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14/09/2026, 08:30
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Nicole Michelle Hartman (TUM (DE))14/09/2026, 09:30
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Ben Assi, Ben Assi, Benoit Assi14/09/2026, 10:00
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Ulrich Willemsen (Rheinisch Westfaelische Tech. Hoch. (DE))14/09/2026, 11:00
b-hive is a general-purpose machine learning framework developed for the CMS experiment. Every stage of the workflow, from dataset construction and training to inference and evaluation, is encapsulated in a self-contained Law task, while physics-specific choices such as architecture, input features, truth definitions, and kinematic selections are injected through YAML configuration files and...
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Siarhei Shulha (Joint Institute for Nuclear Research (RU))14/09/2026, 11:00
The measurement of quark- and gluon-jet properties requires the determination of their fractions in experimental jet samples. This is commonly achieved by fitting data with quark- and gluon-jet templates derived from Monte Carlo simulations. Because the template shapes depend on the underlying model, the extracted jet fractions are intrinsically model-dependent. This talk reviews the main...
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Suprio Dubey (Heidelberg University)14/09/2026, 11:20
Neural network surrogates for LHC scattering amplitudes require trustworthy uncertainty estimates, a challenging task given the non-Gaussian systematics. We target it
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using conformal prediction, a distribution-free post-processing to complement trained
surrogates with calibrated uncertainties. We find that standard conformal predictions
struggle to provide locally calibrated uncertainties.... -
Jawaher Altork (Universita e INFN, Firenze (IT))14/09/2026, 11:20
The CMS experiment at CERN relies on Data Quality Monitoring (DQM) and data certification to ensure that only high-quality data are used for physics analyses. For the JetMET subsystem, this process is traditionally based on the manual inspection of a large number of DQM histograms by detector experts, making it a time-consuming task that can make subtle detector or reconstruction issues...
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Cecilia Antonioli (INFN & Universita' di Padova)14/09/2026, 11:40
The Time Of Propagation (TOP) detector at the Belle II experiment is a ring-imaging Cherenkov detector designed to identify charged hadrons in electron-positron collisions at the SuperKEKB accelerator. It consists of 16 quartz radiator modules arranged around the barrel region of the Belle II detector. When a charged particle crosses a module, Cherenkov photons are emitted. A fraction of these...
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Lorenz Vogel (Institute for Theoretical Physics, Heidelberg University)14/09/2026, 11:40
Calibrated learned uncertainties are a key requirement also for generative neural networks in LHC physics. For a toy model with an explicit likelihood we show how a heteroscedastic and a Bayesian normalizing flow learn the systematic and statistical uncertainties on the underlying phase space density. Without an explicit likelihood we train the heteroscedastic loss on a classifier-reweighted...
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SERGIO RODRIGUEZ BENITEZ (Instituto de Física Teórica IFT-UAM/CSIC)14/09/2026, 13:30
Searches for new particles often span a wide mass range, where both signal and SM background shapes vary significantly. We introduce a multivariate method that fully exploits the correlation between signal and background features and the explored mass scale. The classifiers—either a neural network or boosted decision tree—produce continuous outputs across the full mass range, achieving...
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Alexander Gavin (University of London (GB))14/09/2026, 13:30
The identification of jets containing b-hadrons is essential for many physics analyses at the LHC, including precision measurements of Higgs boson and top-quark processes, as well as searches for physics beyond the Standard Model. We present recent improvements in the discrimination of b-jets from jets originating from lighter quarks using the ATLAS detector. These advances are driven by...
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Mane Papoyan (American University of Armenia (AUA))14/09/2026, 13:50
The H → ZZ → 4ℓ channel remains one of the cleanest probes of Higgs boson properties at the LHC, owing to its fully reconstructable final state and well-understood background composition. This work addresses the problem of identifying an optimal machine-learning classifier for extracting the H → ZZ → 4ℓ signal from the ATLAS Open Data 2025 release (√s = 13 TeV, 36.6 fb⁻¹), comparing seven...
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Jhoão Gabriel Martins Campos de Almeida Arneiro (Universidade de São Paulo (USP))14/09/2026, 13:50
This study explores a convolutional neural network (CNN) approach to classify events produced in high-energy collisions by the presence of heavy (charm and bottom), light (up, down, strange) and gluon jets, with the main characteristic being that jets are not reconstructed in our approach. The method constructs image-like representations based on the kinematics of charged decay products using...
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Jae Jin Hong (Indiana University (US))14/09/2026, 14:10
Discriminating highly boosted jets from the decays of heavy scalar particles that do not involve b-quarks from background is a very challenging problem. We describe a novel "4-Prong Tagger" that uses a graph neural network based on the Lund Jet Planes of a large radius jet to distinguish high energy scalar particle decays, S->WW->4q, from backgrounds such as QCD, hadronic vector-boson decays,...
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Mr Miguel Angel Avendano Bernal (University of Southampton)14/09/2026, 14:10
In the study of DM detection at colliders, novel candidates have emerged to bridge between experimental data and theoretical models. Dark Showers (DS) are being studied as an extension of the Standard Model (SM), containing both invisible and visible particles that allow us to predict scenarios involving collider observables. Among these, Semi-Visible Jets (SVJs), represent a novel promising...
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Dr Adil Jueid (Korea Institute for Advanced Study)14/09/2026, 14:30
The observation of flavor-changing neutral current (FCNC) interactions between the top quark and the Standard Model (SM) Higgs boson would constitute an unambiguous signal of physics beyond the SM. Searches for this process at the LHC are, however, extremely challenging due to the small signal rates and the strong kinematic resemblance between the signal and dominant SM backgrounds,...
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Yipin Wang (Peking University (CN))14/09/2026, 14:30
A calibratable full-event (jet-free) HH→4b framework based on full-event particle-flow (PF) candidates was presented at ML4Jets 2025 and in Refs. [[1]][1] and [[2]][2], demonstrating a dramatic >5× improvement in search sensitivity over conventional approaches. In this talk, I will present a series of controlled ablation studies that progressively enhance the event representation, providing a...
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Feng-Yang Hsieh (National Taiwan University)14/09/2026, 14:50
Using two benchmark models containing extended scalar sectors beyond the Standard Model, we investigate deep learning techniques to enhance the sensitivity of resonant triple Higgs boson ($HHH$) searches in the fully hadronic $6b$ channel, which suffers from severe combinatorial background and jet-pairing ambiguities. Specifically, we employ the Symmetry Preserving Attention Network (SPA-Net),...
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Gregorio de la Fuente Simarro (Massachusetts Institute of Technology)14/09/2026, 14:50
Providing a practical and hadron-level definition of multiple jet flavors has been a long-standing challenge in collider physics. Previous work has introduced a data-driven, operational definition of quark and gluon jets, but no robust generalization beyond two jet categories presently exists. To address this, we introduce a machine-learning framework called "simplex demixing'' to extract $T$...
