Conveners
Artificial Intelligence, Machine Learning and Quantum Computing in HEP
- Vinicius Massami Mikuni
Artificial Intelligence, Machine Learning and Quantum Computing in HEP
- Vinicius Massami Mikuni
Artificial Intelligence, Machine Learning and Quantum Computing in HEP
- Manuel Szewc (UNSAM)
Artificial Intelligence, Machine Learning and Quantum Computing in HEP
- Manuel Szewc (UNSAM)
Artificial Intelligence, Machine Learning and Quantum Computing in HEP
- Ying-Ying Li (IHEP)
Artificial Intelligence, Machine Learning and Quantum Computing in HEP
- Ying-Ying Li (IHEP)
Artificial Intelligence, Machine Learning and Quantum Computing in HEP
- Ying-Ying Li (IHEP)
Artificial Intelligence, Machine Learning and Quantum Computing in HEP
- There are no conveners in this block
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Ms Valentina Schรผtze (University of Cambridge)7/30/26, 8:30โฏAMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
We present Colibri, an open-source Python framework for parton distribution function (PDF) determination, designed to provide a flexible and efficient environment for both frequentist and Bayesian inference. Colibri allows users to implement custom PDF models while leveraging built-in tools for fast observable computation, access to experimental data, and uncertainty propagation via Hessian,...
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Santiago Andres Tanco7/30/26, 8:45โฏAM1Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Collider experiments produce vast, high-dimensional datasets where rare signals are buried beneath large and complex backgrounds. To extract reliable conclusions from all available data, it is essential to leverage prior knowledge across domains and to cleverly model the connection between latent variables and observables. In this talk, I will present a set of tools and techniques to build...
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Anna Polova (UNESP - Universidade Estadual Paulista (BR))7/30/26, 9:00โฏAM1Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Machine Learning has become a key component of high-energy physics, particularly for real-time data processing in trigger systems and in view of the forthcoming HL-LHC upgrade. Such environments impose stringent constraints, requiring event processing under tight latency and memory requirements, thereby motivating the development of highly efficient inference solutions.
The ML4EP team at...
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Ho-Fung Tsoi (University of Pennsylvania)7/30/26, 9:15โฏAMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
In HEP data analyses, finding an adequate function to model binned data has largely relied on a manual process: guess a functional form by intuition, fit, examine, then repeat until successful. We show that this iterative process can be automated by a machine using symbolic regression, which performs a data-driven search over function space without requiring prior knowledge of what an adequate...
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Mohammed Aboelela (Dallas SMU)7/30/26, 9:30โฏAM1Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Multiple proton-proton interactions occur in the same bunch crossing at the LHC (โpile-upโ), with the mean number of interactions per bunch crossing reaching up to about 60 in Run 3 and up to about 200 at the High-Luminosity LHC. As a direct consequence, hadronic activity within each bunch crossing increases, making it more difficult to identify collisions of interest pertaining to multi-jet...
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Prof. Ying-Ying Li (Institute of High Energy Physics, CAS)7/30/26, 9:45โฏAMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Spin correlations provide a powerful probe of the Standard Model (SM), physics beyond the SM (BSM), and the quantum nature of high-energy interactions. While such effects have been extensively studied for heavy particles, accessing spin correlations of light partons that hadronize into jets remains a longstanding challenge. In this work, we study parton spin correlations in hadronic Higgs...
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Matteo Robbiati7/30/26, 10:45โฏAMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
We present the latest advancements to the Qibo ecosystem, a full-stack platform for quantum algorithm development, simulation, and hardware execution. Recent developments focused on deeply integrating Quantum Machine Learning (QML) capabilities and real-time hardware control to accel erate research, prototyping, and deployment of quantum applications.
The dedicated Qiboml module introduces...
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Matteo Argenton (Universita e INFN, Ferrara (IT))7/30/26, 11:00โฏAM1Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Identity management (IM) for multi-object tracking is the problem of evolving a belief state over trackโobject associations, accounting for mixing events and measurement uncertainties.
