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24/08/2026, 13:45
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Jan Kieseler (KIT)24/08/2026, 14:00
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Mario Krenn (University of Tübingen)24/08/2026, 14:45
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Florian Ernst (Heidelberg University (DE), CERN)24/08/2026, 16:00Simulations & Generative Models
One of the costliest elements of the ATLAS detector simulation is the precise modelling of electromagnetic and hadronic showers. With the aim of lowering CPU usage in Run 3 of the LHC, the collaboration introduced AtlFast3, a fast simulation tool that combines classical histogram-based parameterisations with calorimeter models built on GANs. Once Run 3 was underway, a new effort was launched...
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Oleg Savchenko (GRAPPA Institute, University of Amsterdam)24/08/2026, 16:00Inference & Uncertainty
Simulation-based inference (SBI) enables Bayesian analysis of complex cosmological data when only a forward model is available, while field-level inference (FLI) aims to perform inference in a maximally efficient way and retain more information than summary-statistic pipelines. In this talk, I will highlight recent advances and applications of SBI and FLI in cosmology. First, I will show how...
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Rebecca Maria Kuntz (Zentrum für Astronomie der Universität Heidelberg, Astronomisches Rechen-Institut)24/08/2026, 16:00Explainability & Theory
Latent representations are an important theme in modern machine learning. Networks trained with a notion of locality encode task-specific similarity as closeness in the latent space. We analyze this latent information for a variational autoencoder with a classifier head using tools from differential geometry, specifically information geometry. Here, we explore the learned latent space using...
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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))24/08/2026, 16:00Patterns & Anomalies
Since the discovery of the Higgs boson, no experimental evidence for physics beyond the Standard Model has been observed at the LHC. While most searches rely on specific signal hypotheses, anomaly detection aims at identifying potential new-physics signatures without assuming a particular BSM model.
In recent years, unsupervised machine learning has become an active area of research in...
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Thorsten Buss (RWTH Aachen)24/08/2026, 16:10Simulations & Generative Models
To reduce the high computational demand of detector simulations in high-energy physics, various generative surrogate models have been proposed. Classically, one generative model per incident particle type is trained, requiring separate trainings and model weights. This results in increased training and human effort, as well as a larger memory footprint during inference, since multiple sets of...
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Aritra Bal (Karlsruhe Institute of Technology (KIT))24/08/2026, 16:10Explainability & Theory
Pairwise Fisher graphs capture local covariance information, but they cannot distinguish an irreducible multi-observable radiation pattern from a collection of ordinary pairwise correlations. We show that the higher Fisher tensors supply this missing structure. In a finite basis of binned EECs, ECFs, or EFPs, and in the natural exponential-family coordinates generated by that basis, the...
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Christopher Eckner (Instituto de Astrofísica de Canarias (IAC))24/08/2026, 16:10Inference & Uncertainty
Since its mission started more than 17 years ago, the Fermi Large Area Telescope (LAT) has significantly advanced our view of the GeV gamma-ray sky, yet several key questions remain - such as the nature of the isotropic diffuse background, the properties of the Galactic pulsar population, and the origin of the GeV excess towards the Galactic Centre. Addressing these challenges requires...
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Theresa Reisch (University of Geneva)24/08/2026, 16:10Patterns & Anomalies
The LHC search programme remains largely model-driven, probing one final state and one model hypothesis at a time, complemented by occasional model-agnostic searches such as those in dijet spectra. We aim to take the next step: probing many final states at once, in an automatic and robust way, using limited resources efficiently to cover large regions of phase space. This combines reusing our...
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Chitrakshee Yede (Universität Hamburg)24/08/2026, 16:20Patterns & Anomalies
The search for physics beyond the standard model is one of the prime focuses in high-energy physics. Conventional searches at the LHC, though comprehensive, have not yet shown signs for new physics. Machine learning based anomaly detection has emerged offering a model-agnostic way to enhance the sensitivity of generic searches as compared to those targeting specific signal model. CATHODE...
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Lorenzo Valente (University of Hamburg)24/08/2026, 16:20Simulations & Generative Models
Deep generative surrogates can cut the computing cost of detailed Geant4 calorimeter simulation, but they remain tied to the detector they were trained on: each new geometry typically demands a large in-domain dataset. We present two studies that reduce this dependence for point cloud generative models.
In the first, a CaloClouds-based diffusion model pre-trained on ILD photon showers...
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Xingyu Guo (South China Normal Univeristy)24/08/2026, 16:20Explainability & Theory
We propose a statistical-field framework for text generated by large language models (LLMs), treating token embeddings as continuous spin variables on a one-dimensional chain. Defining a susceptibility from the connected two-point correlator and an order parameter from the ensemble-averaged embedding field, we vary the softmax temperature $T$ and observe a sharp susceptibility peak near a...
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Youyou Li (GRAPPA, University of Amsterdam)24/08/2026, 16:20Inference & Uncertainty
The high-energy neutrino emission observed from the Galactic plane by IceCube may contain contributions from both truly diffuse emission produced by cosmic-ray interactions with interstellar gas and a population of individually unresolved hadronic accelerators. Traditional likelihood-based inference is impractical here, since it would require explicit marginalization over the unknown number,...
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Rafał Masełek (Jozef Stefan Institute)24/08/2026, 16:30Patterns & Anomalies
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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Henry Day-Hall (DESY)24/08/2026, 16:30Simulations & Generative Models
Physics employs careful factorising of effects to make powerful general predictions. In a Mixture of Experts network, well chosen factorisation of knowledge can improve both memory footprint and generalisation.
This work explores the relative merits of Expert networks, in comparison to equivalent generalist models. In the setting of fast generative calorimeter simulation, both architectures...
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Gianmarco Puleo (Scuola Internazionale Superiore di Studi Avanzati (SISSA))24/08/2026, 16:30Inference & Uncertainty
The Laser Interferometer Space Antenna (LISA) will observe gravitational waves produced by several massive black hole binary (MBHB) mergers per year. While the likelihood can be written in closed form under idealised stationary, Gaussian noise assumptions, including realistic effects — instrumental glitches, gaps in the data, and non-stationary noise — make it intractable or computationally...
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Yong Sheng Koay (Uppsala University)24/08/2026, 16:30Explainability & Theory
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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Dennis Noll24/08/2026, 16:50Patterns & Anomalies
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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Sinan Deger (University of Cambridge)24/08/2026, 16:50Inference & Uncertainty
Panchromatic surveys such as COSMOS have expanded our understanding of how galaxies evolve through cosmic time immensely. Surveys such as the Vera C. Rubin Observatory's LSST are promising an unprecedented view of this evolutionary paradigm provided the massive data challenge they pose is answered. We have been developing pop-cosmos, a comprehensive galaxy population model housing a...
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Dmitrii Kobylianskii (Weizmann Institute of Science)24/08/2026, 16:50Simulations & Generative Models
Detector simulation and event reconstruction constitute the primary computational bottleneck in modern particle physics. To bypass these CPU-intensive processing chains, we present Parnassus (Particle-flow Neural Assisted Simulations), a state-of-the-art generative AI framework for end-to-end fast simulation. Utilizing conditional flow matching, Parnassus maps truth-level stable particles...
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Darius Jurčiukonis (Vilnius University (LT))24/08/2026, 16:50Explainability & Theory
We consider a three-Higgs-doublet extension of the electroweak Standard Model invariant under a $Z_2 \times Z_2$ symmetry. Since necessary and sufficient bounded-from-below (BFB) conditions are not known for this model, we propose an approach based on increasingly stringent necessary conditions. This procedure is implemented in the Mathematica package StableWein, which allows the user to...