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Mr Linrui Chen (Peking University), Qiang Li (Peking University (CN)), Mr Zixun Kou (Peking University)14/09/2026, 15:10
We investigate a novel class of boosted-object signatures at the LHC, where a high-pT fat-jet contains an identifiable hadron or quarkonium state originating from rare or semi-exclusive decays. Unlike conventional boosted jet studies, which focus on multi-prong partonic substructure, our approach probes hybrid configurations such as W±→π±γ, where a localized hadronic or quarkonium signal is...
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Lena Nowatzki14/09/2026, 15:10
Precise measurements of $B$ meson branching fractions are essential both for testing Standard Model predictions and for many measurements that rely on accurate modeling of data composition in flavor-physics analyses. At Belle II, $B$ mesons are produced via the process $e^+e^- \to \Upsilon(4S) \to B\bar{B}$. The total number of such events can be determined with high precision, yet using this...
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Judita Mamuzic (IFAE - Barcelona)14/09/2026, 16:00
Beyond the Standard Model (BSM) searches show no statistically significant sign of new physics to date. However, several analyses reported small excesses, higher than 2σ SD beyond the SM expectation. In this work, clustering algorithms are used to extract more insight from existing searches and motivate a next round of BSM analyses. The flexible framework of the phenomenological Minimal...
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Sitian Qian (NU FNAL)14/09/2026, 16:00
The release of LEP open data provides a clean test bed for developing machine-learning methods relevant to future lepton colliders. We present an effort to identify individual b hadrons in DELPHI $Z\to b\bar{b}$ events using an approach inspired by panoptic segmentation. Rather than assigning a single flavour label to a jet, the model classifies reconstructed particles and associates them with...
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Shang-Fu Wei (The University of Tokyo)14/09/2026, 16:20
Multivariate classifiers in collider physics increasingly use low-level detector information to maximize sensitivity, but this often amplifies systematic uncertainties arising from mismodelling in simulation. To address this, we apply unsupervised domain adaptation (UDA), allowing the classifier to learn with both simulated and unlabeled real data during training, thereby reducing sensitivity...
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Humberto Reyes-Gonzalez (RWTH Aachen University)14/09/2026, 16:20
Open statistical models released by the LHC experiments are transforming the reinterpretation of collider searches by providing access to the full likelihood information of experimental analyses. However, evaluating these likelihoods remains computationally expensive, particularly for analyses with many signal and control regions, limiting their use in large-scale phenomenological studies. In...
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Robert Les (Michigan State University (US))14/09/2026, 16:40
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, recent highlights of ML applications by ATLAS for boosted-object identification will be presented. This covers results of constituent-based transformers for quark-gluon, top-quark, and W boson...
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Nitish Kumar Kasaraguppe Veerappa Gowda (Rheinisch Westfaelische Tech. Hoch. (DE))14/09/2026, 16:40
Categorizing events using discriminant observables is central to many high-energy physics analyses. Yet, bin boundaries are often chosen manually. A simple, popular choice in multi-classification tasks is to assign events according to the largest per-class score (”argmax”) and to apply equidistant binning to the resulting one-dimensional discriminants. We propose a binning optimization for...
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Umar Sohail Qureshi (Vanderbilt University)14/09/2026, 17:00
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 within the same collision event are classified independently, one at a time. This single-jet approach ignores...
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Radha Mastandrea14/09/2026, 17:00
Fixed-order perturbative calculations for differential cross sections can suffer from non-physical artifacts: they can be non-positive, non-normalizable, and non-finite, none of which occur in experimental measurements. We propose a framework, the Resummed Distribution Function (RDF), that, given a perturbative calculation for an observable to some finite order in $\alpha_S$, will ``resum''...
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Mr Osman Bayraktar (Istanbul University (TR))14/09/2026, 17:20
Particle Transformer ( ParT) has achieved state of the art performance in jet tagging by modeling particle level information through attention. In this work, we introduce Mod-ParT, a physics aware extension of ParT that incorporates relational information into particle representations, enriches pairwise attention biases with additional physics motivated features and replaces the class token...
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Barry Dillon (b.dillon@ulster.ac.uk)14/09/2026, 17:20
We present a theory-informed reinforcement-learning framework that recasts the combinatorial assignment of final-state particles in hadron collider events as a Markov decision process. A transformer-based deep Q-network, rewarded at each step by the logarithmic change in the magnitude of the tree-level matrix element, learns to map final-state particles to partons. Because the reward derives...
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Johann Michael Ioannou-Nikolaides (University of Copenhagen (DK))14/09/2026, 17:40
In many classification problems, reliable instance-level labels are unavailable. However, it is often possible to construct weakly enriched unlabeled samples: datasets selected by different cuts, sources, populations, or experimental conditions that change latent class proportions without revealing them. Classification without Labels (CWoLa) shows that, in the binary case ($K=2$), a classifier...
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João A. Gonçalves (University of Bonn, B-IT, Lamarr Institute)14/09/2026, 17:40
Foundation models for collider physics have so far been trained predominantly on simulated events or proton–proton collision data, leaving their behaviour in the high-occupancy heavy-ion regime largely unexplored. We present, to our knowledge, the first particle-level foundation model pretrained directly on experimentally recorded heavy-ion collisions released through the CERN Open Data...
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14/09/2026, 18:00
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15/09/2026, 08:30
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Theo Heimel (UCLouvain)15/09/2026, 09:00
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Michał Mazurek (National Centre for Nuclear Research (PL))15/09/2026, 09:30
Monte Carlo simulations are essential for physics analyses and detector design in High Energy Physics (HEP). As the computational requirements of traditional simulation rapidly outpace available computing budgets, leveraging Generative AI for fast simulation has become vital to producing the required volume of simulated samples.
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Nevertheless, transitioning from initial, toy models to... -
Jay Ajitbhai Sandesara15/09/2026, 10:00
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Xiaoying Lu15/09/2026, 11:00
Large-volume liquid scintillator detectors produce variable-size sets of PMT(photomultiplier tube) hits with rich timing, charge, and spatial correlations, making track reconstruction a natural application for attention-based architectures. This talk presents Amber (Attention Mechanism Based Event Reconstructor), a self-attention-based framework for cosmic-muon track reconstruction in the...
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Rebecca Revelli (Heidelberg University)15/09/2026, 11:00
Amplitude surrogates speed up one of the most computationally expensive steps in the LHC simulation chain. We adapt the generative amplification framework to amplitude surrogates evaluated by reweighting and apply it to uncertainty-aware surrogates. We show that amplitude surrogates, like generative event generators, can exhibit generative amplification when trained on a limited set of...