In the most general setting the problem state is described by a probability distribution over the $n!$ permutations of $n$ objects, whose exact representation and update are inefficient in classical...
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Jogi Suda Neto (University of Alabama (US)), Dr Sofia Vallecorsa (CERN)7/30/26, 11:15โฏAMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The underlying likelihood of a given event originating from a partonic-level process is known to be approximately invariant under the Lorentz group. We find that quantum neural networks equivariant under such continuous symmetries exhibit improved generalization, sample and training time complexity. We show that this property is induced by the number of distinct group orbits in the data, with...
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Konstantinos Pyretzidis (IFIC ( UV-CSIC ))7/30/26, 2:30โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Perturbative Quantum Field Theory is central for performing precise predictions of observables at high-energy colliders. Fundamental concepts in this framework, such as Loop Feynman diagrams and the phase-space, require evaluating multidimensional integrals. The standard approach relies on adaptive importance sampling, notably the VEGAS algorithm, which updates a multidimensional grid. Because...
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Santiago Andres Tanco7/30/26, 2:45โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Monte Carlo simulations provide powerful approximations to experimental data when accurate modeling and parameter tuning are available. However, it is unrealistic to expect that a single parameterization can reproduce the full complexity of the data. A more plausible assumption is that different simulation setups describe different regions of observable space with varying degrees of...
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Yulei Zhang (University of Washington (US))7/30/26, 3:00โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
While deep learning is transforming data analysis in high-energy physics, computational challenges limit its potential. We address these challenges in the context of collider physics by introducing EveNet, an event-level foundation model pretrained on 500 million simulated collision events using a hybrid objective of self-supervised learning and physics-informed supervision. By leveraging a...
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Antonin VACHERET (LPC Caen)7/30/26, 3:15โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The search for new physics at the Large Hadron Collider (LHC) increasingly depends on the ability to extract tiny signals from petabytes of data. Machine learning (ML) methods have become essential tools for identifying jets and the particles that initiated them, as well as for separating rare physics processes from background events.
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In recent years, self-supervised learning (SSL) has been... -
Brendon Bullard (SLAC National Accelerator Laboratory (US))7/30/26, 3:30โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Jet tagging, identifying the origin of jets produced in particle collisions, is a critical classification task in high-energy physics. Despite the revolutionary impact of deep learning on jet tagging over the past decade, the paradigm has remained unchanged. In particular, jets are classified independently, one at a time. This single-jet approach ignores correlations, overlaps, and wider event...
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Jhoรฃo Gabriel Martins Campos de Almeida Arneiro (Universidade de Sรฃo Paulo (USP) (BR))7/30/26, 3:45โฏPM2Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Jets are important structures observed in high-energy physics for its wide range of uses, including investigations of electro-weak interactions and Beyond the Standard Model physics, among several other applicabilities. Likewise, in recent years the use of neural networks and other machine learning and artifficial intelligence (AI) methods in high-energy physics has rapidly expanded because of...
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Anna Polova (UNESP - Universidade Estadual Paulista (BR))7/30/26, 4:00โฏPM2Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Machine learning models used in real-time and resource-constrained environments, such as hardware triggers, online reconstruction pipelines, and FPGA/GPU inference systems, must satisfy strict latency, memory, and numerical precision requirements. Achieving these targets typically requires extensive tuning of training schedules, quantization settings, sparsity levels, and architectural...
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341. Validating Physical Consistency in Deep Learning for Higgs Boson Discovery using Explainable AILรญdia Gabrielly Dutra de Meneses Santos7/30/26, 4:45โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
While Deep Learning (DL) offers superior performance in event classification, its "black box" opacity hinders widespread adoption in experimental High Energy Physics. This study presents a methodological validation of DL models using Explainable AI (XAI) on the benchmark UCI HIGGS dataset. We benchmark a Deep Neural Network (DNN) against a gradient-boosted baseline (XGBoost), achieving...