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Stephen Jiggins (DESY)24/08/2026, 17:00Inference & Uncertainty
Neural simulation-based inference (NSBI) has emerged as a powerful methodology for unbinned likelihood analyses in high-energy physics (HEP), with recent ATLAS [1] results at the Large Hadron Collider (LHC) demonstrating substantial sensitivity gains over binned methods. The dominant NSBI variant in HEP, Neural Likelihood-Ratio Estimation (NLRE), owes its popularity to the simplicity of the...
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Mr Oliver Schumann (Ludwig-Maximilians-Universität München)24/08/2026, 17:00Simulations & Generative Models
By striving for ever-higher luminosities, the Belle II detector is set to observe rare decay signals. However, these high luminosities correspond to an increased demand for MC-simulated events. Therefore, an efficient algorithm to produce and process simulated events in large quantities is vital for the successful interpretation of Belle II’s data. While generating events in the MC simulation...
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Jonathan Ostertag-Henning (ITP Heidelberg)24/08/2026, 17:00Patterns & Anomalies
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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Robert McNulty (University of Liverpool (GB))24/08/2026, 17:00Explainability & Theory
With the increasing volume and complexity of data, machine learning (ML) models are becoming indispensable tools for identifying patterns within structured datasets. However, as these models grow in complexity, it becomes challenging to determine whether they are truly learning meaningful relationships or capturing unintended artifacts. This lack of interpretability leads to mistrust,...
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Saulo Soares de Albuquerque Filho (University of Tuebingen)24/08/2026, 17:10Simulations & Generative Models
In this work, we present differentiable and hardware-accelerated implementations of the machine-learning surrogate models. The proposed framework enables just-in-time compilation, vectorized paralellization, native GPU acceleration, and automatic differentiation, while preserving the original surrogate-training infrastructure and its applicability to arbitrary non-precessing waveform...
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Seyedeh Fatemeh Mirekhtiary (Near East University)24/08/2026, 17:10Explainability & Theory
Artificial intelligence methods are increasingly used in physics to support classification, prediction, uncertainty handling, and interpretable decision-making. In medical physics, radiation risk assessment often depends on multiple interacting parameters, including absorbed dose, exposure duration, source activity, irradiated skin area, radiation energy, source-to-skin distance, shielding...
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Andre Scaffidi (SISSA)24/08/2026, 17:10Simulations & Generative Models
I will introduce introduce Rapid Simulation Based Inference (RSBI), a diffusion-based variational approach to likelihood-free Bayesian inference that achieves high sampling efficiency under large prior-to-posterior volumes with multi-modal posterior structure.
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RSBI builds on advances in Schrödinger Bridge diffusion sampling to handle multi-modal posteriors, with the likelihood initially... -
ASRITH KRISHNA RADHAKRISHNAN (University of Bologna (IT))24/08/2026, 17:10Patterns & Anomalies
An unsupervised machine learning framework is developed for searching for anomalous events in proton-proton collisions at √s=13 TeV using ATLAS Open Data. The analysis utilizes an object-based Masked Transformer autoencoder trained exclusively on Standard Model Run 2 data to learn complex correlations among reconstructed physics objects and identify events that deviate from expected Standard...
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Lukas Lang (RWTH Aachen University)24/08/2026, 17:20Patterns & Anomalies
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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Joy Sanghavi (UvA)24/08/2026, 17:20Inference & Uncertainty
The 21 cm signal from neutral hydrogen is a key probe of the Epoch of Reionization (EoR), marking the universe’s transition from a cold, neutral state to a predominantly hot, ionized one, driven by the formation of the first stars and galaxies. Extracting this faint 21 cm signal from radio interferometric data requires precise gain calibration. However, traditional calibration methods are...
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Luigi Favaro (UCLouvain - CP3)24/08/2026, 17:20Simulations & Generative Models
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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Ramon Winterhalder (University of Milan)25/08/2026, 09:00
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Lucie Flek (University of Bonn)25/08/2026, 09:45
What can we actually conclude when a model gives us the right answer? Did it learn the right correlations? Does it generalize beyond the setting in which we tested it? And what does it mean to validate increasingly complex "scientific AI" models and workflows? I will explore these questions through examples and cautionary tales from language modeling, human modeling, particle physics and...
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Leonardo Plini (INFN-LNF)25/08/2026, 11:00Patterns & Anomalies
Accurate particle tracking using the GigaTracker (GTK) silicon pixel detector represents 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 $K^{+} \rightarrow \pi^{+} \nu \bar{\nu}$. Operating in a high-intensity environment with a beam rate of up to 750 MHz, the GTK provides the...
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Matthias Vigl (TUM)25/08/2026, 11:00Foundation Models
Recent progress in machine learning has come as much from scale as from architecture, and high-energy physics is starting to follow: work on transformer scaling for flavour tagging in ATLAS [https://inspirehep.net/literature/3114069] has shown that tagger performance follows predictable power laws over orders of magnitude in compute on a multi-billion-jet dataset. This matters particularly in...
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Henning Bahl25/08/2026, 11:00Inference & Uncertainty
Ultra-fast, precise, and controlled amplitude surrogates are essential for future LHC event generation. First, we investigate the noise reduction and biases of network ensembles and outline a new method to learn well-calibrated systematic uncertainties for them. We also establish evidential regression as a sampling-free method for uncertainty quantification. In a second part, we tackle...
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Gert Aarts (Universitaet Bielefeld)25/08/2026, 11:00Simulations & Generative Models
Diffusion models are a widely used method in generative AI to produce images and videos. I will discuss the application to lattice field theory and the connection with methods known from theoretical physics, such as stochastic quantisation. Applications to non-abelian gauge theories using gauge-equivariant convolutional neural networks are presented.
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Bao-Dong Sun (Ruhr-Universität Bochum)25/08/2026, 11:10Simulations & Generative Models
We apply score-based diffusion models to two-dimensional SU(2) lattice pure gauge theory with the Wilson action, extending recent work on U(1) gauge theories. The SU(2) manifold structure is handled through a quaternion parameterization. The model is trained on 10,000 configurations generated via Hybrid Monte Carlo at a fixed coupling $\beta_0= 2.0$ on an $8\times 8$ lattice, augmented to...
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Mr Suprio Dubey (Institute for Theoretical Physics and Mannheim Institute for Intelligent Systems in Medicine, Heidelberg University)25/08/2026, 11:10Inference & Uncertainty
Neural network surrogates for LHC scattering amplitudes require trustworthy uncertainty estimates, a challenging task given the non-Gaussian systematics. We target it 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....
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Dr Zubayer Ahammed (Variable Energy Cyclotron Centre, India)25/08/2026, 11:10Patterns & Anomalies
Resistive Plate Chambers (RPCs) are widely used as tracking detectors in high-energy physics experiments due to their simplicity, robustness, and excellent timing performance. However, low-resistive bakelite RPC prototypes often exhibit secondary hit components that degrade time and position resolution and introduce background, complicating track reconstruction. In this work, we present a...
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Anna Hallin (anna.hallin@uni-hamburg.de)25/08/2026, 11:10Foundation Models
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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Andreas Ipp (TU Wien)25/08/2026, 11:20Simulations & Generative Models
Numerical simulations of quantum field theories are indispensable for precision studies of the Standard Model, yet extracting continuum physics from discretized spacetime remains challenging due to lattice artifacts. Renormalization-group improved fixed-point (FP) actions provide an elegant solution by suppressing these artifacts, but their complexity has long prevented their practical use....