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Sascha Cassandra Diefenbacher (Heidelberg University (DE))15/09/2026, 11:20
Generative networks are perfect tools to enhance the speed and precision of LHC simulations. It is important to understand their statistical precision, especially when generating events beyond the size of the training dataset. We present two complementary methods to estimate the amplification factor without large holdout datasets. Averaging amplification uses Bayesian networks or ensembling to...
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Christina Karagianni (University and INFN Ferrara (IT))15/09/2026, 11:20
Event reconstruction in liquid argon time projection chambers (LArTPCs) requires separating detector activity from overlapping beam particles and cosmic rays into physically meaningful particle hierarchies. In the Pandora event reconstruction chain, event slicing groups particle-flow particles (PFPs) into candidate hierarchies using topological association criteria. We investigate whether this...
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Sophia Vent (Institute for theoretical physics Heidelberg)15/09/2026, 11:40
We employ neural control variates to minimize the range of event weights and avoid negative weights for phase-space integration and event generation. A signed control variate, built from two normalizing flows, fulfills both tasks. Combined with neural importance sampling, it significantly reduces the computational cost of LO and NLO predictions. For the NLO case, our conditional neural control...
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Nathan Suri Jr. (Yale University (US))15/09/2026, 11:40
The current run of the Large Hadron Collider (LHC) yields on average 30-50 simultaneous pileup vertices per event, consisting of both charged and neutral showers. This number is expected to only increase at the High Luminosity LHC with predicted averages on the order of 140 pileup vertices. Pileup presents a salient problem that, if not checked, hinders the search for new physics and Standard...
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Daohan Wang (HEPHY ÖAW)15/09/2026, 13:30
Producing very large unweighted event samples for high-multiplicity processes is limited by expensive matrix-element evaluations and low unweighting efficiencies. We present the first end-to-end GPU-resident event-generation workflow that integrates normalizing-flow proposals with the parton-level event generator Pepper. Helicity-conditioned coupling flows are trained using online updates...
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Asu Guvenli (University of Hamburg (DE))15/09/2026, 13:30
Long-lived particles decaying within the CMS muon system can deposit dense showers of hits in the endcap Cathode Strip Chambers (CSCs), called muon detector showers (MDS), whose hit multiplicity is the primary handle for their identification. Standard CSC reconstruction, built for isolated muon tracks, breaks down here: overlapping detector signals inflate and smear the hit count where the...
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Margaret Rose Lazarovits (The University of Kansas (US))15/09/2026, 13:50
Jet reconstruction is an active and open area of particle physics research, with challenges and questions related to jet size, multiplicity, substructure, and experimental performance in the presence of pileup and noise. A new algorithm, Probabilistic, Structure-Intrinsic Clustering with an Hierarchical Embedding (PSICHE, arxiv:2608.xxxx), introduces a variety novel features to the domains of...
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Konrad Helms (Georg August University of Göttingen)15/09/2026, 13:50
Accurate sampling of multi-particle phase spaces is a major bottleneck in
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high-energy-physics simulations at the LHC, especially for processes with
many final-state particles where matrix-element evaluations become
prohibitively expensive. We introduce a stacked training strategy for
phase-space point generators that cuts the training cost dramatically while
delivering improved... -
Giovanni De Crescenzo15/09/2026, 14:10
We combine fast amplitude surrogates with neural importance sampling to accelerate NLO calculations. For virtual corrections, a learned ratio to the Born matrix element with calibrated uncertainties guarantees reliable precision across phase space. For real emission, we stick to the standard FKS subtraction and train sector-conditioned surrogates of the regularized integrands away from...
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Gabriel Matos (Columbia University (US))15/09/2026, 14:10
Electromagnetic calorimeters provide essential information for reconstructing and selecting both Standard Model (SM) and potential beyond the SM physics events at high-energy particle colliders. The fine-grained segmentation of modern calorimeters captures rich information about the internal structure of particle showers, much of which is discarded by conventional high-level reconstruction...
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Timo Janßen (University of Göttingen)15/09/2026, 14:30
Next-to-next-to-leading-order QCD calculations are essential for precision collider physics, but their computational cost is often dominated by inefficient phase-space integration. In this talk, I will present the application of neural importance sampling to all contributions entering an NNLO QCD calculation of gluonic top-quark pair production within the STRIPPER subtraction framework. The...
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Yuliia Maidannyk (IRFU-CEA, Université Paris-Saclay (FR))15/09/2026, 14:30
The reconstruction of electrons and photons in the CMS Electromagnetic Calorimeter (ECAL) currently relies on a geometrical clustering algorithm called PFClustering. While it is efficient for isolated particles, it has a limited ability to resolve close-by showers and mitigate detector noise, which reduces the sensitivity of physics analyses and will worsen with detector ageing. We present...
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Agni Purani (Rutgers University)15/09/2026, 14:50
We present a new way of measuring the performance of generative models and use it to rerank all CaloChallenge submissions. This method reorders some submissions and provides a new perspective on how the generative models differ from the reference samples.
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Alexander Pesch Berrocal (Rheinisch Westfaelische Tech. Hoch. (DE))15/09/2026, 14:50
Accurate knowledge of tracker material is essential for particle reconstruction and detector simulation in high energy physics. Photon conversions and secondary nuclear interactions provide localized probes of detector structures, but inferring a material map from their sparse and noisy vertex distributions is an ill-posed inverse problem. We present a Bayesian field-inference approach in...
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Mr Syed Haider Ali (Institute of Physics, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032 Debrecen, Hungary)15/09/2026, 15:10
Charged-particle track reconstruction is a central challenge for the High-Luminosity Large Hadron Collider (HL-LHC), where high detector occupancy and the strongly curved trajectories of low-$p_T$ particles create severe combinatorial ambiguities. Although Graph Neural Networks (GNNs) have emerged as a promising approach for graph-based tracking, their performance is often limited by the...
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Mr Jiacheng Wu (Shanghai Jiao Tong University)15/09/2026, 15:10
Optical-photon tracking is a major computational bottleneck in segmented plastic-scintillator detectors, where a single particle crossing can produce tens of thousands of photons. We present FastSimu, a conditional generative surrogate for the response of a 25-mm scintillator voxel at the first entry of photons into three orthogonal wavelength-shifting fibers. Conditioned on a multidimensional...
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Matthew Green (Adelaide University (AU))15/09/2026, 16:00
The ATLAS calorimeter system measures the energy of particles. These are large volumes segmented into many individual cells, each recording a small deposit of energy as a particle passes through. The cell-by-cell structure has a natural geometric and topological form well suited to graph neural networks (GNNs), a class of machine learning models designed to learn from data with irregular,...
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Prabhat Solanki (Universita & INFN Pisa (IT))15/09/2026, 16:00
FlashSim is an end-to-end machine-learning simulation in CMS that produces analysis-level events (NanoAOD) directly from generator-level input, at a small fraction of the cost of detailed simulation. Each reconstructed object is generated by its own model, continuous normalizing flows trained with flow matching, conditioned on generator-level information and the per-object models are combined...
