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15/09/2025, 09:00
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Joachim Kopp (CERN)15/09/2025, 09:151. Cutting Edge AI for Offline Data Processing
We propose to leverage artificial intelligence to advance event reconstruction in neutrino detectors. The first focus of the project is on atmospheric neutrino interactions in liquid argon detectors such as DUNE. These events often involve invisible particles like neutrons, yet kinematic correlations between visible and invisible final states enable robust reconstruction of the energy and...
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Stefano Camarda (CERN)15/09/2025, 09:201. Cutting Edge AI for Offline Data Processing
The usage of ML techniques in classification problems has become since long time a successful standard widely used across measurements and searches at colliders. However, most of the applications rely on simulated samples for training, while there is little or no experience of training based on data. While simulated samples provide nowadays very accurate modelling of physics signals and...
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Christian Sonnabend (CERN, Heidelberg University (DE))15/09/2025, 09:251. Cutting Edge AI for Offline Data Processing
Particle identification is a major task in any high energy physics experiment. With the challenging environments encountered in Run 3 & 4 particle identification for the ALICE TPC tracks has become a machine learning based task, showing significant improvements and flexibility compared to previous approaches. This projects aims to extend this idea in an experiment-agnostic way.
All LHC...
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Dr Thomas Poschl (CERN)15/09/2025, 09:301. Cutting Edge AI for Offline Data Processing
Assigning detector signals to individual particle objects is a central yet highly complex pattern-recognition problem in high-energy physics. Although conceptually similar to object detection in images—a domain where deep learning excels—such methods are still rarely applied to hit clustering in particle detectors.
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Within our project, we investigated the main challenges of using deep learning... -
Benjamin Huth (CERN)15/09/2025, 09:351. Cutting Edge AI for Offline Data Processing
Track reconstruction in high-energy physics experiments make use of algorithms based on least-squares techniques such as the Kalman Filter (KF) or the Global Chi2 fitter for track fitting, and the Combinatorial Kalman Filter (CKF) for track finding. These approaches exploit the fact that many sources of uncertainty can be approximated as normally distributed, and they perform extremely well...
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Noemi Calace (CERN)15/09/2025, 09:401. Cutting Edge AI for Offline Data Processing
Recent years have seen the rise of first close to competitive DL based track finding algorithms, most prominently through the use of Graph neural networks (GNNs). GNN have become the first competitive AI/DL architecture that comes close in physics performance to classical algorithms, effectively exploiting the capabilities of accelerators, mainly on GPUs but in recent research also on FPGAs....
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Eric Cano (CERN)15/09/2025, 09:451. Cutting Edge AI for Offline Data Processing
See full document here
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Eric Cano (CERN)15/09/2025, 09:501. Cutting Edge AI for Offline Data Processing
See full document here
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Sebastian Wuchterl (CERN)15/09/2025, 09:551. Cutting Edge AI for Offline Data Processing
Graphs and Transformers have shown a huge potential to improve reconstruction tasks. The improvement of jet tagging testifies how these architectures are particularly suitable for HEP problems. Nevertheless, the applications are still limited in scope. We propose to exploit GNNs and Transformers on multiple tasks, such as unified object reconstruction, vertex reconstruction, lepton...
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Tancredi Carli (CERN)15/09/2025, 10:001. Cutting Edge AI for Offline Data Processing
The precise calibration of jets can significantly boost the exploitation of the ATLAS data in the LHC run-3 and in the high luminosity phase. The precise knowledge of the jet energy scale (JES) is important for precision measurements like the top quark mass, better jet energy resolution and enhanced pile-up suppression can improve the discovery and measurement of Di Higgs...
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Farouk Mokhtar (Univ. of California San Diego (US)), Joosep Pata (National Institute of Chemical Physics and Biophysics (EE)), Marco Rovere (CERN)15/09/2025, 10:051. Cutting Edge AI for Offline Data Processing
Given the progress and promising results on ML-based particle flow integration with CMS offline reconstruction in a Run 3 setup (CMS-PFT-25-001), we now aim to extend and integrate MLPF with Phase-2 TICL reconstruction as a plug-in. In Run 3, we demonstrated that with a small transformer model, events containing on the order of ~5’000 tracks and clusters can be reconstructed into final-state...
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Peter McKeown (CERN)15/09/2025, 10:101. Cutting Edge AI for Offline Data Processing
Significant effort has recently been invested into developing tools for end-to-end Particle Flow (PF) reconstruction, both at LHC experiments and at Future Colliders. A major advantage of this approach is the potential for developing a tool which is detector agnostic. However, such approaches typically focus on higher level objects (tracks and calorimeter clusters), as operating directly on...
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Davide Valsecchi (ETH Zurich (CH))15/09/2025, 10:151. Cutting Edge AI for Offline Data Processing
New unbinned, higher-dimensional calibration methods for jet, tau, and lepton ID algorithms, simultaneous in multiple jet categories/flavors, within CMS.
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Alberto Bragagnolo (CERN)15/09/2025, 10:201. Cutting Edge AI for Offline Data Processing
Develop state-of-the-art NN for flavour inference of neutral B mesons for CKM CPV analyses, evolution of work performed in BPH-23-004. NNs are used as probability estimators, so perfect calibration is as important as performance. To be deployed in flagship BPH analyses.
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Benedikt Maier (K)15/09/2025, 10:251. Cutting Edge AI for Offline Data Processing
Development of AI-based reconstruction for the CMS High-Granularity Calorimeter using graph- and transformer-based models with TICL objects as inputs and using contrastive learning. In a first step, the work will tackle 2D/3D pattern recognition for particle showers. In a second stage, particle identification will be added using global event properties. The goal is to maximize physics...
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Dr Lena Maria Herrmann15/09/2025, 10:301. Cutting Edge AI for Offline Data Processing
Accurate event reconstruction is essential to fully exploit the physics potential of modern particle physics experiments. Particle Flow (PF) algorithms enhance reconstruction efficiency and resolution by combining information from multiple subdetectors. In particular, high-quality track information significantly contributes to the overall performance of particle object reconstruction.
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Future... -
116. Large Physics Model: a foundation model for HEP data reconstructions and analysis with CMS dataSebastian Wuchterl (CERN)15/09/2025, 10:352. Optimal AI deployment for Online Data Processing
Train foundation models on various tasks to using supervised and unsupervised techniques (e.g.,approach followed to train large LLMs like chatGPT)
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- Jet level: Allow a jet algorithm to self-discover patterns and physics properties on unlabelled data to obtain a pre-trained model unbiased from the Monte-Carlo discrepancy. The final goal would be to obtain a Foundation Model for jets that can... -
Maurizio Pierini (CERN)15/09/2025, 10:404. AI Infrastructure for Model Training
Several reconstruction steps in LHC events are being approached using end-to-end deep learning solutions (e.g., for tracking, calorimetry, and particle flow linking). It has been proposed that foundation models trained on physics events could repeat for LHC event reconstruction the astonishing success of Large Language Models in developing multitasking skills. Such an application could be...
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Michael Duehrssen-Debling (CERN), Nedaa Alexandra Asbah (CERN)15/09/2025, 11:057. Experimental Technologies
Run-3 and HL-LHC analyses require billions of events and numerous systematic variations, making full Geant4 simulation prohibitively slow; a calorimeter fast-simulation offers ~10× speed-up but remains less accurate for hadronic showers, particularly for sub-showers displaced from the shower axis. This project builds on advances from industry-scale image generative AI (e.g., diffusion and...
