pb ion beams was carried out in the CERN Large Hadron Collider (LHC) in 2022, with the aim of gaining experience in view of the future high luminosity heavy-ion physics runs from 2023 onwards. The LHC experiments received the first Pb-Pb collisions at a record energy of 5.36 TeV centre-of-mass energy per colliding nucleon pair (beam energy 6.8 Z TeV). Bunch trains created with a new production scheme in the injectors, including slip-stacking, were injected into the LHC, with the collimation of nuclear beams with bent crystals tested along with a new collimation scheme for collision products. This paper describes the conditions and outcomes of these tests, which are critical steps in the upgrade to higher luminosity.

Radiation resistant optical components for high energy physics detectors

New detectors for future high-energy physics experiments will operate under unprecedented radiation dose rates. This condition requires improved radiation resistance on detector equipment. The consequent development of new materials, particularly optical materials, becomes crucial. In this work, optical components mean reflectors, light absorbers, or light transmitters. These materials, reflectors or light transmitters, are needed in detectors primarily to collect and transmit light from the scintillator to the PMT. When it comes to light absorbers, they are required to protect the detector from light from the environment. This work aims at studying selected optical materials with improved properties (functional and optical) and radiation resistance that can be used in new detectors at the Large Hadron Collider (LHC) experiments. Light transmittance, optical reflection, thermal characteristics, and radiation resistance were investigated to evaluate the proposed materials.We have developed optical systems based on siloxanes to continue our previous developments of radiation-resistant materials for radiation detectors. We also report a study of several reflective materials and light absorber introduced into the siloxane. Investigations have shown that these systems are radiation resistant to doses of at least 1 MGy.Tested samples were irradiated at linear electron accelerator LUE-40 in the National Science Center Kharkiv Institute of Physics and Technology (KIPT). The accelerated electrons were sent to the heavy complex targets to deliver the irradiation with gamma or neutron fluxes. The consequent gamma and neutron fluxes and doses were estimated with the GEANT4 simulation.

Prototyping a ROOT-based distributed analysis workflow for HL-LHC: The CMS use case

The challenges expected for the next era of the Large Hadron Collider (LHC), both in terms of storage and computing resources, provide LHC experiments with a strong motivation for evaluating ways of rethinking their computing models at many levels. Great efforts have been put into optimizing the computing resource utilization for the data analysis, which leads both to lower hardware requirements and faster turnaround for physics analyses. In this scenario, the Compact Muon Solenoid (CMS) collaboration is involved in several activities aimed at benchmarking different solutions for running High Energy Physics (HEP) analysis workflows. A promising solution is evolving software towards more user-friendly approaches featuring a declarative programming model and interactive workflows. The computing infrastructure should keep up with this trend by offering on the one side modern interfaces, and on the other side hiding the complexity of the underlying environment, while efficiently leveraging the already deployed grid infrastructure and scaling toward opportunistic resources like public cloud or HPC centers. This article presents the first example of using the ROOT RDataFrame technology to exploit such next-generation approaches for a production-grade CMS physics analysis. A new analysis facility is created to offer users a modern interactive web interface based on JupyterLab that can leverage HTCondor-based grid resources on different geographical sites. The physics analysis is converted from a legacy iterative approach to the modern declarative approach offered by RDataFrame and distributed over multiple computing nodes. The new scenario offers not only an overall improved programming experience, but also an order of magnitude speedup increase with respect to the previous approach.

Discovering New Science in the Noise from LHC Experiments

Steven Schramm and his team of researchers in the DISCOVERHEP project are searching for evidence of new physics in previously ignored ‘noise’ data of Large Hadron Collider experiments.