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Caio Cesar Daumann (Rheinisch Westfaelische Tech. Hoch. (DE))15/09/2026, 16:20
Monte Carlo simulations are used extensively in high-energy particle physics analyses. However, imperfections in the configuration of detector simulation can lead to significant discrepancies between simulated events and collision data. Such mismodelling is often addressed using scale factors, which can be accompanied by large systematic uncertainties that compromise the sensitivity of...
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Luigi Favaro (Universite Catholique de Louvain (UCL) (BE))15/09/2026, 16:20
Fast parametric detector simulations transform generator-level particles into reconstructed physics objects through a chain of modules controlled by smearing functions. A smearing function contains closed-form resolution and efficiency formulae with numeric coefficients which are traditionally tuned by hand against full simulation or data. We study gradient-based optimization of Delphes3, a...
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Matthew Green (Adelaide University (AU))15/09/2026, 16:40
The precision and reach of physics analyses at the LHC is often tied to the performance of hadronic object reconstruction & calibration, with any incremental gains in understanding & reduced uncertainties being impactful on ATLAS results. Recent improvements from machine learning methods include the calibration & pileup tagging or calorimeter clusters, regressions of b-jet and boosted jet...
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Ya-Feng Lo (University of California Los Angeles (US))15/09/2026, 16:40
Parnassus is a generative detector-simulation model that learns the mapping from generator-level particles to reconstructed particle-flow objects. We apply it to simulated hadronic Z-boson events from legacy e⁺e⁻ collider experiments. Using ALEPH simulation, we validate Parnassus across particle-, jet-, and event-level observables, including particle identification, reconstructed vertices, jet...
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15/09/2026, 17:00
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Anna Hallin (University of Hamburg), Anna Maria Cecilia Hallin (Universität Hamburg)16/09/2026, 09:00
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Eric Anton Moreno (Massachusetts Inst. of Technology (US))16/09/2026, 09:30
Experimental high energy physics analysis is traditionally a multi-year effort dominated by repetitive implementation that demands little physics insight. LLM-based AI agents can now autonomously execute substantial portions of this pipeline, collapsing the implementation bottleneck to roughly ten hours of wall-clock time. This talk surveys the rapidly developing landscape of agentic AI in...
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Andreas Hermansen (Universite de Geneve (CH))16/09/2026, 10:30
Deep learning classifiers for high-energy physics have recently advanced along two largely separate tracks: self-supervised pretraining on large unlabelled datasets, and architectures that respect Lorentz symmetry by construction. How these directions combine has not been shown.
To address this, we integrate Lorentz-invariant models into the LeJEPA pretraining framework, in which the...
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Sitian Qian (Northwestern University and Fermilab)16/09/2026, 10:30
In the upcoming High Luminosity LHC era, detector simulation will face computing resource constraints; at the same time CMS will be upgraded with the new High Granularity Calorimeter (HGCal), which is more intensive to simulate. This computing challenge motivates the use of generative machine learning models as surrogates to replace full physics-based simulation of particle showers in the...
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Florian Ernst (Heidelberg University (DE))16/09/2026, 10:50
Simulating electromagnetic and hadronic showers accurately is among the most computationally demanding parts of the ATLAS detector simulation. To cut CPU consumption for Run 3, the collaboration deployed AtlFast3, a fast simulation tool that pairs traditional histogram-based parameterisations with GAN-based calorimeter models. For the upcoming Run 4 of the LHC, work began on optimising the...
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Anuranjan Sarkar (DESY)16/09/2026, 10:50
In recent years, several pre-training strategies have been proposed for foundation models in jet physics. These approaches range from self-supervised generative tasks like next-token prediction (NTP) and masked particle modeling (MPM), to standard supervised classification. Inputs to foundation models are often tokenized, but this leads to a loss of information. Recent work has shown that...
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Thorsten Buss (RWTH Aachen)16/09/2026, 11:10
To address the high computational demand of detector simulations in high-energy physics, various generative surrogate models have been developed. Traditionally, one generative model per incident particle type is trained, requiring separate trainings and model weights. This increases training and human effort, as well as memory footprint during inference, since multiple sets of weights must be...
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David Rousseau (IJCLab-Orsay)16/09/2026, 11:10
In collider-based particle physics experiments, proton collision events are commonly represented as tabular datasets for specific final states, features being four-vectors of final state particles or higher-level variables (invariant masses).
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Inspired by the success of foundation models in language and vision, recent developments have introduced tabular foundation models such as TabPFN and... -
Martina Mozzanica (University of Hamburg)16/09/2026, 11:30
Fast, reliable surrogates for detector simulation are essential to support the physics program of the high-luminosity LHC (HL-LHC) and future collider experiments. We present a generative model for pion showers in the ECal and HCal of the International Large Detector (ILD), representing each shower as a high-granularity point cloud where every point encodes position, energy, and — for the...
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Tianji Cai16/09/2026, 11:30
Foundation models have demonstrated remarkable performance in collider physics, yet little is understood about the geometric principles underlying their neural representations. We propose representation geometry as a new direction for Scientific AI, where machine learning models serve not only as powerful predictors but also as “microscopes” for revealing the intrinsic structure of the...
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Dan Godi (Weizmann Institute of Science)16/09/2026, 11:50
Full simulation and reconstruction are projected to become major bottlenecks for computation at the High-Luminosity LHC, motivating the need for fast, ML-based surrogates. At the same time, LLMs have driven fast progress in generative discrete modeling: autoregressive transformers trained on tokenized data now represent the state of the art across a range of generative tasks. We extend the...
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Peter McKeown (CERN)16/09/2026, 11:50
The substantial computational burden associated with the use of traditional Monte Carlo simulation for the calorimeter systems of high energy physics experiments has driven the development of numerous deep generative models for fast calorimeter simulation. Recently, several models have been proposed which move away from the common image-like representation of a shower using a regular grid to a...
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Marco Menen (TU Dortmund)16/09/2026, 12:10
The top quark is well suited for precision tests of the Standard Model and also provides a unique window into physics beyond the SM, especially via probing of its spin state. Its spin information is encoded in its decay products and is needed for the construction of genuine CP-odd observables, making an accurate polarization reconstruction essential. In this work, we employ L-GATr to study the...
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Mr Chen-Hua Hsu16/09/2026, 14:00
Precision studies of $\tau^+\tau^-$ production at the Z pole provide a clean environment for investigating electroweak spin correlations and quantum information observables. Using archived LEP-1 data collected by the DELPHI experiment, the process $e^+e^- \to Z \to \tau^+\tau^-$ is well measured, but the presence of multiple neutrinos in $\tau$ decays limits reconstruction of the $\tau$-pair...