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Andreas Salzburger (CERN)15/09/2025, 11:104. AI Infrastructure for Model Training
Cutting edge AI/DL research, and algorithmic R\&D in general, profits immensely from openly accessible, realistic training data - in the field of HEP often paired and augmented with the relevant ground truth information. Examples for such datasets are the TrackML dataset (https://doi.org/10.1007/s41781-023-00094-w), which counts currently more than 100 citations in other research projects, the...
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Anna Zaborowska (CERN)15/09/2025, 11:151. Cutting Edge AI for Offline Data Processing
While numerous advances have been made in the simulation of electromagnetic showers with generative models, significantly less attention has been given to the simulation of hadronic showers. Simulation of these showers represents a significantly more complicated task, with showers featuring much larger event-to-event fluctuations due to the mix of hadronic and electromagnetic interactions, the...
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Anna Zaborowska (CERN)15/09/2025, 11:201. Cutting Edge AI for Offline Data Processing
Recent approaches to fast simulation have proposed directly operating on calorimeter showers in the form of a point cloud. This representation promises much improved efficiency compared to the regular grid based methods that are typically used, particularly for calorimeters which feature a very high granularity. Point clouds are also a very flexible representation, and form a compelling basis...
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Anna Zaborowska (CERN)15/09/2025, 11:251. Cutting Edge AI for Offline Data Processing
This project proposes to investigate, develop, and optimize a tool for placing pre-generated energy deposits (with ML models) into the highly granular CMS High Granularity Calorimeter (HGCal). Depending on the data representation of the deposits (e.g. point positions or voxelized volumes), efficient mapping to the detailed HGCal geometry is critical. The procedure must preserve shower...
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Anna Zaborowska (CERN)15/09/2025, 11:301. Cutting Edge AI for Offline Data Processing
This project aims to extend the lifecycle of the CaloChallenge [2411.05996] by turning it into a continuously updated, live benchmark for calorimeter shower simulation. The goal is to provide a long-term benchmark on an ML-challenge platform where new machine learning models can be submitted, evaluated, and compared in a consistent way. By tracking progress over time and highlighting novel...
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Anna Zaborowska (CERN)15/09/2025, 11:351. Cutting Edge AI for Offline Data Processing
Diffusion models are proven to be a good candidate for the generation of calorimeter showers. However, due to their slow inference occurring over several reverse diffusion steps, the base diffusion model often needs to be distilled to perform a single-step or few-step inference. Currently explored consistency distillation [2303.01469] works well for single-step inference over low-granularity...
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Peter McKeown (CERN)15/09/2025, 11:401. Cutting Edge AI for Offline Data Processing
Evaluation of generative models is a difficult task. In the domain of computer vision, the community has started to adopt LPIPS [1801.03924]. This requires a pretrained model capable of perceptually understanding the input. However, in the domain of fast simulation, no such pretrained model exists. Reasons include a lack of a challenging downstream supervised task, a lack of augmentations for...
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Michał Mazurek (National Centre for Nuclear Research (PL))15/09/2025, 11:451. Cutting Edge AI for Offline Data Processing
The [CaloChallenge][1] challenge was undertaken by the CERN-SFT group in the past few years, resulting in a [collaborative effort of 60 participants with different backgrounds][2]. The infrastructure presented in this challenge has already been adopted by the LHCb simulation project, and therefore we can use the models prepared by the community in our simulation framework. Other experiments...
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Peter McKeown (CERN)15/09/2025, 11:501. Cutting Edge AI for Offline Data Processing
Fast simulation of calorimeter showers has resulted in a surge of generative machine learning models showing excellent performance in mimicking Geant4. However, current models assume no information about the underlying geometry of the detector. The models are expected to learn the patterns in the geometry via information about the position of the incident particle or from the showers...
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Peter McKeown (CERN)15/09/2025, 11:551. Cutting Edge AI for Offline Data Processing
Generative machine learning models are becoming essential tools for fast simulation at collider experiments, providing a means to produce the vast amounts of simulated data required by physics programmes. These approaches are trained using full simulation input provided by Geant4, the state-of-the-art Monte Carlo simulation tool used throughout high energy physics. While Geant4 includes a...
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Peter McKeown (CERN)15/09/2025, 12:001. Cutting Edge AI for Offline Data Processing
Fast simulation of calorimeter showers with generative models has seen significant development in recent years, with many LHC experiments either having already deployed generative models for this purpose as part of their simulation work flows, or currently validating their models for production. At the core of fast simulation methods lies a balance between speed and accuracy, with a detailed...
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Anna Zaborowska (CERN)15/09/2025, 12:051. Cutting Edge AI for Offline Data Processing
This project proposes a development of a machine learning model to simulate punch-through particles - secondaries from calorimeter showers that exit the detector and enter downstream systems. While current fast simulation methods model more and more accurately in-calorimeter activity, they focus on cascades and calorimeters, limiting realism for studies involving muon systems or...
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Michał Mazurek (National Centre for Nuclear Research (PL))15/09/2025, 12:101. Cutting Edge AI for Offline Data Processing
The LHCb detector simulation processing time is dominated by the calorimeter simulation, however, a non-negligible part is spent for the simulation of optical photons in RICH detectors. Following the success of the CaloChallenge organized in close collaboration with the EP-SFT department, we would like to propose to organize a similar challenge targeting the use of ML-based optical simulation...
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Anna Zaborowska (CERN)15/09/2025, 12:151. Cutting Edge AI for Offline Data Processing
This project aims to develop machine learning models to parametrise the simulation of RICH detector images principally for use in LHCb, but with potential applications to other CERN experiments relying on Cherenkov-based particle identification, e.g. NA62. The goal is to create fast generative models that produce 2D ring-like photon hit patterns based on input parameters such as particle type,...
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Dr Sofia Vallecorsa (CERN)15/09/2025, 12:201. Cutting Edge AI for Offline Data Processing
This project addresses the growing need for scalable and efficient detector simulation in HEP, leveraging self-supervised learning and generative modeling to enable fast, generalizable simulations. By learning from unlabeled data and exploiting the intrinsic structure of detector responses, the proposed approach aims to reduce simulation time while maintaining physical fidelity. The project...
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Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Maurizio Pierini (CERN)15/09/2025, 12:251. Cutting Edge AI for Offline Data Processing
Detailed event simulation at the LHC is taking a large fraction of computing budget. CMS developed an end-to-end ML based simulation that can speed up the time for production of analysis samples of several orders of magnitude with a limited loss of accuracy. As the CMS experiment is adopting a common analysis level format, the NANOAOD, for a larger number of analyses, such an event...
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Michał Mazurek (National Centre for Nuclear Research (PL))15/09/2025, 12:301. Cutting Edge AI for Offline Data Processing
The CERN-SFT group, in their [summary paper][1], proposed that "a common end-to-end fast-simulation tool could be created across experiments to complement the GEANT library." Building on the experience gained by LHCb in developing its Flash Simulation framework, Lamarr, several key challenges have emerged in integrating machine learning (ML) algorithms into high-energy physics software...