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Mr Luis Navarro (Federico Santa Maria Technical University (CL))7/30/26, 5:00โฏPM2Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
We present a multi-task deep learning framework for the reconstruction of extensive air showers (EAS) in the context of the CONDOR Observatory, a proposed high-altitude gamma-ray experiment in northern Chile. The method addresses three key reconstruction tasks: zenith-angle estimation, gammaโhadron shower classification, and primary energy prediction.
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The model is based on a **hybrid... -
Rodrigo Congio7/30/26, 5:15โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The next generation of neutrino experiments requires high-precision data acquisition systems capable of processing massive data volumes with minimal latency. This work presents the development of the CAIPORA (Compact Artificial Intelligence Placed On Reprogrammable Array), an intelligent trigger system implemented on Field-Programmable Gate Arrays (FPGAs) for the real-time identification of...
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Dr Jianlong Lu (National University of Singapore)7/30/26, 5:30โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Collective neutrino flavor oscillations driven by neutrinoโneutrino interactions are a quantum many-body dynamics problem amenable to digital quantum simulation, but near-term superconducting hardware is constrained by circuit depth, two-qubit errors, and topology-dependent routing overhead. Recent three-flavor circuit constructions (including qubit and qutrit encodings) demonstrate...
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Sara Mirthis Dantas dos Santos (Universidade Estadual de Campinas (UNICAMP))7/30/26, 5:45โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The COherent NeutrinoโNucleus Interaction Experiment (CONNIE) operates 30 meters from the Angra 2 reactor at the Almirante รlvaro Alberto Nuclear Power Plant in Brazil. CONNIE is the first reactor neutrino experiment to employ silicon Skipper-CCDs with the aim of detecting coherent elastic neutrino-nucleus scattering (CEvNS), as well as searching for new physics, and enabling real-time reactor...
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Dr Saul Alonso Monsalve (ETH Zurich)7/30/26, 6:00โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Precise reconstruction of high-energy neutrino interactions at the LHC is critical for the physics program of the proposed FASERCal detector, an off-axis neutrino detector for the FASER experiment during LHC Run 4, enabling precision measurements of TeV-scale neutrino interactions in the far-forward region. The detector's highly granular, 3D voxelized geometry produces sparse data that...
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Margita Kubรกtovรก (Institute of Physics)7/30/26, 6:15โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Ultra-high-energy cosmic rays are particles of extraterrestrial origin with energies exceeding $10^{18}$ eV, which are studied indirectly through the extensive air showers (EASs) they induce in the atmosphere. Determining their energy spectrum and mass composition is crucial for identifying their astrophysical sources. Sensitivity to the primary mass comes from observables related to...
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Katharina Lachner (ETH Zurich (CH)), Dr Saul Alonso Monsalve (ETH Zurich)7/31/26, 8:30โฏAMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
One of the aims of large water Cherenkov neutrino experiments like Hyper-Kamiokande (Hyper-K) experiment is to detect low energetic neutrinos to allow studies in the solar and supernova sector. This requires pattern recognition of very faint signals on top of the inevitable background noise. Such developments have to begin already at the stage of data acquisition, where Hyper-K is targeting...
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Marta Babicz (University of Zurich (CH))7/31/26, 8:45โฏAMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Pulse-shape discrimination (PSD) in point-contact high-purity germanium (HPGe) detectors is a primary handle for background rejection in neutrinoless double-beta decay searches. Standard analyses reduce each waveform to a few engineered scalars (e.g.\ AvsE, delayed-charge recovery (DCR) and late charge (LQ)), potentially discarding information in the full time series. We benchmark end-to-end...
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Beijiang Liu, Changzheng YUAN, Dr Ke Li (Institute of High Energy Physics, Chinese Academy of Sciences (CN)), Zhengde Zhang (ไธญๅฝ็งๅญฆ้ข้ซ่ฝ็ฉ็็ ็ฉถๆ)7/31/26, 9:00โฏAMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
We present Dr.Sai, a novel multi-agent system powered by large language models (LLMs), designed to automate full-chain physics analysis at the BESIII experiment. The system directly interprets a physicist's natural language query (e.g., "Measure the J/ฯ mass spectrum and branching ratio"), autonomously decomposes it into structured subtasks (data skimming, fitting, etc.), and orchestrates the...