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Henning Rose (Uni Hamburg)16/09/2026, 14:00
We introduce SPADE (SPlit And Delay Embeddings), which embeds each feature of a token independently and staggers the resulting streams along the sequence with progressively increasing delays. The vocabulary then scales additively rather than multiplicatively, while intra-token correlations are recovered by the ordinary causal self-attention mechanism: each feature is predicted at its own...
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Dr Minh Tuan Pham (IJCLab, Université Paris-Saclay, CNRS/IN2P3)16/09/2026, 14:20
Detector simulation is among the most resource-intensive components of modern collider experiments, currently consuming roughly half of the LHC computing budget and more still in the High-Luminosity phase. Normalizing flows are attractive surrogate models for fast simulation: they sit on the Pareto frontier between inference speed and accuracy compared with competing generators such as VAEs...
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Mr Ting-Hsiang Hsu16/09/2026, 14:20
Precise reconstruction of event kinematics in the presence of invisible particles constitutes a fundamental underconstrained inference problem in particle physics. In processes such as dileptonic $t\bar{t}$ production, multiple undetected neutrinos lead to a multimodal solution space, where several kinematically consistent configurations can explain a single observed event.
We present a...
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Rebecca Maria Wolfgramm-Kuntz (Astronomisches Rechen-Institut, Zentrum für Astronomie, Universität Heidelberg)16/09/2026, 14:40
Many classification and reconstruction tasks in physics rely on learned latent representations of the data. When networks are trained with a notion of locality, they encode task-specific similarity as closeness in the latent space. Differential geometry, particularly information geometry, is a powerful tool to uncover the learned information in these latent representations and thereby retrace...
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Vinicius Massami Mikuni (Lawrence Berkeley National Lab. (US))16/09/2026, 14:40
Foundation models have the potential to expand the discovery reach for new physics searches. In this paper, a full physics analysis using CMS Open Data is carried out to search for new physics in dijet events using the OmniLearned foundation model. We find that the background estimation describes the data well in validation regions, but is unable to accurately model the signal region where a...
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Soumya Shaw (CISPA, Saarland University)16/09/2026, 15:00
Foundation models have recently emerged as a promising approach for learning transferable representations from low-level particle physics data. In this work, we investigate the application of the OmniJet-α foundation model, to jet anomaly detection, focusing on how pretraining strategies and downstream optimization affect performance on the LHC Olympics (LHCO) benchmark. We systematically...
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Benedikt Schosser16/09/2026, 15:00
Latent representations are an important theme in modern machine learning. While information geometry provides a framework to analyze their structure, it also offers new insight into the physics learned by jet-tagging networks. We apply these methods to binary quark-gluon classification and three-fold fat-jet tagging and relate the learned latent representation to characteristic features of QCD...
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Yue Xu (University of Washington (US))16/09/2026, 15:20
Agentic AI frameworks offer a promising path toward automated HEP analyses, but current approaches rely on iterative prompting and accumulated context, leading to limitations in reproducibility and generality. We try to address these limitations by designing a structured agentic framework that drives a physics foundation model (FM) as its engine, replacing ad-hoc prompt engineering with...
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Sitian Qian (Northwestern University and Fermilab)16/09/2026, 15:20
Particle Transformers have achieved state-of-the-art performance in jet tagging, but the physical information underlying their decisions remains difficult to characterize. This limits our ability to assess their robustness, diagnose sensitivities to Monte Carlo mismodeling, and design effective pretraining strategies. We introduce a Jacobian-lens framework that resolves the response of a...
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Sofia Palacios Schweitzer (Rutgers University)16/09/2026, 15:40
Autonomous language-model agents are increasingly evaluated on long-horizon tool-use tasks, but existing benchmarks rarely capture the complexity and nuance of real scientific work. To address this gap, we introduce ColliderBench, a benchmark for evaluating whether LLM agents can reproduce parts of experimental analyses from the Large Hadron Collider (LHC) using only public papers and open...
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Daniel Tiourine16/09/2026, 15:40
A growing body of work on large language models has focused on steering vectors, which are linear directions within a model’s latent space that correspond to interpretable language concepts the model has learned. However whether such interpretable directions exist in models trained on physics data, that correspond to physics concepts, remains underexplored. We investigate this in the context...
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Alan Gu16/09/2026, 16:30
Deep learning (DL) approaches to high-energy jet tagging achieve state-of-the-art performance over classical methods, but lack human interpretability. We propose a framework for constructing post-hoc interpretable symbolic surrogates of state-of-the-art DL jet taggers trained on particle cloud input representations. A Deep Sets variational autoencoder first learns fixed-size tabular...
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Daniel Schiller (Institute for Theoretical Physics, Heidelberg University)16/09/2026, 16:30
We present MadAgents, an effective and communicative set of agents for working with MadGraph. Agentic installation, learning-by-doing training, user support, and autonomous simulation campaigns provide easy access to state-of-the-art simulations and accelerate LHC research. We show how MadAgents interact with inexperienced and advanced users, support a range of simulation tasks, and analyze...
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Konstantin Matchev (University of Alabama (US))16/09/2026, 16:50
I will review the first generation of agentic AI tools for high-energy theory and simulations, including HEPTAPOD for orchestrating collider phenomenology workflows and Diagrammatica for autonomous symbolic Feynman-diagram calculations. I will also describe the ongoing community efforts for creating an ecosystem-level infrastructure for deployability, reproducibility, community uptake, and...
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Sanmay Ganguly (Indian Institute of Technology Kanpur (IN))16/09/2026, 16:50
We open the black box of machine-learning jet taggers, asking how they compute their decisions and whether they rediscover QCD. Applying the causal mechanistic-interpretability toolkit (ablation, path patching, logit-lens, probing) to a Particle Transformer top-tagger, we isolate a sparse six-head source -> relay -> readout circuit that recovers 97.3% of full-model AUC, encodes the...
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Hancheng Li (Rutgers University)16/09/2026, 17:10
As particle collider experiments produce increasingly large and complex datasets, a fundamental question arises: how should we quantify the similarity between two events? A variety of physically motivated metrics have been developed—from optimal transport to phase-space distances—yet the geometric structures induced by these metrics remain largely unexplored. We introduce the Multi-Reference...
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Aman Upadhyay (Rutgers University)16/09/2026, 17:10
LLM agents increasingly assist with coding, but still struggle with long, tool-heavy, multi-step tasks common in high-energy physics. We present Ariadne, an autonomous multi-role LLM agent that, given a natural-language task, is designed to plan, divide, and execute this workflow step by step.
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Ariadne's orchestration is based on a LangGraph state that organizes the workflow context into... -
Nikita Schmal (Universität Heidelberg)16/09/2026, 17:30
Analysis re-casting at the LHC is highly standardized and nevertheless requires resources, time, and expert physics input. Building on the newly developed MadAgents.v3 framework, we demonstrate how a global SMEFT analysis can be updated and improved through an agentic workflow with a physicist in the loop. Although demonstrated within the SFitter framework, the underlying technical aspects of...