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Michael Duehrssen-Debling (CERN), Nedaa Alexandra Asbah (CERN)15/09/2025, 12:357. Experimental Technologies
The standard Monte Carlo pipeline separates generation, detector simulation and reconstruction. This project advances an end-to-end generative approach that maps truth-level particles directly to reconstructed objects, reducing per-event runtime to ≪ 1 s by bypassing detailed detector simulation and algorithmic reconstruction. For correctly identified objects, the simulation of kinematic...
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Andrea Caputo (CERN)15/09/2025, 14:001. Cutting Edge AI for Offline Data Processing
DREAMS (DaRk mattEr with AI and siMulationS) is a state-of-the-art platform that combines thousands of high-resolution cosmological hydrodynamic simulations with machine learning to probe the nature of dark matter while marginalizing over uncertain baryonic physics. These simulations are run on the Flatiron Institute’s CCA cluster, supported by the Simons Foundation, anchoring DREAMS...
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Vitaly Magerya15/09/2025, 14:051. Cutting Edge AI for Offline Data Processing
The QCD theory group currently has a minor involvement with machine learning methods in learning amplitudes and optimizing amplitude evaluation via sector decomposition; the larger community however has greater expertise in applying ML to amplitude learning, optimization of integration procedures, etc, and is also making first steps towards optimizing the solution of integration-by-parts...
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Alexander Zhiboedov (CERN)15/09/2025, 14:101. Cutting Edge AI for Offline Data Processing
Recent advances in machine learning have given rise to a multitude of applications in physics, from jet tagging algorithms, to fast detector simulators or AI-driven symbolic regression and give us the perfect tool for tackling hard numerical problems for which classical algorithms are challenging to design. Is it well known that neural networks are universal function approximators and as such...
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Jacob Friedrich Finkenrath (CERN)15/09/2025, 14:151. Cutting Edge AI for Offline Data Processing
A longstanding problem in lattice QCD is critical slowing down by taking the continuum and infinite volume limit. One part, the critical slowing of solving the Dirac equation towards the continuum, is effectively solved with the introduction of very effective multi-level solvers. However, a universal solution for the other part, critical slowing down of the Markov Chain Monte Carlo (MCMC)...
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Jacob Friedrich Finkenrath (CERN)15/09/2025, 14:201. Cutting Edge AI for Offline Data Processing
Artificial Intelligence is emerging as a new paradigm of science having an impact on fundamental research. AI applications have the potential to be transformative and change the research process in High Energy Physics, i.e. from data processing to simulation, from theoretical exploration to the design and operation of detectors and accelerators. With the advent of large language models it...
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Giuliano Giacalone15/09/2025, 14:251. Cutting Edge AI for Offline Data Processing
CERN continues to stand as a world-leading laboratory in nuclear research. At the forefront of this effort is the ISOLDE facility, renowned for its cutting-edge studies of nuclear structure, alongside the Large Hadron Collider (LHC) with its program on high-energy nuclear collisions devoted to the exploration of the quark-gluon plasma, which is planned to continue at least until the end of LHC...
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Valerie Domcke (CERN)15/09/2025, 14:301. Cutting Edge AI for Offline Data Processing
The next generation of gravitational wave (GW) interferometers, in particular the Laser Interferometer Space Antenna (LISA) will revolutionize our ability to explore the Universe through Gws. However, there is a significant data analysis challenge that comes with this increased sensitivity. While current ground-based GW detectors are noise dominated with sparse signals due to merging compact...
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Josh Bendavid (CERN)15/09/2025, 14:351. Cutting Edge AI for Offline Data Processing
High performance maximum likelihood fitting and associated statistical analysis tools, initially based on Tensorflow 1 (combinetf) and used for precision measurements (CMS W helicity, W mass), now re-written with Tensorflow 2 (RABBIT) and being used for in-progress CMS alphaS measurement, etc. Older combinetf has also been used for some of the analyses in the FCC feasibility study
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Josh Bendavid (CERN)15/09/2025, 14:401. Cutting Edge AI for Offline Data Processing
Systematic variations based on explicit variation of kinematic quantities (e.g. shifting jet pT for Jet Energy Scale variations) is computationally inefficient in analysis workflows and can introduce additional statistical fluctuations in predictions. In cases where the probability density of the corresponding response distribution is known, these variations can be replaced with event weights...
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Jonas Rembser (CERN)15/09/2025, 14:454. AI Infrastructure for Model Training
To get the most information out of the LHC dataset, a physics analysis has to be globally optimized from event generation to statistical inference.
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Automatic Differentiation (AD) is one of the cornerstones of ML/AI. Applying AD to simulation and analysis codes is also very appealing for optimizing next-generation HEP analysis. For example, augmenting simulated data with gradients helps to... -
Michał Mazurek (National Centre for Nuclear Research (PL))15/09/2025, 14:501. Cutting Edge AI for Offline Data Processing
Modelling of MIB (Machine Induced backgrounds) to provide particles originating from losses entering the LHCb cavern. Beam losses of primary concern are beam halo on tertiary collimators close to the IP and beam gas interaction in the tunnel within a few hundred meters from the IP. This requires the determination of the beam losses based on beam optics and vacuum conditions through dedicated...
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Peter McKeown (CERN)15/09/2025, 14:551. Cutting Edge AI for Offline Data Processing
This project proposes to study a replacement of the solution like the one implemented currently in LHCb, where beam-induced background (BIB) particles are sampled from very large FLUKA output files, with a modern machine learning–based generator. Instead of relying on repeated access to stored datasets, a trained generative model would learn the distributions of BIB particles and produce new...
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Sebastian Wuchterl (CERN)15/09/2025, 16:007. Experimental Technologies
While the usual attention mechanism successfully introduced edge features allowing to compute efficiently the inter-connection between two elements, one could consider more-object connections via a simplex system, which would generalize the concept of attention to any higher dimension, allowing a “hyper-graph” like attention model; see, e.g., arXiv:2309.02138.
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Christine Zeh (Vienna University of Technology (AT))15/09/2025, 16:057. Experimental Technologies
Event reconstruction at the HL-LHC requires combining hits into clusters and linking them with tracks to form higher-level objects. This process is inherently multi-step and local, which risks globally suboptimal results when pile-up is high or when showers overlap. Current machine learning methods, like graph neural networks and transformers, already exploit relational structures, with recent...
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Maurizio Pierini (CERN)15/09/2025, 16:107. Experimental Technologies
Energy efficiency, while lowering the barrier to incorporating emerging device technologies into muture generations of computing systems must achieve higher processing speed and energy efficiency to support rapidly growing workloads under strict environmental constraints. To address this, domain-specific hardware accelerators have gained traction, with in-memory computing (IMC) emerging as a...
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Ms Ema Puljak (The Barcelona Institute of Science and Technology (BIST) (ES)), Maurizio Pierini (CERN)15/09/2025, 16:157. Experimental Technologies
Develop applications based on tensor networks for LHC tasks. As part of this effort, develop the tn4ml library, to train TNs with tools used for deep learning applications.
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Jacco Andreas De Vries (Nikhef National institute for subatomic physics (NL)), Dr Nicole Skidmore (University of Warwick)15/09/2025, 16:207. Experimental Technologies
The advent of the High-Luminosity LHC presents unprecedented computational challenges for the LHCb experiment, pushing the limits of classical algorithms in areas such as real-time data filtering, complex track reconstruction, and multidimensional analysis. To address this we propose a dedicated initiative to expand upon LHCb’s pioneering application of Quantum Machine Learning (QML) for b-jet...