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Davide Valsecchi (ETH Zurich (CH))7/31/26, 9:15โฏAMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Precise simulation-to-data corrections, encapsulated in scale factors, are crucial for achieving high precision in physics measurements at the CMS experiment. Traditional methods often rely on binned approaches, which limit the exploitation of available information and require a time-consuming fitting process repeated for each bin. This work presents a novel approach utilizing modern...
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Davide Di Croce (CERN)7/31/26, 9:30โฏAM2Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The High-Luminosity LHC (HL-LHC) will operate with pile-up levels of up to ฮผโ 200, significantly increasing the trigger and reconstruction challenge for the ATLAS experiment due to high detector occupancies and background activity. To meet the stringent computing budget and physics-performance requirements, ATLAS is migrating tracking to the experiment-agnostic A Common Tracking Software...
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Prof. Eduardo Furtado De Simas Filho (UFBA)7/31/26, 9:45โฏAM1Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The increasing instantaneous luminosity and pile-up at the Large Hadron Collider impose stringent constraints on the online reconstruction and selection of electrons and photons in the ATLAS High-Level Trigger (HLT). To address these challenges, machine learning techniques are being integrated at multiple stages of the electromagnetic trigger sequence, from early energy reconstruction to...
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Kaito Sugizaki (Pennsylvania)7/31/26, 10:45โฏAMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Anomaly detection expands on traditional model-focused search programs at colliders via data-driven machine learning (ML) selection algorithms. Deploying anomaly detection at filtering (trigger) level enables the acquisition of fundamentally novel events, probing below historically constraining energy thresholds. However, successful use of such ML algorithms in the trigger requires attention...
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Alex Sopio (The University of Edinburgh (GB))7/31/26, 11:00โฏAMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Hadronic object reconstruction & classification is one of the most promising settings for cutting-edge machine learning and artificial intelligence algorithms at the LHC. In this contribution, highlights of ML/AI applications by ATLAS to QCD and boosted-object identification, MET reconstruction and other tasks will be presented.
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Giuliano Gustavino (Roma I)7/31/26, 11:15โฏAM1Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The High-Luminosity LHC will generate unprecedented data rates, pushing real-time trigger systems to their limits. We present a novel approach deploying graph neural networks (GNNs) on FPGAs to achieve fast, sub-microsecond inference for Level-0 muon triggers. Exploiting the sparse, relational structure of detector hits, the method preserves key spatial correlations while enabling...
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Pedro Da Costa Huot7/31/26, 11:30โฏAM1Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
We present a study on the application and comparison of different machine learning algorithms for the identification of hypernuclei from simulated heavy-ion collisions, particularly those with mass number A = 3 to A = 5. The study focuses on three supervised learning algorithms - Boosted Decision Trees, Support Vector Machines and Artificial Neural Networks - which were trained to distinguish...
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Dr Guang Zhao (Institute of High Energy Physics (CAS))7/31/26, 11:45โฏAM2Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Particle identification (PID) is essential for future particle physics experiments such as the Circular Electron-Positron Collider (CEPC) and the Future Circular Collider. A high-granularity Time Projection Chamber (TPC) not only provides precise tracking but also enables dN/dx measurements for PID. The dN/dx method estimates the number of primary ionization electrons, offering significant...
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Christian Sonnabend (CERN, Heidelberg University (DE))7/31/26, 12:00โฏPM1Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The ALICE TPC is the main tracking and PID detector used in the ALICE experiment at CERN. The online reconstruction is capable of handling dense tracking environments at data rates of 900 GB/s, with a GPU-based infrastructure, ideally suited for parallelizable machine learning applications.
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The work to be presented concerns cluster finding, with the first-ever application of neural networks... -
Felipe Luan Souza De Almeida (University of Barcelona (ES))7/31/26, 12:15โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Searches for Charge-Parity (CP) violation in multibody decays provide a powerful probe of physics beyond the Standard Model, particularly in scenarios where interference effects and other final-state interactions can potentially generate localised asymmetries across the decay phase space, which may be significantly larger than the corresponding phase-spaceโintegrated effects. In decays with...