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Raphael Bonnet Guerrini (Computer Science Dep. University of Milan)16/09/2026, 17:30
We show that Shapley values can be used to trace how individual parton distributions (PDFs) shape the theory predictions for high-energy observables computed from them.
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This provides a tool for assessing the impact of data on PDFs when determining them, and the impact of PDF uncertainties when using the PDFs to compute collider observables.
The Shapley value is computed by treating the... -
Susie Kim (ITP, Heidelberg University)16/09/2026, 17:50
Due to the non-perturbative nature of hadronization, its simulation relies on a fragmentation function of a fixed parametric form. We present HOMER, a data-driven alternative based on neural networks that extracts the Lund string fragmentation function directly from data. HOMER addresses the information gap between the latent and observable phase spaces through an iterative reweighting...
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Darius Faroughy (Rutgers University)16/09/2026, 17:50
LLM agents are beginning to take on real high-energy-physics workflows — tasks that demand orchestrating full toolchains of event generators, detector simulation, and analysis code rather than simply producing text. Making such agents reliable, auditable, and cheap enough to run on local hardware raises a distinct set of questions from those studied in general-purpose agent benchmarks.
This...
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Marie Hein (RWTH Aachen University)17/09/2026, 09:00
Traditional new-physics searches face a trade-off between coverage and sensitivity. Anomaly detection offers a way to extend their reach by searching for unusual signatures without assuming a specific signal model. Over the past decade, the field has developed a broad range of anomaly-detection paradigms and methods. More recently, the focus has begun to shift from developing new methods...
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Sioni Paris Summers (CERN)17/09/2026, 09:30
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Rikab Gambhir (University of Cincinnati)17/09/2026, 10:30
Weakly-supervised anomaly searches, such as CWOLA and CATHODE, typically train a classifier to separate a signal region from a sideband-estimate background and then cut on the classifier output to create a signal-enriched dataset. We show that there is a more efficient use of the output: the same classifier output can be used instead as a per-event weight, This results in a more powerful...
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Philipp Wagner (ETH Zürich), Younes Malek Elberkennou (ETH Zürich)17/09/2026, 10:30
The CMS 40 MHz Scouting program at the High-Luminosity LHC requires aggressive real-time compression of particle-flow event information to operate within stringent bandwidth constraints. We investigate the use of quantized autoencoders for converting Level-1 event representations into compact sequences of discrete tokens. We explore vector quantization, finite scalar quantization, and...
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Mark Fanselow (RWTH Aachen University)17/09/2026, 10:50
Resonant anomaly detection is a promising strategy for extending the reach of bump-hunt searches at the LHC. In a weakly supervised setup, a high-quality background template is essential to produce an anomaly score but can serve a second purpose: It allows to directly estimate the background expectation in a simple cut and count setup, removing the problem of background sculpting. For...
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Stella Felice Schaefer (Hamburg University (DE))17/09/2026, 10:50
At the Phase-2 Upgrade of the CMS Level-1 Trigger (L1T), particles will be reconstructed by linking charged particle tracks with clusters in the calorimeters and muon tracks from the muon stations. The 200 pileup interactions will be mitigated using primary vertex reconstruction for charged particles and a weighting for neutral particles based on the distribution of energy in a small area....
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Dorian Sloot (Austrian Academy of Sciences (AT))17/09/2026, 11:10
Real-time jet classification in high-energy physics requires high predictive performance under strict latency, throughput, and FPGA-resource constraints. Although aggressive weight quantisation promises simpler arithmetic, reductions in numerical precision and theoretical operation count do not necessarily translate into more efficient synthesised implementations. This study investigates...
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Lukas Lang (RWTH Aachen University)17/09/2026, 11:10
Recent years have seen rapid progress in resonant anomaly detection for collider searches, but existing studies often rely on a limited set of signal benchmarks and face a trade-off between sensitive but model-dependent high-level observables and fully agnostic but less performant low-level representations. We address both limitations by introducing new simulated signal benchmarks, publicly...
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Rafal Maselek17/09/2026, 11:30
Discovering new particles from beyond the Standard Model remains one of the main goals of present-day particle physics. Traditional searches for new physics at the Large Hadron Collider rely on specific theoretical scenarios and simulation-based background estimates, limiting their reach and introducing modeling uncertainties. We present an anomaly detection method that uses normalizing flows...
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Sanjiban Sengupta (The University of Manchester (GB))17/09/2026, 11:30
SOFIE (System for Optimized Fast Inference code Emit), being developed by the ML4EP Project at CERN, translates trained machine learning models into self-contained, low-latency C++ code that is portable, hardware-agnostic, and highly optimized while depending only on BLAS libraries.
SOFIE achieves portability across heterogeneous computing architectures by leveraging the abstract buffer...
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Chirayu Gupta (Vrije Universiteit Brussel (BE))17/09/2026, 11:50
Jet clustering remains one of the few steps in modern analysis and reconstruction chains that is still bound to the CPU. Machine learning workflows that wish to recluster jets inside the training loop, scan the jet radius, or access substructure dynamically must pay for repeated transfers between host and device, and reconstruction itself is steadily moving toward heterogeneous environments in...
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Antonio D'Avanzo (University Federico II and INFN, Naples (IT)), Elvira Rossi (University Federico II and INFN, Naples (IT)), Francesco Cirotto (University Federico II and INFN, Naples (IT)), Francesco Conventi (Università degli studi di Napoli "Parthenope" and INFN Sezione di Napoli (IT)), Graziella Russo (University of California,Santa Cruz (US))17/09/2026, 11:50
Searches for physics beyond the Standard Model at the LHC are traditionally optimized for specific signal hypotheses. Anomaly detection provides a complementary approach by identifying events that deviate from the expected Standard Model background without relying on a particular new-physics model. In fully hadronic final states, these techniques exploit the rich information encoded in the...
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Riku Takizawa (UTokyo)17/09/2026, 12:10
SuperKEKB is an electron–positron collider operating at a center-of-mass energy of 10.58 GeV and has achieved the world’s highest instantaneous luminosity. At present, collision parameters are optimized manually through a procedure known as an interaction-point (IP) knob tuning. This study aims to improve the efficiency and reproducibility of IP knob tuning, and ultimately automate the process...
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Marie Hein (RWTH Aachen University)17/09/2026, 12:10
Machine learning–based anomaly detection can search for new physics in high-dimensional data with minimal theory bias. However, because these methods scan many possibilities at once, they suffer from a look-elsewhere effect that weakens statistical significance. We study this in weakly supervised settings and find a key trade-off: training and testing on the same data gives high sensitivity...