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Dr Michele Grossi (CERN), Dr Sofia Vallecorsa (CERN)15/09/2025, 16:257. Experimental Technologies
Monte Carlo (MC) event generators are indispensable in High-Energy Physics (HEP) for simulating scattering processes and sampling the multidimensional phase space according to the differential cross section dσ. Since dσ is not known analytically in full generality, event generators must determine both the local structure of the integrand and the global phase space distribution through adaptive...
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Jogi Suda Neto (University of Alabama (US))15/09/2025, 16:307. Experimental Technologies
Variational quantum algorithms (VQAs) offer a promising approach for near-
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term quantum devices but often suffer from trainability issues such as barren
plateaus. While certain VQAs can avoid these problems, they are typically
classically simulable and thus of limited quantum advantage. This project explores the use of pre-training as a warm-starting strategy for VQAs that are... -
Mr Barthelemy Von Haller (CERN)16/09/2025, 09:003. AI for metadata analysis
Data Quality Control (QC) in ALICE encompasses both the online Data Quality Monitoring (DQM) and the offline Quality Assurance (QA), running synchronously and asynchronously with the data taking.
The goal of this exploratory project is to enhance and expand the ALICE QC framework with Machine Learning and Artificial Intelligence techniques. Other LHC experiments have already started...
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Pedro Vieira De Castro Ferreira Da Silva (CERN)16/09/2025, 09:053. AI for metadata analysis
Support for the production and commissioning of HL-LHC detectors. Provide tools to reduce the amount of time needed to check correct functioning by having machine learned algorithms parse the data, building on the successful experience with anomaly detection for HGCAL sensors. Use for raw data, but also for configuration data, at different levels of integration and for different steps in...
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Matteo Concas (CERN), Vasco Barroso (CERN)16/09/2025, 09:103. AI for metadata analysis
Computing infrastructures for LHC experiments, including their online high-level-trigger (HLT) farms and offline reconstruction facilities, generate a massive volume of complex telemetry, with logs and metadata often exceeding 100 TB per day.
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Managing these distributed systems at scale reveals already the limitations of the traditional monitoring. Static, rule-based alerting is insufficient... -
Tassilo Rauschendorfer (Politecnico di Milano (IT))16/09/2025, 09:153. AI for metadata analysis
The AE$\bar{g}$IS experiment at CERN's Antiproton Decelerator performs research using antiprotons and positrons, utilizing detectors and methods commonly used in atomic and nuclear physics experiments [1,2,3,4].
Designed for flexibility and scalability in the number of interconnected hardware components, the AE$\bar{g}$IS control system has been implemented and proven to work fully...
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Dr Thomas Poschl (CERN)16/09/2025, 09:203. AI for metadata analysis
Efficient online monitoring of the data quality and the detector control system is essential for the smooth operation of any high-energy physics experiment. However, much of this responsibility still relies on manual shifter activity. To reduce workload and increase reliability, we explored artificial intelligence methods that automatically detect unusual patterns in monitoring plots and...
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Jaroslaw Szumega (CERN EP-DT-DD, Mines ParisTech (FR))16/09/2025, 09:253. AI for metadata analysis
The IRRAD Proton Facility, located on T8 beamline at CERN PS East Hall, is an experimental infrastructure. It hosts irradiation tests and experiments of various nature, focusing especially on the ones dedicated to the development of equipment and electronics for High-Energy Physics (HEP) experiments.
Over years of operation, multiple data-management systems were designed and implemented to...
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IT-CA-IR/OSI16/09/2025, 09:303. AI for metadata analysis
This proposal extends the foundations laid by the AIRDEC project (IT-CA/SIS) to develop a robust AI agent infrastructure that ensures safe, reliable, and transparent use of AI across research repository and library services. Building on this foundation, the project will expand the AI support into critical areas of high cost and human effort, such as spam detection and mitigation, automated...
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Pierfrancesco Cifra (CERN)16/09/2025, 09:357. Experimental Technologies
The LHCb data centre is a key element of the experiment’s Data Acquisition (DAQ) system, while also supporting other computing tasks when not dedicated to DAQ. This project investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to further improve its efficiency and sustainability. It focuses on two main aspects: Cooling Optimization, evaluating AI...
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Benedict Kamoni Njoki (University of Nairobi (KE))16/09/2025, 09:403. AI for metadata analysis
The operational control layer in the Experiment Control System (ECS) of the LHCb experiment is built on WinCC Open Architecture (OA), which generates large volumes of logs. Currently, operators and shifters examine these logs manually to identify system errors. This process is time-consuming, tedious, and requires expert knowledge.
Patterns in the logs are not easily discernible, making it...
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Titus Mombächer (University of Cincinnati (US))16/09/2025, 09:453. AI for metadata analysis
Data Quality assessment - choosing which data are good for physics and which are not - is an ideal use case for anomaly detection algorithms.
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For assessing LHCb data quality we need a tool that takes a data quality decision and justifies them to make the decisions traceable.
Currently histograms of observables ranging from low-level sub-detector quantities up to high-level physics quantities... -
Andreas Salzburger (CERN)16/09/2025, 09:507. Experimental Technologies
Optimization of detector design using AI/DL, either through exploiting fully differentiable programming models or through the help of intelligent agents has been a growing field of interest in the recent years. This was spearheaded by the MODE collaboration (https://mode-collaboration.github.io/) and other research groups (https://doi.org/10.3390/particles8020047). While full end-to-end...
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Andreas Salzburger (CERN), Martin Aleksa (CERN), Nikiforos Nikiforou (CERN)16/09/2025, 09:557. Experimental Technologies
Building on extensive experience with the ATLAS LAr Calorimeters, the CERN ATLAS Team is leading R&D on noble-liquid ionization calorimetry for future colliders. Within the ALLEGRO detector concept for FCC-ee [1], we are developing a liquid-argon electromagnetic calorimeter with lead absorbers and advanced multi-layer PCB electrodes. A central challenge is optimizing detector granularity: the...
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Axel Naumann (CERN), Christine Zeh (Vienna University of Technology (AT)), Leonardo Beltrame (Politecnico di Milano (IT))16/09/2025, 10:007. Experimental Technologies
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Lubos Krcal (CERN)16/09/2025, 10:053. AI for metadata analysis
The current ALICE HPC farm operates with static resource partitioning between workloads. While this guarantees robustness, it also limits the ability to fully capitalize on the available capacity. Given the heterogeneous workloads from online (DAQ) to offline (async) processing, as well as cloud services (e.g. OpenStack, including AI/ML training), there is an opportunity to introduce machine...
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Dr Sofia Vallecorsa (CERN)16/09/2025, 10:554. AI Infrastructure for Model Training
Training large-scale generative models for particle detector simulation is computationally demanding, contributing significantly to energy consumption. This project focuses on developing energy-efficient training strategies for generative models used in detector simulation. By integrating energy-aware optimization strategies, mixed-precision training and sustainability metrics, the project...
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Sebastian Wuchterl (CERN)16/09/2025, 11:004. AI Infrastructure for Model Training
Development of a cutting-edge Deep Learning framework for HEP objects and analysis tasks, automatising the tasks with optimized data structure, CPU overhead, and GPU usage. The functionalities include model and feature modularity, benchmarking, hyperparameter optimization, distributed running, optimized data structures, data loading, and inference optimization. One option as a baseline...