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Humberto Reyes-Gonzalez (RWTH Aachen University)25/08/2026, 11:20Inference & Uncertainty
Generative models are becoming an integral component of scientific workflows, yet their reliable deployment requires a rigorous understanding of their limitations through statistically robust validation. In particular, high-dimensional settings demand powerful goodness-of-fit tests that provide well-defined statistical interpretations and enable hypothesis testing. In this contribution, we...
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Guang Zhao (Institute of High Energy Physics (CAS))25/08/2026, 11:20Patterns & Anomalies
PID is essential in high energy physics experiments, particularly for future e+e- colliders. The tera-z samples at FCC or CEPC offers vast flavor physics opportunities, necessitating robust hadron identification over a broad momentum range. Moreover, there is growing recognition of the performance gains that PID provides for jet flavor tagging. A key breakthrough in PID is cluster counting...
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Joaquin Iturriza Ramirez (LPNHE - Sorbonne Université)25/08/2026, 11:20Foundation Models
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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Sascha Diefenbacher (ITP Heidelberg)25/08/2026, 11:30Inference & Uncertainty
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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Pavish Subramani (University of Wuppertal)25/08/2026, 11:30Patterns & Anomalies
The Compressed Baryonic Matter (CBM) experiment is a fixed target heavy-ion collision experiment currently being built at the Facility for Antiproton and Ion Research (FAIR), Darmstadt, Germany.
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The CBM experiment is designed to probe the QCD phase diagram at moderate temperatures and high net-baryon density via different physical observables, including flow, fluctuations, and correlations of... -
Prof. Kai Zhou (CUHK-Shenzhen)25/08/2026, 11:30Simulations & Generative Models
Generative AI is rapidly becoming more than a tool for producing realistic images: in physics, it can be understood as a learnable machinery for transporting, sampling, and interrogating high-dimensional probability measures. In this talk I will discuss how this viewpoint opens new routes for exploring QCD matter under extreme conditions, where both first-principles theory and event-by-event...
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Yong Sheng Koay (Uppsala University)25/08/2026, 11:30Foundation Models
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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Thanush Sivagnanalingam (University Heidelberg)25/08/2026, 11:40Foundation Models
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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Ethan Simpson (The University of Manchester (GB))25/08/2026, 11:50Patterns & Anomalies
In particle collider experiments, event reconstruction is the task of inferring the kinematics of the short-lived, 'interesting' particles of the hard scatter from the stable final states recorded by detectors. We decompose event reconstruction into two tasks: assigning measured jets and leptons to parent particles, and regressing unmeasured neutrino kinematics. We present a novel geometric...
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Timur Sypchenko (IPPP)25/08/2026, 11:50Simulations & Generative Models
Neural quantum states provide expressive variational representations of quantum many-body wavefunctions. 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...
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Timo Saala (University of Bonn (DE))25/08/2026, 11:50Inference & Uncertainty
In High Energy Physics, as in many other fields of science, the application of machine learning techniques has been crucial in advancing our understanding of fundamental phenomena. Increasingly, deep learning models are applied to analyze both simulated and experimental data. In most experiments, a rigorous regime of testing for physically motivated systematic uncertainties is in place. The...
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Nadezhda Dobreva25/08/2026, 12:00Patterns & Anomalies
Muon reconstruction in high-energy physics experiments relies on identifying detector hits originating from muons and fitting trajectories through them to extract track parameters. This process currently consists of separating signal hits from background before the track reconstruction can be performed. In this work, we present a deep learning solution for discriminating between background and...
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Iuliia Panteleeva25/08/2026, 12:00Simulations & Generative Models
Extracting hadronic form factors from sparse and noisy lattice QCD data typically relies on parametric ansätze, introducing model-dependence. We present a generative framework based on denoising diffusion models for parameterisation-independent reconstruction of these quantities. The generative prior is built from a large ensemble of synthetic curves drawn from distinct functional classes...
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Gabriele Pignalberi (Sapienza Università di Roma and INFN)25/08/2026, 12:00Inference & Uncertainty
Many scientific datasets are fundamentally incomplete: only a biased subset of true positives is ever observed, while the remainder stay unlabeled. This Positive-Unlabeled (PU) setting arises whenever detection efficiency is imperfect and covariate-dependent—a structure shared across many fields in fundamental science. In hadron collider experiments, hardware triggers record only a biased...
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Alexandra Wernersson25/08/2026, 12:10Explainability & Theory
Matched filter searches for gravitational waves from compact binary coalescences require template banks that densely cover the signal parameter space. Standard lattice based and stochastic placement methods both require a metric derived from a second order expansion of the waveform mismatch, which becomes difficult to construct and expensive to evaluate for signal spaces with strong curvature...
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Cesare Cazzaniga (DESY)25/08/2026, 12:10Inference & Uncertainty
Estimating the size and shape of backgrounds is a key part in the vast majority of HEP analyses. The ABCD method provides a reliable way to extract this information directly from the experimental data, thus avoiding reliance on simulation. It relies on the assumption that two, statistically independent variables can be constructed, in order to compute the expected background contribution in...
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Jan Pawlowski (Heidelberg university)25/08/2026, 12:10Simulations & Generative Models
The physics-informed renormalisation group describes general reparametrisation of given data distributions. It provides an analytic flow in terms of the distribution itself and the differential reparametrisation, the physics-informed kernel. From the perspective of quantum field theory, this flow can be understood as a general non-linear coarse graining procedure. From the perspective of...
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Thomas Vuillaume (LAPP, CNRS, USMB)25/08/2026, 12:20Patterns & Anomalies
Deep learning methods have emerged as a powerful alternative to reconstruct physical properties directly from images of atmospheric events acquired by Imaging Atmospheric Cherenkov Telescopes (IACTs). In the context of the Cherenkov Telescope Array Observatory (CTAO) first Large-Sized Telescope (LST-1), the specialized architecture gamma-PhysNet has demonstrated strong performances on...
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Renzo Kapust (University of Heidelberg)25/08/2026, 12:20Simulations & Generative Models
Computing observables with respect to highly oscillatory or complex-valued distributions is an important task in physics, in particular in quantum field theory. For complex distributions, however, traditional sampling algorithms either lose their straightforward applicability or are rendered inefficient by the oscillations, which cause a signal-to-noise problem that scales exponentially with...
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Jovin Drews (Universität Hamburg)25/08/2026, 12:20Patterns & Anomalies
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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Dario Hügel (Barra Labs)25/08/2026, 14:00
At barra, we implement agentic AI projects within complex, large-scale enterprise environments. The enterprise landscape introduces significant organisational complexity as well as rigid requirements for stability, scalability, and economic viability. Navigating these requirements demands a departure from experimental setups. This talk provides an overview of our operational learnings,...
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Nicole Hartman (TUM)25/08/2026, 14:45
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Adrian Oeftiger (Linacre College)25/08/2026, 16:00
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Eilam Gross (Weizmann Institute of Science)25/08/2026, 16:45
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Luigi DelDebbio (University of Edinburgh)26/08/2026, 09:00
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Maximilian Dax (ELLIS Institute Tübingen)26/08/2026, 09:45
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Eilam Gross26/08/2026, 11:00
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Tristan Bereau (University of Heidelberg)26/08/2026, 11:45
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Shreya Saha (Adelaide University)26/08/2026, 14:00Foundation Models
The integration of foundation models in particle physics is gaining pace rapidly and has expanded the search for new physics. This talk presents foundation models trained on low-level data from the first fully simulated dataset using Open Data Detector (ColliderML), to distinguish between Standard Model and Beyond Standard Model processes. We compare new physics discovery using only low level...
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Adrian Oeftiger (University of Oxford, John Adams Institute)26/08/2026, 14:00Simulations & Generative Models
This contribution reviews and explores approaches for physics-informed AI-based particle accelerator models. Digital Twin frameworks promise to be a key element for future operation of such highly complex systems like particle accelerators -- in particular when operational demands change continuously with a versatile accelerator user community and ever-varying beam production schemes. We...