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Vincent Benne (RWTH Aachen University)17/09/2026, 14:00
Weakly supervised anomaly detection has been shown to be an effective tool for model agnostic searches for new physics, especially in the context of resonance searches. However, to integrate weak supervision into standard resonance search analysis workflows, which rely on fits in the sidebands for the background estimation, anomaly scores need to be well behaved in the sidebands. In order to...
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Jaco ter Hoeve (The University of Edinburgh)17/09/2026, 14:00
The precise determination of the parton distribution functions (PDFs) of the proton is an essential ingredient for LHC analyses, including for those at the upcoming High-Luminosity LHC. So far, PDFs are determined from global fits to binned low-dimensional data obtained from unfolded hard-scattering cross section measurements. In this talk, we demonstrate the feasibility of neural...
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Dennis Noll (Stanford University)17/09/2026, 14:20
The Higgs boson, with its universal coupling to mass, provides a broadly applicable portal to sectors beyond the Standard Model and is therefore a natural anchor for anomaly detection (AD) at collider experiments. The Higgs And X Anomaly Detection (HAXAD) strategy offers a principled approach to searching for anomalies occurring in association with a Higgs boson. In the kinematic region of the...
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Nino Kovacic (University of Zagreb (HR))17/09/2026, 14:20
Neural Simulation Based Inference (NSBI) is a collection of statistical machine learning methods that learn the likelihood or posterior based on high-dimensional input data. One flavor of NSBI, Neural Likelihood Ratio Estimation (NLRE), has emerged as a powerful method in the domain of High Energy Physics (HEP), having recently been used in Higgs-boson measurement [1] at the Large Hadron...
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Ranit Das (Heidelberg University)17/09/2026, 14:40
Simulation-based inference (SBI) is a powerful tool for likelihood-free parameter estimation, but often requires large numbers of simulated events. At the LHC, where event generation can be computationally expensive, particularly with restrictive generator-level cuts and detector simulation, this can become a major bottleneck. We demonstrate sequential SBI for the inference of resonance masses...
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Tore Von Schwartz (Hamburg University (DE))17/09/2026, 14:40
In the absence of direct evidence for new physics in targeted searches, model-independent strategies are becoming increasingly important. In this talk, we present recent results of model-agnostic searches that are facilitated by advanced machine learning techniques, opening a new avenue for unbiased detection of potential new physics signals.
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Nityaansh Parekh (Michigan State University (US))17/09/2026, 15:00
CP-sensitive observables in H+2jet production, such as the azimuthal angle between the leading jets, are defined at parton level but measured at detector level, requiring unfolding to recover them. We present a conditional invertible neural network (cINN) that learns a full posterior over parton-level kinematics given detector-level observables trained jointly across multiple EFT coupling...
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Jonathan Ostertag-Henning (Institute for Theoretical Physics, Heidelberg University)17/09/2026, 15:00
Unsupervised anomaly detection with autoencoders is a promising data-driven and model-agnostic approach for new physics searches at the LHC. However, current anomaly scores assigned by neural networks suffer from a lack of statistical interpretability. The normalized autoencoder (NAE) combines a standard bottleneck architecture with a well-defined probabilistic description. We show that the...
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Amishi Agrawal (KJ Somaiya School of Engineering)17/09/2026, 15:20
Learned anomaly triggers such as CMS AXOL1TL and CICADA are calibrated against a fixed background model, but there is no standard way to check in real time whether that calibration still holds as detector conditions change. I present two components that address this from different angles.
The first is a sequential change-point detector that monitors trigger calibration health online....
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Sebastian Pitz (LPNHE Paris)17/09/2026, 15:20
Machine learning enables unbinned unfolding with per-event posteriors, but a measurement is only as good as its error bars. We dissect the uncertainty budget of conditional flow matching unfolding for WZ production. Bootstraps propagate the statistics of the training sample and, by reweighting, of the data. We show how weight sampling from a Bayesian neural network relates to the spread of...
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Yong Sheng Koay17/09/2026, 15:40
Collider simulations simultaneously provide three sources of information: whether an event is signal or background, the physics parameters that generated each signal event, and the event kinematics from which signal regions are defined. Conventional pipelines use these sources in separate stages—training classifiers for signal discrimination, performing parameter inference for fixed analysis...
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Ayodele Ore17/09/2026, 15:40
Correcting measurements for detector effects is a pressing inverse problem in LHC physics. Current methods solve this problem by relying on iterative refinement, minimax optimization, or a surrogate forward mapping. In this talk, I present Adversary-free Unfolding SanS Iteration or Emulation (AUSSIE), which dispenses with these mechanisms while remaining asymptotically correct. AUSSIE unfolds...
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Kevin Thomas Greif (University of California Irvine (US))17/09/2026, 16:30
The production of a $Z$ boson decaying to muons in association with hadronic jets is a benchmark process in proton--proton collisions at the Large Hadron Collider. It provides a clean experimental signature with high selection efficiency and purity while probing a wide range of Standard Model dynamics. Measurements of this process are typically reported as binned differential cross sections in...
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Joaquin Iturriza Ramirez (LPNHE - Sorbonne Université)17/09/2026, 16:30
Fast and precise evaluations of scattering amplitudes even in the case of precision calculations is essential for event generation tools at the HL-LHC. We explore the scaling behavior of the achievable precision of neural networks in this regression problem for multiple architectures, including a Lorentz symmetry aware multilayer perceptron and a fully Lorentz equivariant transformer using...
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Santiago Tanco17/09/2026, 16:50
Topic modeling techniques allow to learn robust representations of a data in terms of latent themes or topics. In this talk, I will present an application of Latent Dirichlet Allocation that exploits information from multiple Monte Carlo simulation setups of known processes to learn the shapes of observable distributions directly from data. This approach provides a general framework to infer...
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Anna Kindsvater (University of Hamburg)17/09/2026, 16:50
Scaling laws have become a central topic in modern machine learning, providing a quantitative understanding of how model performance improves with increasing model size, training data, and compute. They also offer insights into whether learning is approaching the information limits of a given dataset. In this contribution, we present the first study of neural scaling laws for generative jet...
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Danae Danielle Valdenaire (Austrian Academy of Sciences (AT))17/09/2026, 17:10
CRESST (Cryogenic Rare Event Search with Superconducting Thermometers) is a direct dark matter detection experiment located at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy. It searches for dark matter–nucleus interactions using scintillating cryogenic calorimeters, pushing its energy threshold ever lower to improve sensitivity to low‑mass dark matter. At these thresholds, however,...
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Pavlo Kashko (Vrije Universiteit Brussel (BE))17/09/2026, 17:10
Transformer-based taggers have become the workhorse of heavy-flavour identification at the LHC, with the Unified Particle Transformer (UParT) folding flavour classification, track-level auxiliary tasks and regression into a single architecture. Their development has nonetheless remained largely empirical: model capacity, training-sample size and input granularity are chosen by convention...