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Apostolos Karvelas (CERN)16/09/2025, 11:054. AI Infrastructure for Model Training
This project focuses on establishing a dedicated MLOps environment tailored to the needs of the online operations of the LHCb experiment. Its goal is to enable the development, optimization, and deployment of machine learning models entirely within the LHCb technical network, using LHCb-managed resources and directly supporting online workflows.
The first phase of the project, focused on...
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Stephan Hageboeck (CERN)16/09/2025, 11:104. AI Infrastructure for Model Training
In the HL-LHC era, ever larger datasets for ML training are in sight. These will enable the training of increasingly complex models, but the sheer volume of data may exhaust the capabilities of the machines that run the training. The data might neither fit in RAM, nor might saving the data on fast storage be cost-effective.
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In this project, ROOT and the existing CERN infrastructure such as... -
Dr Vincenzo Eduardo Padulano (CERN)16/09/2025, 11:154. AI Infrastructure for Model Training
Training ML models on High Energy Physics data currently requires either very expensive copies and conversion to some intermediate format or creation of custom I/O pipelines for the end user. ROOT provides a prototype system for ingestion of data in the common TTree format (which also supports the future RNTuple format) directly into the ML model. This requires zero conversion steps and is...
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Andre Sailer (CERN)16/09/2025, 11:204. AI Infrastructure for Model Training
AI/ML tools evolve quickly, new versions and new packages are constantly being created. Providing new and updated packages in a consistent manner and for a distributed environment takes dedicated effort to avoid scalability issues. The LCG software stacks provide a wide range of AI/ML and related packages via CVMFS such as tensorflow, torch, jax, CUDA, and ROOT. As part of the RCS/AI...
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Valentin Volkl (CERN)16/09/2025, 11:254. AI Infrastructure for Model Training
Modern AI training for complex neural networks demands low-latency access to multi-petabyte datasets, versioned software stacks, and reproducible environments, mirroring challenges traditionally addressed by CVMFS in scientific domains. While at its core a software distribution tool, CVMFS can provide a general filesystem view on external data in object stores. This data-distribution over...
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Raulian-Ionut Chiorescu, Ricardo Rocha (CERN)16/09/2025, 11:304. AI Infrastructure for Model Training
As AI/ML usage and use cases grow at CERN, training at scale as well as testing, benchmarking and validation on newer generation devices requires access to resources not currently available on-premises.
This activity involves setting up the required integrations in the CERN MLOps infrastructure to accommodate these requirements as seamlessly as possible. The work considers integration with...
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Lena Maria Herrmann16/09/2025, 11:354. AI Infrastructure for Model Training
Event reconstruction is key to unlocking the full physics potential of the Future Circular Collider (FCC). Particle Flow (PF) techniques, which combine information from different subdetectors, rely on precise and well-understood inputs. Classical approaches often use hand-crafted features and detector-specific preprocessing, but machine learning (ML) methods require a different level of...
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David Gutierrez Rueda (CERN), Eric Grancher (CERN)16/09/2025, 11:404. AI Infrastructure for Model Training
While the infrastructure supporting AI/ML can be in the cloud, or use the existing HPC resources; this proposal considers the need to support on-premises AI/ML workloads with stringent requirements of performance, bandwidth, latency and lossless communication over Ethernet.
If CERN/RCS strategy for IA includes the support of high-performance resources in CERN's Datacentres for AI/ML...
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Matteo Bunino (CERN), Dr Maria Girone (CERN)16/09/2025, 11:454. AI Infrastructure for Model Training
This proposal focuses on the further development and adoption of the itwinai framework, designed to help scientists scale their AI workloads on HPC and cloud systems while minimizing engineering overhead. itwinai provides high-level, reproducible workflows for distributed machine learning training and hyperparameter optimization using tools such as PyTorch DDP, DeepSpeed, Horovod, and Ray...
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Ricardo Rocha (CERN)16/09/2025, 11:505. Infrastructure for AI Deployment
The existing MLOps offering in CERN IT covers the requirements for data preparation, iterative development, training and inference. It enables integration with existing infrastructure via APIs or by directly embedding the models.
This activity will focus on an extension of these environments to enable the deployment and management of agents as well as the required components to standardize...
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Danilo Piparo (CERN)16/09/2025, 11:555. Infrastructure for AI Deployment
A lot of attention and care is dedicated to code and calibrations used for official data processing campaigns of experiments, such as event generation, simulation, reconstruction, or derivation. The same level of care should be dedicated to trained ML models deployed as part of the aforementioned data processing steps. Such entities should be easily findable, documented, versioned, and...
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Ioannis Xiotidis (CERN), Thorsten Wengler (CERN)16/09/2025, 12:005. Infrastructure for AI Deployment
This proposal outlines a structured R&D programme to develop a standardised test-bed infrastructure for evaluating heterogeneous hardware solutions targeting Machine Learning (ML) model deployment in High Energy Physics (HEP) Trigger and Data Acquisition (TDAQ) systems, and using it to evaluate available hardware acceleration options. The test-bed will support benchmarking of co-processors...
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Ioannis Xiotidis (CERN), Thorsten Wengler (CERN)16/09/2025, 12:055. Infrastructure for AI Deployment
The growing demand for fast and reliable Machine Learning (ML) inference in hardware triggers of High Energy Physics (HEP) experiments introduces new challenges in terms of model development, deployment, and long-term sustainability. This proposal aims to develop a generic, CERNwide ML Operations (MLOps) framework that enables end-to-end support for ML model lifecycles targeting Field...
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Amine Lahouel (CERN)16/09/2025, 12:105. Infrastructure for AI Deployment
Storage and versioning of models, especially when handling a large number (1k to 10k+) needs specialized services. Traceability of the published models back to their training executions and parameterization is essential to offer trust and reproducibility.
This initiative will build on the ongoing NGT effort of offering a centralized mlflow instance and extend it to the whole CERN community,...
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Hannes Jakob Hansen16/09/2025, 12:155. Infrastructure for AI Deployment
The current procurement process for accelerator devices is done targeting specific vendors and models, in contrast with the generic procurement of CPU hardware. This limits the possibilities for vendors to optimize their offers with different layouts.
This activity focuses on establishing a benchmark suite with reference AI/ML workloads that can be used to augment or as alternatives to the...
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Valentin Volkl (CERN)16/09/2025, 12:205. Infrastructure for AI Deployment
The infrastructure to deploy both training data and final models in a distributed computing environment like the WLCG is essential in order to make optimal use of ML/AI in offline computing. CVMFS is the de-facto standard to deploy software binaries, and could bring its advantages to ML operations, in particular with respect to software preservation.
As ML models used for inference are...
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Andre Sailer (CERN)16/09/2025, 12:255. Infrastructure for AI Deployment
Developing new AI/ML uses is important, but it is equally important to ensure they can be used in production environments once they mature. For HEP experiments this means they need to integrate seamlessly into the respective software frameworks. For this purpose the integration into the experiment datamodels and frameworks has to be developed. The key4hep software ecosystem uses the Gaudi...
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Dr Vincenzo Eduardo Padulano (CERN)16/09/2025, 12:301. Cutting Edge AI for Offline Data Processing
With the proliferation of different ML strategies being employed in HEP analysis workflows, the question of ensuring smooth integration with existing analysis tools is of paramount importance. Many aspects may subtly hinder the user experience and potentially block analysis development: the Python/C++ integration of the framework, on-disk vs in-memory representation of the physics events (with...