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Valéria Carvalho (Universidade de Coimbra and Nicolaus copernicus astronomical center)26/08/2026, 14:00Simulations & Generative Models
Future multimessenger observations of neutron stars (NS) are expected to substantially increase both the number and precision of astrophysical constraints on the equation of state (EoS) of dense matter. This motivates inference frameworks capable of accommodating a variable, non fixed number of observations while preserving the posterior information associated with each measurement.
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In this... -
Miroslav Saur (Lanzhou University)26/08/2026, 14:00Real-time Data Processing
The LHCb experiment has deployed machine learning and artificial intelligence models in its real-time data processing from the start of Run 1 data taking, and by Run 3 such models are an integral part of the trigger.
This talk will describe the usage of machine learning and AI models and algorithms within the LHCb real-time analysis data processing paradigm as part of both reconstruction...
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Dunstan Becht (CEA)26/08/2026, 14:10Simulations & Generative Models
The growing need for accurate atomic and nuclear data is driven by applications such as spectroscopic analysis, astrophysical modeling, plasma diagnostics, and the identification of metastable states. These applications rely on large and reliable theoretical databases, which require extensive numerical calculations of quantum many-body systems.
In atomic physics, electrons must be described...
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Iuliia Panteleeva26/08/2026, 14:10Simulations & Generative Models
Neutron-star observations encode the equation of state of cold, dense matter, but reconstructing it is an ill-posed inverse problem, and standard analyses assume fixed functional forms that restrict and unevenly weight the admissible equations of state. We reconstruct the equation of state with a denoising diffusion model trained on a large synthetic ensemble of sound-speed profiles spanning...
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Guillaume Letellier (GREYC - University of Caen Normandy, France)26/08/2026, 14:10Foundation Models
Jet tagging at the Large Hadron Collider (LHC) increasingly relies on deep learning models trained on massive simulated datasets, leading to high computational costs and limited robustness to detector mismodeling. We introduce JetParticle-JEPA (JP-JEPA), a self-supervised Joint-Embedding Predictive Architecture that learns physically meaningful jet representations directly from continuous...
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Giulia Fazzino26/08/2026, 14:10Real-time Data Processing
The High-Luminosity LHC (HL-LHC) will deliver proton-proton collisions at unprecedented pile-up conditions, making fast and efficient track reconstruction a key computational challenge. Graph Neural Networks (GNNs) offer a promising approach to this problem.
A critical first step in GNN-based charged-particle tracking is graph construction, which can be carried out with the Metric Learning...
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Olga Soloveva (GSI Helmholtz Zentrum fuer Schwerionenforschung GmbH)26/08/2026, 14:20Real-time Data Processing
High-rate heavy-ion experiments require fast and robust event-selection strategies capable of identifying rare physics signatures in real time under severe throughput and storage constraints. We present a convolutional-neural-network-based trigger concept for the selection of events associated with quark–gluon plasma (QGP) formation. The method represents each collision event as a compact...
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Desheng Yang (Sapienza Università di Roma and INFN sezione di Roma)26/08/2026, 14:20Foundation Models
Foundation models have recently emerged as powerful tools for learning general-purpose representations that can outperform task-specific models while being generalizable to multiple downstream tasks. In this project, we explore their application to particle physics by training a foundation model on simulated jet data from hadronic collisions. The goal is to build a model that can learn...
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Zewei Xiong (GSI, Darmstadt)26/08/2026, 14:20Simulations & Generative Models
Neutron-rich outflows in neutron-star mergers (NSMs) or other explosive events can be subject to substantial heating through the release of rest-mass energy in the course of the rapid neutron-capture (r-) process. This r-process heating can potentially have a significant impact on the dynamics determining the velocity distribution of the ejecta, but due to the complexity of detailed nuclear...
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Husain Mustansir Manasawala (Universität Heidelberg)26/08/2026, 14:20Inference & Uncertainty
Precision tests of fundamental symmetries frequently rely on multi-stage experimental setups where distinct physical mechanisms shape a common observable via an intractable likelihood. Conditional invertible neural networks, trained on high-fidelity forward simulations, learn to reconstruct the full multidimensional posteriors over the parameter space directly from detector-level observables....
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Ariadna Uxue Palomino Ylla (Nagoya University)26/08/2026, 14:30Simulations & Generative Models
We investigate whether matter-induced deviations from a vacuum black hole spacetime can be constrained through redshift observations of orbiting stars. Building on our previous study of redshift signatures in fluid-perturbed black hole backgrounds, we consider the problem of efficiently connecting model parameters to observable redshift curves in a setting relevant for data analysis. Since...
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Luca Fallböhmer (Max-Planck-Institute for Nuclear Physics)26/08/2026, 14:30Simulations & Generative Models
Following the completion of its neutrino mass measurement program at the end of 2025, the KATRIN experiment aims to probe keV-scale sterile neutrinos by analyzing the full tritium beta decay spectrum with a novel detector system, TRISTAN. Leveraging KATRIN’s high source activity, this search is sensitive to mixing amplitudes at the parts-per-million level. However, extracting a potential...
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Manjunath Omana Kuttan (Albert-Ludwigs-Universität Freiburg)26/08/2026, 14:40Real-time Data Processing
The LHCb experiment at CERN's Large Hadron Collider (LHC) relies on precise alignment of its tracking detectors comprising the Vertex Locator (VELO), Upstream Tracker (UT), and Scintillating Fibre (SciFi) Tracker to achieve the momentum, mass, and vertex resolutions demanded by the LHCb physics programme. During data-taking, detector alignment at LHCb is performed on CPUs using an iterative χ²...
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Una Alberti (University of Bern)26/08/2026, 14:40Foundation Models
The high-energy physics pipeline already resembles a multi-modal foundation model: a general-purpose representation built once and reused across downstream tasks - but it lacks scale and differentiability. Already, we are seeing the gains from large-scale pre-training in individual pieces of the ATLAS pipeline, such as jet flavour identification. Flavour taggers, however, are normally trained...
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Dily Duan Yi Ong (University of Cambridge)26/08/2026, 14:50Inference & Uncertainty
The era of precision cosmology has revealed persistent tensions between independent measurements of fundamental parameters within the concordance model, most notably the Hubble constant ($H_0$), the clustering amplitude ($\sigma_8$), and spatial curvature ($\Omega_K$). The study of these discrepancies between independent datasets, which are theoretically predicted to agree, is known as tension...
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Divesh Jain (Heidelberg University)26/08/2026, 14:50Simulations & Generative Models
The Epoch of Reionization marks the period when the first galaxies ionized the neutral hydrogen in the intergalactic medium. Upcoming 21 cm experiment such as SKA, together with line-intensity mapping and galaxy surveys, will observe overlapping cosmic volumes. Much of their constraining power will come from cross-correlating different tracers, since each observable is affected by different...
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Urs Moritz Fischer26/08/2026, 14:50Real-time Data Processing
Machine learning techniques have demonstrated great potential for efficiently addressing the increasing combinatorial complexity of track finding at high-luminosity collider experiments. To achieve high computational efficiency, neural network models often require compression, a necessity that becomes increasingly important for the large models used in GNN-based tracking pipelines. This is...
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Giovanni Ottaviano (Sorbonne University)26/08/2026, 14:50Foundation Models
Tokenization plays a central role in modern foundation models, especially those relying on next-token prediction, and has been extensively studied in domains such as language and vision. In high-energy physics, however, the adaptation of tokenization to continuous jet data from collider experiments remains at an early stage, with few studies on tokenizer performance, comparable evaluation...