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17/09/2026, 19:00
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Felix Weiglhofer (CERN)18/09/2026, 09:00
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Manuel Szewc18/09/2026, 09:30
Hadronization, the transition between unobservable quarks and gluons to observable hadrons, is a key aspect of the theoretical framework of particle physics. However, it is a fundamentally challenging process due to its non-pertubative nature, and thus event generators implement empirical models based on QCD insights. In this talk, I'll detail how Machine Learning has been incorporated into...
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weilin wu (Peking University)18/09/2026, 10:30
Neural-network quantum states provide a flexible representation of high-dimensional many-body wave functions, offering a promising approach to quantum systems that remain challenging for conventional numerical methods. In this talk, I will present their applications to exotic hadrons and nuclei. By combining expressive neural-network wave functions with variational Monte Carlo and...
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Jonas Spinner (Durham University)18/09/2026, 10:30
We study for the first time the benefit of Lorentz-equivariant transformers for large-size jet tagging and flavor tagging. To control their computing demands, we optimize their implementations for inference cost metrics. In our scaling studies, we find that Lorentz-equivariant networks outperform standard transformers provided geometric features are relevant. This holds true in an idealized...
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Nadia Sharna (Bursa Technical University)18/09/2026, 10:50
In collider physics, jet tagging is a key classification task in which models must identify the initiating particle from a reconstructed jet's internal structure. Though their final predictions are often produced by classical neural-network heads, Particle Transformer architectures achieve strong performance by learning interactions among jet constituents. In this paper, the usefulness of a...
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Antoine Petitjean (Heidelberg University)18/09/2026, 10:50
Modern machine learning is transforming jet tagging at the LHC, but the leading transformer architectures are large, not particularly fast, and training-intensive. We present a slim version of the L-GATr tagger, reduce the number of parameters of jet-tagging transformers, and quantize them. We compare different quantization methods for standard and Lorentz-equivariant transformers and estimate...
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Vishal Singh Ngairangbam18/09/2026, 11:10
Improving interpretability is essential for building robust
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and trustworthy machine learning tools in collider physics. To address this challenge, we systematically investigate equivariant and IRC-safe graph neural networks for jet classification. Using simulated jet datasets, we compare IRC-safe architectures with inbuilt E(2) and O(2) equivariance in the rapidity-azimuth plane against... -
Timur Sypchenko (IPPP)18/09/2026, 11:10
Neural quantum states provide expressive variational representations of quantum many-body wavefunctions.
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However, their practical performance depends on how well they can sample the relevant configurations from
an exponentially large Hilbert space. Conventional Markov chain Monte Carlo methods can mix slowly between separated high-probability regions, particularly in frustrated and strongly... -
Louis Choron (Imperial College (GB))18/09/2026, 11:30
As next-generation colliders reach unprecedented energies and luminosities, novel computing techniques become essential to meet the resulting computational challenges. One area of promise is quantum machine learning (QML), which combines the quantum effects of superposition and entanglement with classical optimisation techniques. Within QML, photonic devices are of particular interest due to...
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Umar Sohail Qureshi (Vanderbilt University)18/09/2026, 11:30
IRC safety has long guided the design of robust jet substructure observables, and this principle has increasingly been built into machine-learned taggers as well. For attention-based architectures such as the Particle Transformer (ParT), however, it is non-trivial to enforce IRC safety without discarding the pairwise, energy-dependent features that make these models powerful. In this work, we...
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Aritra Bal (KIT - Karlsruhe Institute of Technology (DE))18/09/2026, 11:50
Jet substructure studies at the LHC often rely on observables that do not fully capture all possible correlations between constituents, especially at higher irreducible orders. We present two approaches for modelling this structure, based on information theory and quantum information geometry. Both methods use the Fisher Information Matrix, the first being constructed classically using the...
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Thanush Sivagnanalingam (University Heidelberg)18/09/2026, 11:50
Neural network training for LHC event generation should, ideally, benefit from common high-level patterns in different processes. We propose novel conditioning schemes for continuous parameters, process labels, and Feynman diagrams. We employ pre-trained LLMs as multi-modal foundation models to provide descriptive embeddings for an autoregressive transformer. With such high-level...
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Luca Della Penna (Universita e INFN, Perugia (IT))18/09/2026, 12:10
The application of quantum algorithms to jet substructure analysis is of growing interest as Noisy Intermediate-Scale Quantum (NISQ) hardware continues to mature in qubit count and gate depth. Jet substructure remains essential for addressing challenges at the LHC and beyond, notably object classification and polarization tagging. However, existing quantum machine learning approaches typically...
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Gemma Tinti (INFN e Laboratori Nazionali di Frascati (IT))18/09/2026, 14:00
Accurate particle tracking using the GigaTracker (GTK) silicon pixel detector represents
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a mission-critical stage in the data processing pipeline of the NA62 experiment at CERN,
which is dedicated to the precision measurement of the ultra-rare decay K+ → π+ν ¯ν.
Operating in a high-intensity environment with a beam rate of up to 750 MHz, the GTK
provides the momentum and direction... -
Paul Schmidt18/09/2026, 14:00
Recent advances in language models have sparked growing interest in their use for specialized scientific applications. In this work, we apply supervised fine-tuning to adapt a small pretrained language model to a task in theoretical particle physics. We consider the generation of particle-physics Lagrangians from structured field descriptions. Given a set of fields, the model must generate the...
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Ziyue Chen (IHEP)18/09/2026, 14:20
We propose CG-SFT, a parameter-efficient framework that turns a causal large language model into a structured hierarchical multi-label classifier without adding a classification head. The label space is encoded as a fixed sequence where each label is followed by a <yes>/<no> decision token. Supervision is applied only to these decision positions using cross-entropy, binary cross-entropy, and...
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Yong Sheng Koay18/09/2026, 14:40
Foundation models embed not only their training examples but the space those examples were drawn from. A transformer trained to write Lagrangians symbolically (such as BART-L) thus embeds the space of theories itself. In this work, we investigate the navigation of this learned theory space using activation steering, adapted from interpretability work on LLMs. Using only small contrastive...
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Giulia Borghetto (University of Swansea)
Recent cosmological observations suggest possible deviations from a cosmological constant, pointing toward a dynamical nature of dark energy. Quintessence models, which assume a slowly rolling scalar field, provide a compelling theoretical framework to explain this late time evolution in the dark energy equation of state. However, identifying the correct form of the quintessence potential...
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Zlatan Dimitrov (GATE Institute; Sofia University St. Kliment Ohridski)
AllShowers (arXiv:2601.11716) marks a significant step towards a universal model for calorimeter shower simulations in collider experiments. Unlike traditional surrogate models that train separate networks for each particle species, AllShowers unifies shower generation across multiple particle types in a single continuous normalizing flow with a Transformer architecture, producing realistic...
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