Effect of ionizing radiation on thermal and mechanical properties of filled-epoxy adhesives

Stave assemblies at the Large Hadron Collider and other high-energy physics experiments require radiation-resistant adhesives composed of light elements with high thermal conductivity and low interfacial resistance. Porous carbon foams are currently bonded to carbon fiber faceplates with graphite-filled epoxy. Low-viscosity epoxies are used to increase the amount of filler in order to double the thermal conductivity without impacting the properties of the material under irradiation. Promising new adhesive formulations showed no degradation in thermal conductivity after exposure to a fluence of 1016 n/cm2 (>0.1 MeV neutrons) or 10 Gy gamma radiation, but showed variable degradation, compared to the currently used adhesive, after exposure to a fluence of 1015 p/cm2 (1 MGy, 67.5 MeV protons). Adhesives with more than double the irradiated thermal conductivity of the baseline material showed marked improvement in thermal transport in graphite/epoxy/foam/epoxy/graphite structures based on thermal diffusivity measurements. Silicon carbide particles can be added to graphite to stiffen the modulus of the filled epoxy. When spherical AlN was used as a filler, the degradation in thermal conductivity under proton irradiation varied based on the size of the ceramic filler. Options for improving the thermal performance of irradiated adhesives for high-energy physics experiments are discussed.

A sensitivity study of triboson production processes to dimension-6 EFT operators at the LHC

We present the first parton-level study of anomalous effects in triboson production in both fully and semi-leptonic channels in proton-proton collisions at 13 TeV at the Large Hadron Collider (LHC). The sensitivity to anomalies induced by a minimal set of bosonic dimension-6 operators from the Warsaw basis is evaluated with specific analyses for each final state. A likelihood-based strategy is employed to assess the most sensitive kinematic observables per channel, where the contribution of Effective Field Theory operators is parameterized at either the linear or quadratic level. The impact of the mutual interference terms of pairs of operators on the sensitivity is also examined. This benchmark study explores the complementarity and overlap in sensitivity between different triboson measurements and paves the way for future analyses at the LHC experiments. The statistical combination of the considered final states allows setting stringent bounds on five bosonic Wilson coefficients.

MetaHEP: Meta learning for fast shower simulation of high energy physics experiments

For High Energy Physics (HEP) experiments, such as the Large Hadron Collider (LHC) experiments, the calorimeter is a key detector to measure the energy of particles. Incident particles interact with the material of the calorimeter, creating cascades of secondary particles, so-called showers. A detailed description of the showering process relies on simulation methods that precisely describe all particle interactions with matter. Constrained by the need for precision, the simulation of calorimeters is inherently slow and constitutes a bottleneck for HEP analysis. Furthermore, with the upcoming high luminosity upgrade of the LHC and a much-increased data production rate, the amount of required simulated events will increase. Several research directions have recently investigated the use of Machine Learning based models to accelerate particular calorimeter response simulation. These models typically require a large amount of data and time for training, and the result is a simulation tuned specifically to this configuration. Meta-learning has emerged recently as a fast learning algorithm using small training datasets. In this paper, we use a meta-learning approach that “learns to learn” to generate showers from multiple calorimeter geometries, using a first-order gradient-based algorithm. We present MetaHEP, the first application of the meta-learning approach to accelerate shower simulation using very high granular data and using one of the calorimeters proposed for the Future Circular Collider (FCC), a next-generation of high-performance particle colliders.

Optimization strategies for the greenhouse gas consumption of the resistive plate chamber detectors at the CERN LHC experiments

Resistive Plate Chamber (RPC) detectors at the Large Hadron Collider (LHC) experiments are operated with a gas mixture containing two greenhouse gases (GHG): C2H2F4 (R134a) and SF6. These gases are used because they allow to achieve specific detector performance. However, due to their high Global Warming Potential (GWP) they are currently subject to a phase down policy that affects price and availability. To reduce the GHG usage, different strategies have been identified. As immediate actions, during the LHC Long Shutdown 2 (LS2) the RPC gas systems was upgraded to cope with new detector requirements and, in parallel, extensive campaigns for fixing leaks at detector level were performed. Since R134a dominates the GHG consumption, the development of a R134a gas recuperation plant is ongoing. A first prototype system was tested with encouraging results. A second prototype is under construction and it will be ready by beginning of 2023. For future long-term detector operation, R&D studies are ongoing for finding “green” alternatives to the currently used gases. Unfortunately, new gases developed by industry as refrigerant fluids are not behaving as the R134a in particle detectors which makes the replacement for present experiments difficult.