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Thomas Blankenburg26/08/2026, 15:00Simulations & Generative Models
Observations of the large-scale structure, such as intensity maps of the 21cm line of neutral hydrogen, raise the question whether a precise, robust, and efficient mapping can be established from these observables to the underlying dark matter density field. We present a direct diffusion framework based on Conditional Flow Matching that enables accurate reconstruction of dark matter density...
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Óscar Marcos Pérez Cytron (Ruhr-Universität Bochum, GSI Helmholtzzentrum für Schwerionenforschung)26/08/2026, 15:00Real-time Data Processing
Maintaining data quality in real-time systems remains a fundamental requirement in nuclear and particle physics experiments. At the HADES collaboration, the Quality Assessment (QA) system generates plots that visualize the real-time performance of detector subsystems. Currently this requires that operators manually inspect these visualizations to identify potential anomalies.
This...
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Ik Siong Heng (University of Glasgow)26/08/2026, 15:00Inference & Uncertainty
Kilonovae are the optical counterparts to gravitational wave signals from binary neutron stars. Combining gravitational wave and kilonova observations provide insight into the equation of state of neutron stars. Kilonova detection is challenging and only a few kilonova candidates have been detected to date, with AT2017gfo being the only one associated with a gravitational wave event GW170817....
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Henning Rose (Uni Hamburg)26/08/2026, 15:00Foundation Models
Autoregressive transformers are increasingly applied outside the language domain that motivated them, but the tokenization step transfers poorly. Scientific data are already numerical, often partly discrete, and typically live in high-dimensional spaces where each token carries several features. The standard workaround, compressing feature vectors into a single token via a learned codebook,...
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Haris Kapetanovic26/08/2026, 15:10Simulations & Generative Models
Fourier Neural Operators (FNOs) and related architectures have emerged as promising surrogates for physical systems, with notable success in fluid dynamics, where they achieve high accuracy at a small fraction of the cost of conventional solvers. Reionization cosmology is a demanding test of that promise: the neutral fraction field in 21cm simulations is near-binary, dominated by sharp...
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Herzallah Alharazin (Ruhr Universität Bochum)26/08/2026, 15:10Inference & Uncertainty
Extracting continuous physical quantities from sparse and noisy data is a ubiquitous inverse problem across theoretical physics. In this talk, I will present a physics-guided generative framework based on denoising diffusion probabilistic models that reformulates function reconstruction as a conditional generation (inpainting) problem. Rather than relying on a prescribed functional ansatz, the...
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Waleed Esmail26/08/2026, 15:20Simulations & Generative Models
Fast waveform surrogates are essential for gravitational-wave inference, but neural surrogates typically lack uncertainty estimates and offer little guidance on how accuracy scales with training resources. We present an autoregressive flow-matching surrogate for binary-black-hole waveforms that operates on amplitude-phase representations and samples each successive segment with a conditional...
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Gianluca Inguglia (MBI Vienna), Huw Haigh (MBI Vienna), Ulyana Dupletsa (MBI Vienna)26/08/2026, 16:00Patterns & Anomalies
We present an anomaly detection algorithm based on a deep convolutional autoencoder. The algorithm, initially developed with a focus on the Einstein Telescope, was adjusted to the higher noise levels of the LIGO detectors, implementing coherence, trained and validated using O3 data, and tested on publicly available O4 data. We achieve an excellent recovery rate for short, loud signals and...
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Pau Solé Vilaró (ICC - Universitat de Barcelona)26/08/2026, 16:00Explainability & Theory
We investigate the reconstruction of holographic duals for strongly coupled quantum field theories in regimes characterized by large hierarchies and the presence of false vacua. Within the gauge/gravity duality, these features translate into non-trivial thermodynamic behaviour and exotic renormalization group flows, including skipping flows between non-adjacent fixed points. Building on...
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Vitus Past (Technische Universität München)26/08/2026, 16:00Inference & Uncertainty
Simulation-based inference (SBI) has become a key tool for hypothesis testing in high-energy particle physics, enabling likelihood ratio estimation directly from simulated data without requiring tractable likelihoods. In practice, SBI is often applied to high-level variables reconstructed from detector measurements as point estimates, discarding hypothesis-dependent information contained in...
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Luis Felipe Cattelan (University of Zurich)26/08/2026, 16:00Hardware & Design
The SHiP experiment is a proposed fixed-target experiment at the CERN SPS designed to search for feebly interacting particles beyond the Standard Model. A critical challenge for SHiP is suppressing the massive flux of beam-dump muons entering the detector acceptance. The Active Muon Shield, a system of magnets designed for muon deflection, must therefore be optimized to minimize this...
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Prof. Elena Cuoco (Unversity of Bologna)26/08/2026, 16:10Patterns & Anomalies
All gravitational wave detections thus far have been from binary compact object coalescences. These detections rely on analyses that exploit the well-modelled nature of the sources. Generic-transient (burst) gravitational waves, on the other hand, do not have well-modelled gravitational wave signals. Therefore, searches for burst gravitational waves must efficiently scan a broad parameter...
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Sara Zoccheddu26/08/2026, 16:10Hardware & Design
Instrument design requires searching a large space of discrete and continuous choices under hard resource constraints, and the effort of setting up such optimizations increasingly limits how far they can scale. We investigate the use of large language models (LLMs) for physics instrument design and compare their performance with reinforcement learning (RL). Using only prompting, the models...
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Andreas Hermansen (Universite de Geneve)26/08/2026, 16:10Inference & Uncertainty
In high-energy collider physics, the reconstruction of complicated event topologies, such as fully hadronic $t\bar{t}$ decays, is a difficult challenge for many analyses due to the large combinatorial backgrounds. While previous machine learning methods have advanced this task, they struggle to learn the exact joint distribution, and instead learn various independent marginal approximations....
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Friederike Ihssen (ITP Heidelberg)26/08/2026, 16:10Explainability & Theory
The renormalisation group considers general coarse-graining transformations in lattice systems and more broadly speaking field theories or data spaces and is based on an analytically known optimal transport equation.
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This framework has a natural correspondence with modern generative architectures in terms of pooling and convolutions, but differs in terms of the non-linearity with corresponds... -
Edoardo Murano (Sapienza Università di Roma and INFN)26/08/2026, 16:20Explainability & Theory
Partitioning a graph into communities is an NP-hard optimization problem with a natural statistical-physics formulation: the optimal partition is the ground state of an antiferromagnetic Potts model, with modularity playing the role of negative energy. Clustering on graph-structured data is a recurring task in fundamental physics, where detector and event data are naturally represented as...
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Nathan Campioni (Sapienza Università di Roma and INFN)26/08/2026, 16:20Hardware & Design
QSOpt (Quantum Sensing Optimization) is an end-to-end differentiable simulation and machine-learning optimization framework for open quantum networks composed of superconducting qubits, bosonic modes and input-output channels with user-defined interactions.
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The advancements in quantum technologies have sparked interest in employing quantum systems as sensors, with superconducting quantum... -
Hugo EInsle (UAntwerpen)26/08/2026, 16:20Patterns & Anomalies
Detecting a stochastic gravitational-wave background (GWB), the incoherent superposition of gravitational waves from many faint, unresolved sources, and separating its components are among the main targets of the LIGO--Virgo--KAGRA (LVK) network of ground-based gravitational-wave interferometers. However, both objectives remain challenging for the cross-correlation analyses currently in use,...