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Sanjiban Sengupta (CERN, The University of Manchester)16/09/2025, 12:355. Infrastructure for AI Deployment
With the upcoming HL-LHC phase, optimizing machine learning inference becomes a critical challenge for data processing at CERN. Efficient inference requires not only fast algorithms for the underlying operations within ML models but also careful use of heterogeneous computing architectures. This includes minimizing data transfers between CPUs, GPUs, and specialized accelerators, as well as...
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Lukasz Michalski (Wroclaw University of Science and Technology (PL))16/09/2025, 12:405. Infrastructure for AI Deployment
Current CMSSW workflows suffer from inefficient CPU-GPU data transfers when running machine learning models, leading to significant overhead. It can add up to several hundreds of milliseconds per event, which is a big issue, especially in real-time environments such as at trigger level. This reduces performance and scalability, making it harder to fully leverage ML in CMS operations.
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Our... -
Maarten Van Veghel (Nikhef National institute for subatomic physics (NL)), Michał Mazurek (National Centre for Nuclear Research (PL))16/09/2025, 12:455. Infrastructure for AI Deployment
Gaudi is a common software framework underlying event processing in multiple experiments like ATLAS, LHCb and FCC. In addition, the simulation framework Gaussino (used by LHCb and FCC) is another user of Gaudi. As machine learning becomes increasingly central to real-time data processing, simulation and physics analysis, integrating diverse ML software stacks into Gaudi in a sustainable and...
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Maarten Van Veghel (Nikhef National institute for subatomic physics (NL)), Michał Mazurek (National Centre for Nuclear Research (PL)), Dr Nicole Skidmore (University of Warwick)16/09/2025, 12:505. Infrastructure for AI Deployment
As more and more ML models get used in production, like in real-time data processing and simulation, having infrastructure for reliable and fast turnaround of model retraining and deploying is crucial. To this end, a centralized CI/CD infrastructure and model storage, within LHCb, needs to be developed further as current solutions don’t scale well. In addition, user friendliness needs to be...
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Mario Lassnig (CERN), Martin Barisits (CERN)16/09/2025, 12:555. Infrastructure for AI Deployment
High energy physics at large undergoes - similar to other domains - a paradigm shift to ever increasing importance of AI/DL applications. The success of these sophisticated techniques will depend heavily on our ability to manage, access, and trace data in a manner that is both performant and trustworthy.
This document puts forward a proposal for a research and development programme to...
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Maciej Mikolaj Glowacki (CERN)16/09/2025, 14:002. Optimal AI deployment for Online Data Processing
ML deployment in the CMS Level-1 Trigger follows a compute-intensive pipeline involving data acquisition (detector and simulation), preprocessing, training, firmware synthesis, and deployment to online (FPGA) and offline (CMSSW emulator) environments. This project aims to streamline this chain across heterogeneous compute platforms to enable frequent model updates, in particular during HL-LHC...
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Maarten Van Veghel (Nikhef National institute for subatomic physics (NL))16/09/2025, 14:052. Optimal AI deployment for Online Data Processing
With the high demands on throughput of real-time data processing, in some cases, even existing fast ML inference libraries are not fast enough. Currently, hard-coded solutions native to the LHCb event model and algorithmic structure still win, both at GPU and CPU level. The goal is to develop solutions that have all these benefits but scale better and reduce the maintenance burden by for...
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Sioni Paris Summers (CERN)16/09/2025, 14:102. Optimal AI deployment for Online Data Processing
Development of libraries to deploy NNs and BDTs on FPGAs and front-end electronics
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Dr Thomas Poschl (CERN)16/09/2025, 14:152. Optimal AI deployment for Online Data Processing
Modern high-energy physics experiments generate large data rates, requiring fast and efficient online processing. Embedding machine–learning–based feature extraction directly in the front-end electronics of the detectors is a promising approach to reduce the amount of data to be transmitted. Field-Programmable Gate Arrays (FPGAs) offer a unique platform for such tasks due to their parallelism,...
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Dimitrios Danopoulos (CERN)16/09/2025, 14:205. Infrastructure for AI Deployment
The project proposes to evaluate and integrate emerging AI-specific computing hardware, such as AMD Versal devices (often referred to as "AI Engines"), into real-time inference workflows relevant to HL-LHC experiments. With active interest and efforts already underway in experiments such as ATLAS and CMS, and ATLAS planning procurement of such hardware, this work seeks to establish a common...
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Roope Oskari Niemi16/09/2025, 14:252. Optimal AI deployment for Online Data Processing
Training library including compression techniques for NNs, such as heterogenous quantization, hyperparameter optimization, pruning, etc.
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Amine Lahouel (CERN)16/09/2025, 14:302. Optimal AI deployment for Online Data Processing
Model inference often has to be restricted by the use of shared GPU resources or targeting specialized hardware like FPGAs or edge devices. Without optimization a single model can monopolise memory and compute and manual optimization can take weeks or months.
This activity will focus on the integration of automated optimization pipelines with pre-configured recipes targeting GPUs, FPGAs and...
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Davide Di Croce (CERN)16/09/2025, 14:352. Optimal AI deployment for Online Data Processing
Over the past year, the ATLAS muon group has successfully incorporated machine learning (ML) techniques to improve the identification of hits in the ATLAS Muon Spectrometer (MS) originating from primary vertices, while effectively rejecting noise and background muons. These advancements are critical to address the challenges posed by the extreme operating conditions during Phase-2 of the High...
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Christian Sonnabend (CERN, Heidelberg University (DE))16/09/2025, 14:402. Optimal AI deployment for Online Data Processing
The Kalman-Filter tracking algorithm has proven great success within the history of LHC experiments. While performing very well at moderate occupancies both for track finding and fitting, the combinatorics becomes prohibitive at high occupancies. This is particularly true at the initial phase of the tracking when the seed parameters are not constrained yet and brute force combinatorial seeding...
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André David (CERN)16/09/2025, 14:452. Optimal AI deployment for Online Data Processing
End-to-end optimization of trigger system architectures implementing distributed deep learning (DL) across all layers. Integrated within low-latency, high-speed Field Programmable Gate Arrays (FPGAs) and Application-Specific Integrated Circuits (ASICs), the goal is to use on-detector DL-based encoded data directly. Additionally, enable end-to-end reconstruction of physics objects - such as...
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Arne Christoph Reimers (CERN)16/09/2025, 14:502. Optimal AI deployment for Online Data Processing
Development, characterization, and integration of ML models to reject common-mode noise in the CMS HGCAL for the HL-LHC.
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Sioni Paris Summers (CERN)16/09/2025, 14:552. Optimal AI deployment for Online Data Processing
This proposal explores the feasibility and potential of custom AI chips for both front-end and back-end electronics in High Energy Physics (HEP) experiments. The objective is to investigate whether AI-enabled devices can provide efficient data reduction by compression, filtering, and reconstruction close to the detector, and to define the technological and research pathways toward AI-enabled...
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Maciej Mikolaj Glowacki (CERN)16/09/2025, 15:002. Optimal AI deployment for Online Data Processing
Training, deploying, operating, and analysing anomaly detection triggers in the CMS Level 1 Trigger. In Run 3 CMS is using the AXOL1TL and CICADA triggers to collect anomalous events towards unbiased new physics searches. For the Phase 2 Upgrade, anomaly detection will benefit from the higher fidelity information provided by performing PF and PUPPI reconstruction in the L1T. Research is...