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Tommaso Fulghesu (Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France)7/31/26, 2:30โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
During Run 3, the LHCb experiment operates at an instantaneous luminosity approximately five times higher than in Run 2, leading to a substantial increase in event multiplicity and data volume. Each protonโproton collision produces hundreds of tracks, while only a small fraction corresponds to the signal. To reduce event size without compromising physics performance, LHCb has developed...
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Peilian Li (University of Chinese Academy of Sciences)7/31/26, 2:45โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The precise determination of the initial flavour of neutral B mesons is crucial for time-dependent measurements of CP violation and mixing parameters, where it directly constrains the ultimate physics sensitivity. Leveraging the unprecedented data set of Run 3 and capitalizing on modern algorithmic advances, the LHCb collaboration has undertaken a comprehensive redesign of its flavour tagging...
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Wren Vetens (Syracuse University (US))7/31/26, 3:00โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The High-Luminosity upgrade of the LHC will increase the collision rate by a factor of five, leading to extremely dense environments with a large number of overlapping protonโproton interactions. In this context, the LHCb Upgrade II and its next-generation electromagnetic calorimeter, PicoCal, face major challenges in the accurate reconstruction of photons, electrons, and neutral pions, which...
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Elena Graverini (University of Pisa (IT) and EPFL - Ecole Polytechnique Federale Lausanne (CH))7/31/26, 3:15โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The increasing luminosity at the Large Hadron Collider challenges event reconstruction and data selection at LHCb due to rising particle multiplicities, enhanced combinatorial backgrounds, and stringent constraints on trigger latency and data storage. To address these limitations, the deep-learningโbased Full Event Interpretation (DFEI) framework is being developed at LHCb, aiming at the...
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Samuel Morand (EPFL - Ecole Polytechnique Federale Lausanne (CH))7/31/26, 3:30โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The large samples of Z0 bosons expected at the potential Future Circular Collider (FCC-ee) will provide an unprecedented environment for precision flavour physics. In this context, the ability to reconstruct heavy-hadron decay chains with high efficiency and purity is essential to fully exploit the physics potential of the experiment, enabling improved measurements of rare decays, CP...
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DAVID FRANCISCO RENTERIA ESTRADA (IFIC UV-CSIC)7/31/26, 3:45โฏPM1Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
We present the first quantum computation of a total decay rate in high-energy physics at second order in perturbative quantum field theory. This work underscores the confluence of two recent cutting-edge advances. On the one hand, the quantum integration algorithm quantum Fourier iterative amplitude estimation, which efficiently decomposes the target function into its Fourier series through a...
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Santosh Parajuli (Univ. Illinois at Urbana Champaign (US))7/31/26, 4:00โฏPMArtificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
Graph Neural Networks (GNNs) are increasingly used for particle tracking in High Energy Physics (HEP), as they provide a natural framework for modeling the relational structure of detector hits. In parallel, recent developments in quantum computing have motivated the exploration of quantum machine learning techniques, which may offer enhanced expressive power through quantum superposition and...
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Christine Angela McLean (The State University of New York SUNY (US))8/1/26, 5:30โฏPM4Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
The particle-flow (PF) algorithm aims to provide a global event description for each collision in terms of the comprehensive list of final-state particles. It is of central importance to event reconstruction in CMS, and has been a focus of developments in light of planned high-luminosity running conditions with increased pileup and detector granularity. Existing implementations rely on...
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Manuel Szewc8/1/26, 5:45โฏPM2Artificial Intelligence, Machine Learning and Quantum Computing in HEPTalk
A fundamental part of event generation, hadronization is currently simulated with the help of fine-tuned empirical models. Motivated by the difficulties of these models, in this talk I'll present the efforts of the MLHAD collaboration in improving several aspects of hadronization modelling: observable selection via correlation modelling, parameter tuning and uncertainty quantification via...
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