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Dr Evangelia Drakopoulou (NCSR "Demokritos")26/08/2026, 16:20Inference & Uncertainty
Accurate energy reconstruction is a key challenge in neutrino telescopes, where the total neutrino energy is not fully contained in the detector and relying only on the reconstructed muon energy neglects the contribution from hadronic showers. We present a Graph Neural Network (GNN)-based approach for visible energy reconstruction in KM3NeT/ARCA using the DynEdge architecture within the...
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Tae-Geun Kim (Fudan U. / RIKEN)26/08/2026, 16:30Explainability & Theory
An autoregressive neural sampler writes a lattice configuration site by site, each site conditioned on the sites already written, and it returns independent samples with an exact likelihood. Each conditional sees only a context window of fixed reach, and nothing in the architecture says how far that reach has to extend.
Set the reach one unit short of what the target needs and the interior...
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Tom Dooney (Nikhef / Utrecht University)26/08/2026, 16:30Patterns & Anomalies
Gravitational-wave detectors such as LIGO, Virgo, and KAGRA, detect faint signals from distant astrophysical events. However, their high sensitivity also exposes them to noise artifacts — including unmodelled transients known as "glitches" — that can mimic or mask genuine signals. Last year we introduced DeepExtractor, a deep learning framework that reconstructs arbitrary signals or glitches...
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Antonin Vacheret (CNRS - LPC Caen)26/08/2026, 16:30Inference & Uncertainty
Neutrino oscillations encode fundamental information about neutrino masses and mixing parameters, offering a unique window into physics beyond the Standard Model. Estimating these parameters from oscillation probability maps is, however, computationally challenging due to the maps’ high dimensionality and nonlinear dependence on the underlying physics. Traditional inference methods, such as...
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Elli Jobst (Technical University of Munich, Max Planck Institute for Physics)26/08/2026, 16:40Hardware & Design
The past decade has witnessed a rapid expansion of machine learning applications in very-high-energy (VHE) gamma-ray astronomy. While many efforts have focused on Convolutional Neural Networks (CNNs), these approaches remain constrained by existing camera geometries and by the limited ability of Monte Carlo simulations to fully capture real telescope performance. In this work, we employ a...
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Sven Põder (SISSA)26/08/2026, 16:50Patterns & Anomalies
Detecting faint stellar substructure in the Milky Way halo and its surroundings favours methods that remain sensitive to weak signals without restrictive assumptions about either the signal morphology or the Galactic background. We present recent applications of EagleEye, a model-independent anomaly detection framework that compares multidimensional data distributions to identify localized...
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Gábor Bíró (HUN-REN Wigner RCP)26/08/2026, 16:50Inference & Uncertainty
Proton computed tomography (pCT) promises a direct, low-dose measurement of the relative stopping power (RSP) map that governs proton-therapy dose calculations. In a tracking-based scanner every proton is recorded individually, and reconstructing the RSP image requires estimating the path each proton actually took through the patient. This is intrinsically hard: multiple Coulomb scattering...
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Ms Aleksandra Stojanović (School of Electrical Engineering, University of Belgrade), Mr Dimitrije Pešić (School of Electrical Engineering, University of Belgrade), Mr Janko Vukobratović (School of Electrical Engineering, University of Belgrade)26/08/2026, 16:50Patterns & Anomalies
Macro-X-ray fluorescence (MA-XRF) scanners with two detectors are used, with their signals normally summed to improve the signal-to-noise ratio and the difference between them neglected. We show that this difference, plus one extra measurement, scanning the same painting again with the canvas tilted forward, is enough to characterise the instrument, with no dedicated calibration measurements...
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Aritra Bal (Karlsruhe Institute of Technology (KIT))26/08/2026, 16:50Explainability & Theory
Large machine learning models benefit substantially from multimodal inputs that provide a complementary view of the same example. We introduce <span style="font-variant: small-caps;">Quiver</span> (QUantum-Informed Views for Enhanced Representations), a paradigm that enriches classical data-driven features...
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Jesus Pena-Rodriguez (JLU Giessen)26/08/2026, 17:00Hardware & Design
SiPMs (Silicon Photomultipliers) have recently been studied as candidates for building photon cameras in RICH (Ring Imaging Cherenkov) detectors. SiPM-based cameras improve detection efficiency (up to $60\%$), spatial resolution (mm), timing ($< 100$\, ps), scalability, and magnetic field immunity. Nevertheless, for single-photon detection, SiPM thermal noise ($\sim 10^2$\,kHz/mm$^2$) and...
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Lorenzo Colantonio (Sapienza Università di Roma and INFN)26/08/2026, 17:00Explainability & Theory
In recent years, approaches inspired by fundamental physics have played an important role in the development of modern machine learning algorithms, from energy based models to diffusion processes. Motivated by this perspective, we propose a hybrid quantum-classical learning framework inspired by adiabatic quantum dynamics and quantum annealing for solving constraint satisfaction problems.In...
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Mr Said Abolhassan Razavi26/08/2026, 17:00Inference & Uncertainty
Gradient-boosted decision trees such as XGBoost remain the default for tabular high-energy-physics (HEP) classification. We investigate Kolmogorov-Arnold Networks (KAN), which replace fixed activations with learnable B-spline functions on network edges, as an alternative for Higgs boson signal/background separation. We implement KAN from scratch in PyTorch (Cox-de Boor B-splines, adaptive knot...
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Sanja Dumenčić (University of Nova Gorica)26/08/2026, 17:00Patterns & Anomalies
The Fermi Large Area Telescope (Fermi-LAT), operating since August 2008, has recently released an updated catalog of gamma(γ)-ray sources containing over 7000 γ-ray sources based on 14 years of observations. One of the biggest challenges in analyzing the γ-ray sky is the uncertainty of the interstellar emission model (IEM). This diffuse background significantly complicates the detection and...
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Gioacchino Alex Anastasi (Università di Catania & INFN Catania)26/08/2026, 17:10Patterns & Anomalies
In this contribution, we propose a data-driven approach based on unsupervised learning, specifically using deep Convolutional AutoEncoders (CAE), to identify signals from low-energy nuclear recoils in the dual-phase Liquid Argon Time Projection Chamber (LAr TPC) of the Recoil Directionality (ReD) experiment. This is a challenging task for recoil energies $\sim$1 keV, since scintillation...
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Federica Di Bartolomeo (Sapienza Università di Roma)26/08/2026, 17:10Inference & Uncertainty
We present a hybrid graph-based Bayesian framework for uncertainty-aware inference of rare events on spatially embedded networks.
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Starting from a Multivariate Conditional Autoregressive Model (MCAR) in a hierarchical Bayesian architecture defined on the dual graph of a street network, events counts are described by Poisson likelihoods with structured graph effects, unstructured heterogeneity... -
Ema Puljak (CERN)26/08/2026, 17:10Explainability & Theory
Tensor Networks have emerged as a prominent alternative to neural networks for addressing machine learning challenges in foundational sciences, offering a bridge between quantum information techniques and classical learning algorithms. This transfer of methods from quantum information to machine learning has the potential to enhance model interpretability and efficiency, paving the way for...
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Vishal Ngairangbam (Karlsruhe Institute of Technology)26/08/2026, 17:20Explainability & Theory
Classical neural networks have rich feature learning capabilities where each functional composition has the ability to transform the hidden representations into non-isometric geometries. In quantum neural networks, however, depth or state reachability alone does not guarantee this feature-learning capability. We study this question in the pure-state setting by viewing encoded data as embedded...
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Bralyne Vanessa Kamga Matoukam (University of The Witwatersrand)26/08/2026, 17:20Patterns & Anomalies
The ATLAS experiment at the CERN Large Hadron Collider (LHC) records and processes vast amounts of data from proton-proton collisions. With the High-Luminosity LHC (HL-LHC), the expected increase in data volume by more than an order of magnitude will place unprecedented demands on storage, data throughput, and analysis. In this contribution, we will start with the comparison of two novel...