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Maurizio Pierini (CERN)16/09/2025, 15:052. Optimal AI deployment for Online Data Processing
Inspired by the success of hls4ml in translating high-level machine learning models into FPGA firmware, this initiative envisions training and adapting LLMs capable of producing hardware description language (HDL) code, such as Verilog or VHDL, directly from algorithmic or high-level specifications. While the initial motivation is to enable neural network deployment, the scope extends to a...
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Francesco Vaselli (Scuola Normale Superiore & INFN Pisa (IT)), Maurizio Pierini (CERN)16/09/2025, 15:102. Optimal AI deployment for Online Data Processing
From the preliminary work of https://arxiv.org/abs/2508.11594 , we would like to deploy agnostic, anomaly detection triggers based on continuos flows, for the first time in an experiment. Additionally, we would like to investigate possible applications of other ML algorithm based on vector fields, such as Diffusion Models, for the task of anomaly detection in triggers, while innovating and...
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Elias Leutgeb (CERN)16/09/2025, 15:152. Optimal AI deployment for Online Data Processing
Anomaly detection plays a key role as a novel strategy for trigger systems and real-time data analysis. This project, funded by Oracle Corporation and CERN openlab, focuses on developing and deploying AI models on the latest generation of AMD FPGAs (Versal with "adaptive intelligence" AI engine accelerators), for the L1 scouting system of CMS at the HL-LHC. The goal of this project is to...
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Ioannis Xiotidis (CERN), Markus Elsing (CERN), Thorsten Wengler (CERN)16/09/2025, 15:202. Optimal AI deployment for Online Data Processing
This proposal outlines a research programme to develop low-latency, unsupervised anomaly detection algorithms for deployment within the hardware trigger systems of modern High Energy Physics (HEP) experiments. Focusing on calorimeter data, the goal is to identify rare or previously unseen physics signatures that evade standard trigger logic, using methods such as Auto-Encoders (AE),...
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Sebastian Wuchterl (CERN)16/09/2025, 15:252. Optimal AI deployment for Online Data Processing
CMS is investing resources in the scouting stream but its use so far has been limited to a few applications, mostly with jets and muons. Scouting has more potential than that, particularly with its extension at L1, being investigated in Run 3 and to reach its best in HL-LHC. One of the limiting factor towards a broad use of scouting is the resolution loss online also due to resource...
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Sioni Paris Summers (CERN)16/09/2025, 15:302. Optimal AI deployment for Online Data Processing
Ongoing activities include, but are not limited to:
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Jet tagging [CMS-DP-2025-032]
Electron reconstruction [CMS-DP-2023-047, CMS-DP-2024-098]
Vertex reconstruction (usage in correlator) [CMS-DP-2021-035, CMS-DP-2022-020]
ML for Puppi
Event selection (especially VBF, di-Higgs)
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Lorenzo Santi (CERN), Markus Elsing (CERN)16/09/2025, 15:352. Optimal AI deployment for Online Data Processing
Current flavour-tagging algorithms at the LHC rely on reconstructed tracks to capture the signatures of displaced heavy-flavour decays. This approach requires a full track reconstruction, which is computationally expensive and not available at the earliest trigger levels in ATLAS. In this work we explore the potential of hit-based b-tagging, i.e. exploiting raw hit patterns in silicon trackers...
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Ioannis Xiotidis (CERN), Thorsten Wengler (CERN)16/09/2025, 15:402. Optimal AI deployment for Online Data Processing
This proposal outlines an R&D programme to integrate Machine Learning (ML) models into existing heuristic-based trigger and reconstruction algorithms in High Energy Physics (HEP) experiments. Using ATLAS Phase-II as an example and extending towards the Future Circular Collider (FCC) era, the goal is to demonstrate how ML can improve performance of traditional well understood algorithms in...
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Jolly Chen (CERN & University of Twente (NL))16/09/2025, 15:452. Optimal AI deployment for Online Data Processing
In the HL-LHC era, we expect a significant increase in the amount of data and compression can serve as an effective tool to reduce storage requirements. As more and more computing facilities are equipped with GPUs, enabling lossless (de)compression directly on GPUs can reduce costly memory transfers to/from the GPU, remove reliance on the CPU for compression tasks, and increase the effective...
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Sioni Paris Summers (CERN)16/09/2025, 15:502. Optimal AI deployment for Online Data Processing
ML on Earth Observation satellites for data reduction, and monitoring of plastics pollution in the oceans.
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Maximiliano Puccio (CERN)16/09/2025, 16:206. Large Language Models-based assistants
We propose an on-premises, agentic system built on open-weight models to automate repetitive tasks in grid-based analyses and MC production, cutting manual effort for submitting jobs for both operators and experts. The agent integrates with existing middleware (in the case of ALICE: Hyperloop, MonALISA, and jAliEn), using retrieval-augmented generation to interpret production and analyses...
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Mr George Raduta (CERN)16/09/2025, 16:256. Large Language Models-based assistants
The ALICE O² Bookkeeping system provides essential log entries of activities in the experimental area, along with run, quality of run, environments, LHC Fills metadata, statistics, and quick-visualization plots. While powerful, the system requires frequent updates to its graphical user interface and demands familiarity with API queries or manual navigation, which can be a barrier for shifters...
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Carlos Solans Sanchez (CERN)16/09/2025, 16:306. Large Language Models-based assistants
The operation and maintenance of the experiments like ATLAS require expertise across many domains, particularly during interventions or unexpected events. While much knowledge is documented in CERN’s Engineering Data Management Service (EDMS), the system is fragmented, with limited metadata and diverse formats that hinder quick access. To address this, the Expert System tool...
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Titus Mombächer (University of Cincinnati (US))16/09/2025, 16:356. Large Language Models-based assistants
The code used in the LHCb data processing frameworks is becoming more and more complex to enhance flexibility while maintaining high performance. This leads to a steep learning curve for beginners and relatively few people with a complete overview over the experiment’s software, leading to a gap between the necessary coding skills to write performance code and the number of people willing to...
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Dr Nicole Skidmore (University of Warwick)16/09/2025, 16:406. Large Language Models-based assistants
LHCb has a vast and distributed volume of documentation, software, and operational knowledge, creating a significant barrier to entry for new researchers and a persistent challenge for information retrieval. To address this "knowledge-access gap", we propose the development of LHCb-GPT, a specialized LLM designed to serve as an intelligent assistant for LHCb researchers. We will develop a...
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Dr Nicole Skidmore (University of Warwick)16/09/2025, 16:456. Large Language Models-based assistants
Physics analysis at the LHCb experiment demands that researchers possess a dual expertise: deep knowledge of particle physics and mastery of a complex, ever-evolving software stack. This necessity creates a significant bottleneck, steepening the learning curve for new collaborators and diverting experienced physicists' time from discovery to software engineering. To address this challenge, we...
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Georgios Karathanasis (CERN)16/09/2025, 16:506. Large Language Models-based assistants
Content: within the CMG group, there is an ongoing effort to explore commercial and open-weight LLMs for editorial work at CERN: We propose two specific tasks to help paper editing and proof reading:
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identify wrong scientific statements, wrong constant values, etc. (leaving the correction to the user);
fix grammar in the text, making plots according to pre-defined style, etc.