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Emille Ishida (Clermont Auvergne)27/08/2026, 09:00
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Johann Brehmer (CuspAI)27/08/2026, 09:45
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Theo Heimel (UCLouvain)27/08/2026, 11:00Simulations & Generative Models
We present a fully automated implementation of neural multi-channel importance sampling with MadNIS for leading-order LHC event generation in MadGraph. The method selects its training and network hyperparameters from the requested process and target event count, runs training data generation and network optimization concurrently to enable efficient GPU execution, and learns the flavor...
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Alexander Froch (Université de Genève)27/08/2026, 11:00Patterns & Anomalies
Identifying the flavour of hadronic jets is a cornerstone of the ATLAS physics programme, underpinning measurements of the Higgs boson and top quark as well as searches for di-Higgs production and physics beyond the Standard Model. We present the latest generation of ATLAS flavour-tagging algorithms, which have evolved from hybrid multi-stage approaches into unified transformer architectures...
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Merve Nazlim Agaras (Brookhaven National Laboratory (BNL))27/08/2026, 11:00Datasets & Ethics
The Tokenized Representations for Energy-frontier AI Searches via Understanding and Reasoning (TREASURE) project aims to make high-energy physics collider data usable by modern AI methods through standardized tokenized representations. Collider experiments produce rich datasets in different, experiment-specific formats, which limits cross-experiment analysis and the reuse of legacy data....
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Ayodele Ore (ITP, Heidelberg University)27/08/2026, 11:00Inference & Uncertainty
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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Caterina Doglioni (University of Manchester)27/08/2026, 11:10Datasets & Ethics
The petabyte-scale data generated by High Energy Physics (HEP) experiments presents a significant storage challenge. We present the Bytewise Online Autoregressive (BOA) Constrictor, a new pseudo-streaming lossless neural compressor built upon the Mamba state space model. BOA achieves competitive compression ratios across diverse structured HEP datasets, matching or exceeding LZMA, ZSTD and...
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Susie Kim (ITP, Heidelberg University)27/08/2026, 11:10Inference & Uncertainty
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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Sebastian Pitz (LPNHE Paris)27/08/2026, 11:10Patterns & Anomalies
Lorentz-equivariant neural networks are becoming the leading architectures for high-energy physics. Current implementations rely on specialized layers, limiting architectural choices. We introduce Lorentz Local Canonicalization (LLoCa), a general framework that renders any backbone network exactly Lorentz-equivariant. Using equivariantly predicted local reference frames, we construct...
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Giovanni De Crescenzo27/08/2026, 11:10Simulations & Generative Models
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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Gianluca Inguglia (MBI Vienna), Dr Tomislav Andric (GSSI)27/08/2026, 11:20Datasets & Ethics
The Einstein Telescope (ET) Collaboration, the European third-generation flagship experiment for gravitational wave searchs, has recently launched a new cross-board division, AI-for-ET, with the aim to explore challenges and opportunities for ET related to the adoption and implementation of AI workflows into the activities of the collaboration. In this talk we will povide an overview of the...
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Richard Fuchs (TUM)27/08/2026, 11:20Inference & Uncertainty
(Sub-)millimeter single-dish telescopes observe larger spatial scales and feature faster mapping speeds than radio interferometers. However, their measured signals are dominated by atmospheric fluctuations and instrumental noise, making it difficult to recover the true astronomical sky. We introduce maria-nifty, a Gaussian process-based framework for reconstructing sky maps from single-dish...
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Sophia Vent (Heidelberg University)27/08/2026, 11:20Simulations & Generative Models
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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Huilin Qu (TDLI)27/08/2026, 11:20Patterns & Anomalies
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 all 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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Daohan Wang (Marietta Blau Institute for Particle Physics, Austrian Academy of Sciences)27/08/2026, 11:30Simulations & Generative Models
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 controlled from Python that integrates normalizing-flow proposals with the parton-level event generator \Pepper. Helicity-conditioned coupling flows are trained...
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Arantza Oyanguren (IFIC - CSIC/UV)27/08/2026, 11:30Datasets & Ethics
Artificial intelligence, heterogeneous computing, and scientific software are rapidly transforming the way research is conducted. To address these challenges in a coordinated manner, the Spanish Network on Advanced Computing for Fundamental and Applied Physics (COMCHA) was established approximately a decade ago. Since then, the network has evolved from a collaborative initiative of LHC groups...
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Fracesco Passante (Sapienza Università di Roma and INFN)27/08/2026, 11:30Patterns & Anomalies
Gauge equivariant convolutional neural networks have shown that enforcing exact lattice gauge symmetry in a neural network significantly improves regression accuracy on gauge invariant observables. However, their convolutional kernels stay fixed after training and cannot adapt to configuration-dependent features. We introduce GELT (Gauge Equivariant Lattice Transformer), a gauge equivariant...
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Martin Boerstad Eriksen (IFAE-PIC)27/08/2026, 11:30Inference & Uncertainty
Deep photometric surveys increasingly rely on low signal-to-noise imaging to detect and characterise faint galaxies, where noise affects source detection, flux measurements, and redshift estimation. Self-supervised denoising methods are attractive because they do not require ground-truth images, but their application to quantitative photometry demands control over systematic flux biases.
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We... -
Dr Klecio Lima (Universidade Federal de Campina Grande)27/08/2026, 11:50Inference & Uncertainty
Fast radio bursts (FRBs) provide a promising tool to investigate the baryonic content of the Universe through their dispersion measures. In this work, we constrain the baryon density parameter $\Omega_b h^2$ using a sample of 130 localized FRBs combined with a non-parametric reconstruction of the Hubble parameter $H(z)$ obtained from cosmic chronometer data through the ReFANN neural-network...
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Rebecca Revelli (Heidelberg University)27/08/2026, 11:50Simulations & Generative Models
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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Tatsuyuki Sekine (University of Hamburg)27/08/2026, 11:50Patterns & Anomalies
Within the first billion years of cosmic time, galaxies and supermassive black holes (SMBHs) had already formed. Especially, rapidly growing billion solar mass black holes - shining as quasars - discovered at z > 7 challenge standard models of SMBH growth. To constrain their formation, expanding the quasar redshift frontier is critical.
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The deep, wide-area photometry of the Euclid mission,... -
Javier Mariño Villadamigo (University of Heidelberg)27/08/2026, 12:00Simulations & Generative Models
High-multiplicity events remain a bottleneck for LHC simulations due to their computational cost. We present a ML-surrogate approach to accelerate matrix element reweighting from leading-color (LC) to full-color (FC) accuracy, building on recent advancements in LC event generation. Comparing a variety of modern network architectures for rep-
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resentative QCD processes, we achieve speed-up of... -
Marija Čuić (Irfu, CEA, Université Paris-Saclay/Aidas)27/08/2026, 12:00Inference & Uncertainty
Generalized parton distributions (GPDs) encode the three-dimensional partonic structure of the nucleon and are inferred from exclusive scattering experiments such as deeply virtual Compton scattering. The reconstruction is an underdetermined inverse problem: the measurements — Compton form factors — constrain only one region of the underlying distribution, leaving an unmeasured complementary...
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Ranit Das (Heidelberg University)27/08/2026, 12:00Patterns & Anomalies
Beyond the practical goal of improving search and measurement sensitivity through better jet tagging algorithms, there is a deeper question: what are their upper performance limits? Generative surrogate models with learned likelihood functions offer a new approach to this problem, provided the surrogate correctly captures the underlying data distribution. In this work, we introduce the...