Ultimately,... -
Micha Moskovic (CERN)16/09/2025, 16:556. Large Language Models-based assistants
Publications are the primary vehicle for the transmission of scientific results. To convey these results in the clearest and most accurate way, researchers at CERN spend significant efforts on ensuring the quality of their prose. These efforts are particularly important for large experimental collaborations, where a handful of people in each publication committee are responsible for editing...
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Dr Maria Arsuaga Rios (CERN)16/09/2025, 17:006. Large Language Models-based assistants
The goal is to streamline support and reduce overheads across large communities using HEP technologies and navigating complex service environments (e.g., EOS, ROOT, ATLAS, CMS). By enabling natural-language assistants in the shared Discourse AI platform and context-aware queries, users will be able to query documentation and extract operational knowledge more intuitively, lowering the...
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Maurizio Pierini (CERN)16/09/2025, 17:054. AI Infrastructure for Model Training
There is a growing interest in developing a dedicated CERN chatbot, based on a Large Language Model (LLM). A first example in this direction is the accGPT project. To make such a service functional to the experimental physics community, the chatbot capabilities should include experiment-specific functions, such as editing paper in the style of a certain collaboration, searching internal...
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Alexandre Franck Boyer (CERN)16/09/2025, 17:106. Large Language Models-based assistants
The LHCb experiment increasingly relies on volunteer shifters, physicists, and developers to operate and analyse data, yet existing tools (e.g., LHCbDIRAC, LHCbPR) are often unintuitive, poorly documented, and generate cryptic errors that demand expert intervention. This mismatch creates cognitive overload, reduces motivation, and hampers productivity across the collaboration.
We propose an...
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Ismael Posada Trobo (CERN)16/09/2025, 17:156. Large Language Models-based assistants
The rapid evolution and the growing demand for AI-powered development tools present both unprecedented opportunities and significant challenges for organizations. As AI technologies become integral to research, product development, and operational workflows, the need for a structured approach to manage these tools is essential.
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This project explores the usage and governance of AI-powered... -
Ricardo Rocha (CERN)16/09/2025, 17:206. Large Language Models-based assistants
While core physics use cases currently rely on small models, other CERN use cases already ask for integration with pre-built LLMs with required fine tuning for different purposes.
This initiative builds on previous efforts in this area, such as AccGPT, to offer a centralized service with a catalog of pre-built models and the required integrations for selection, serving and fine-tuning using...
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Danilo Piparo (CERN)16/09/2025, 17:256. Large Language Models-based assistants
As LLMs are increasingly used to help researchers design and implement HEP analyses, it is essential to understand beyond anecdotal evidence their strengths and weaknesses with respect to common coding tasks in HEP. To this end, a set of several tens of typical ROOT-specific questions should be sampled, e.g. from the ROOT forum. The questions should span different categories (e.g., physics,...
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Maurizio Pierini (CERN)16/09/2025, 17:302. Optimal AI deployment for Online Data Processing
Study techniques to compress LLMs. This could become relevant to deploy at CERN specific LLMs (e.g., chatbot) minimizing resources needed for inference
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Alessandro Di Girolamo (CERN), Dr Maria Arsuaga Rios (CERN), Panos Paparrigopoulos (CERN)16/09/2025, 17:356. Large Language Models-based assistants
Abstract
This proposal outlines a plan to relaunch the Operational Intelligence (OpInt) initiative, leveraging recent advances in Generative AI and AI Agent (AIA) technology to address the escalating complexity of distributed computing operations at CERN. While previous OpInt efforts demonstrated the value of data-driven insights, the landscape has now fundamentally shifted. AIAs offer a...
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Felice Pantaleo (CERN)16/09/2025, 17:408. Training and Education
The STEAM Academy is the educational arm of the “Next-Generation Triggers” project. Between 2026 and 2028 it will deliver a rolling sequence of short, intensive courses that mix morning seminars and lectures with afternoon laboratories. The programme is organised around three inter-locking themes: Edge Computing for Trigger and DAQ, Modern Software Technologies, and Data Science & Machine...
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Raulian-Ionut Chiorescu, Ricardo Rocha (CERN)16/09/2025, 17:458. Training and Education
With the number of teams working on machine learning at CERN increasing, one of the top requests has been to develop a set of introductory and more advanced courses to the available ML platforms and tools.
This initiative should focus on developing the required content, engaging a large enough number of trainers and establishing a structure where these courses can be generally available for...
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Maurizio Pierini (CERN)16/09/2025, 17:508. Training and Education
Between 2018 and 2023, a series of ML-related tutorials was organised as part of the mPP project. This included training on hls4ml, TensorFlow, Pytorch, Neuromorphic computing, Quantum Machine Learning, etc. The average attendance exceeded 100 people per event. We propose to restart this effort, though a funding program that would provide resources to invite speakers, provide access to cloud...
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Dolores Garcia (CERN)16/09/2025, 17:558. Training and Education
We propose an interdisciplinary applied ML sprint for CERN. During this eight week sprint, four person teams, Master and PhD, (2 domain experts + 2 ML specialist) work on a tightly defined CERN identified problem guided by two supervisors (one CERN domain expert and an ML external supervisor). The set of projects should be well defined, and should serve as a incubator for early-stage...
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Andreas Salzburger (CERN), Noemi Calace (CERN)18/09/2025, 10:00
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Lorenzo Moneta (CERN), Maurizio Pierini (CERN), Dr Sofia Vallecorsa (CERN)19/09/2025, 09:00
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Maurizio Pierini (CERN)19/09/2025, 09:10
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Andreas Salzburger (CERN)19/09/2025, 09:25
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Anna Zaborowska (CERN)19/09/2025, 09:40
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Jacob Friedrich Finkenrath (CERN)19/09/2025, 09:55
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Sioni Paris Summers (CERN)19/09/2025, 10:30
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Maurizio Pierini (CERN)19/09/2025, 10:50
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Felice Pantaleo (CERN)19/09/2025, 11:05
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Maurizio Pierini (CERN)19/09/2025, 11:15
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19/09/2025, 11:30
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Dr Sofia Vallecorsa (CERN)19/09/2025, 11:55
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Ricardo Rocha (CERN)19/09/2025, 12:05
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19/09/2025, 12:20
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Dr Maria Arsuaga Rios (CERN)6. Large Language Models-based assistants
This project explores the adoption of AI-powered code assistants such as GitLab Duo, GitHub Copilot, and JetBrains AI (CLion) across CERN’s full portfolio of storage open-source software. The evaluation will examine how these tools can support developers in writing, debugging, and validating code more efficiently while respecting the workflows and standards of our open-source projects. By...
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Dr Maria Arsuaga Rios (CERN)3. AI for metadata analysis
This project focuses on strengthening EOS diagnostics by introducing AI-based anomaly detection into the error log analysis pipeline. We begin by evaluating the Random Cut Forest (RCF) algorithm available in OpenSearch, using logs from the EOS pre-production environment to measure its effectiveness at identifying unusual patterns. The study will benchmark RCF against alternative approaches,...
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Prof. Luis M Fraile (CERN)1. Cutting Edge AI for Offline Data Processing
Artificial Intelligence (AI) and Machine Learning (ML) are transforming key aspects of nuclear physics experimentation. These technologies enable the efficient and precise processing of large volumes of data. Quantum Computing (QC) also holds promise for significant future advancements, particularly through Quantum Machine Learning (QML) applied to complex datasets [Boehnlein2022]. Only a few...
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