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Gergely Gábor Barnaföldi (HUN-REN Wigner RCP)27/08/2026, 12:10Simulations & Generative Models
Hadronization is a non-perturbative process, which theoretical description can not be deduced from first principles. Modeling hadron formation requires several assumptions and various phenomenological approaches. Utilizing state-of-the-art Deep Learning algorithms, it is eventually possible to train neural networks to learn non-linear and non-perturbative features of the physical processes. In...
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Johann Ioannou-Nikolaides (Niels Bohr Institute), Raphaël Bonnet-Guerrini (Computer Science Dep. University of Milan)27/08/2026, 12:10Patterns & Anomalies
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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Eva Groenendijk (University of Milan and INFN Milan)27/08/2026, 12:10Inference & Uncertainty
Parton distribution functions (PDFs) are a key ingredient for precision predictions at particle colliders, describing the dynamics of quarks and gluons inside the proton. Their determination is a challenging inverse problem, where PDFs are extracted from data via a convolution with theoretical predictions. The NNPDF methodology uses a neural network as a flexible parameterisation and a Monte...
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Mane Papoyan (American University of Armenia (AUA))27/08/2026, 12:20Patterns & Anomalies
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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Yannic Pietschke (Institute for Theoretical Physics, Heidelberg)27/08/2026, 14:00Foundation Models
Simulation-based inference (SBI) has become the default for intractable-likelihood problems across fundamental physics, but is limited by model misspecification: a density estimator trained on one simulator becomes inaccurate or miscalibrated when applied to data drawn from a different forward model or with unseen instrumental systematics. We study this in 21cm cosmology, where the...
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Sergio Arguedas Cuendis (CONARE)27/08/2026, 14:00Agentic AI
The LHCb collaboration at CERN spans from individual physics analyses to production computing. That breadth makes it a natural place for the agentic AI. The software stack grew over decades around human experts. The physics is split into internal working groups, which standard LLMs were not trained to distinguish. This HEP-specific workflow cannot be integrated with commercial tools without...
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Luca Wolf27/08/2026, 14:00Explainability & Theory
Neural ODEs enable the automatic discovery of a system's ordinary differential equations (ODEs) using only trajectory measurements. We combine this powerful and widely used machine learning method with the principle of stationary action from theoretical physics to learn only ODEs that are admissible as fundamental physical laws. To this end, we develop Helmholtz metrics, a machine learning...
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Sebastian Dittmeier (PI)27/08/2026, 14:00Real-time Data Processing
Machine learning is playing an increasingly important role in particle physics, from offline data analysis to real-time event selection at the Large Hadron Collider. The COST Action EPIGRAPHY (Edge deeP learnIng foR pArticle PHYsics) brings together researchers across Europe to advance efficient deep learning methods for resource-constrained, low-latency computing environments. This...
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Antoine Petitjean (ITP, Heidelberg University)27/08/2026, 14:10Real-time Data Processing
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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Abdullah Umut Hamzaogullari (Bogazici University)27/08/2026, 14:10Explainability & Theory
Lagrangian Neural Networks (LNNs) can learn arbitrary Lagrangians from trajectory data, but their unusual optimization objective leads to significant training instabilities that limit their application to complex systems. We propose several improvements that address these fundamental challenges, namely, a Hessian regularization scheme that penalizes unphysical signatures in the Lagrangian’s...
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Daniel Schiller (Institute for Theoretical Physics, Heidelberg University)27/08/2026, 14:10Agentic AI
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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Johann Ioannou-Nikolaides (Niels Bohr Institute), Raphaël Bonnet-Guerrini (Computer Science Dep. University of Milan)27/08/2026, 14:10Foundation Models
Pretrained foundation models are emerging in neutrino telescopes, but what their internal representations actually contain, and whether the tasks built on top of them put it to use, is largely unknown. We study PolarBERT, a transformer pretrained on IceCube data and fine-tuned for direction reconstruction. Sparse autoencoders uncover a validated atlas of physics across the frozen backbone,...
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Nikita Schmal (ITP, Heidelberg University)27/08/2026, 14:20Agentic AI
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 through an agentic workflow with a physicist in the loop. Although demonstrated within the SFitter framework, the underlying technical aspects of the agentic...
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Sanjiban Sengupta (CERN, University of Manchester)27/08/2026, 14:20Real-time Data Processing
SOFIE, or the System for Optimized Fast Inference code Emit, is a tool developed by the ML4EP team at CERN that translates trained machine learning models into self-contained, low-latency C++ code. The generated code is portable, hardware-agnostic, and highly optimized while depending only on BLAS libraries.
Code generated by SOFIE achieves portability across different hardware...
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Sena Kalabalik27/08/2026, 14:20Explainability & Theory
Lagrangian Neural Networks (LNNs) learn mechanical systems directly from trajectory data by parameterizing a scalar Lagrangian and deriving the dynamics through the Euler-Lagrange equations, so that the learned model is strongly biased toward motion that respects the variational structure of mechanics. The interactions studied in this way have so far been pairwise-additive, leaving open...
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João A. Gonçalves (U. Bonn, B-it, Lamarr Institute)27/08/2026, 14:20Foundation Models
Jet quenching modifies the energy and substructure of jets in heavy-ion collisions, but the corresponding inverse problem remains largely unexplored at the constituent level: given a medium-modified jet, infer the vacuum jet associated with the same parton shower. We formulate this low-level inverse jet-quenching task using paired HYBRID simulations, in which each quenched shower is obtained...
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Andrew Pilkington (University of Manchester)27/08/2026, 14:30Agentic AI
Particle physics collider experiments provide Rivet routines as part of the analysis preservation strategy for model-independent measurements. Rivet is a C++ toolkit that allow new theoretical models to be compared to the measurements, thus aiding the development and tuning of Monte Carlo event generators as well as searches for physics beyond the Standard Model. However, analysis coverage is...
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Thomas Gessey-Jones (PhysicsX)27/08/2026, 14:30Foundation Models
Inferring a three-dimensional shape from sparse, indirect measurements is a severely ill-posed inverse problem, which arises throughout the physical and biological sciences. Doing so successfully with principled Bayesian uncertainties requires a tractable parametrisation of geometry, and a strong prior encoding domain knowledge of plausible shapes.
In this talk we show how a pretrained...
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Mr Sukru Caglar (Bogazici University)27/08/2026, 14:30Explainability & Theory
Lagrangian Neural Networks (LNNs) learn dynamics from trajectory data by parameterizing a scalar Lagrangian and deriving accelerations through the Euler–Lagrange equation, providing a variational inductive bias that improves physical consistency over purely data-driven models. LNNs have recently been extended to general relativistic settings, leaving Schwarzschild geodesics as an open...
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Philipp Niedermayer (GSI GmbH)27/08/2026, 14:40Foundation Models
Large-scale accelerator facilities such as GSI/FAIR rely heavily on distributed expertise and fragmented documentation for daily operations, creating persistent challenges in knowledge retention, troubleshooting speed, and workforce continuity. To address these operational bottlenecks, we present an AI assistant designed to deliver context aware, expert level guidance to shift operators....
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Erik Bründermann (Karlsruhe Institute of Technology (KIT))27/08/2026, 14:40Real-time Data Processing
The KIT accelerator team has contributed significantly to the state-of-the-art of accelerator control especially in controlling electron beams near real-time in storage rings and synchrotrons. Adapted to the latency of components like magnets, slow control optimizes beam transfer and injection into a storage ring [1]. Fast control by online reinforcement learning with AI-on-hardware...
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Alexander Kazatsky (Ruhr University Bochum)27/08/2026, 14:50Agentic AI
Natural science increasingly relies on complex models embedded in large computational frameworks. Motivated by the DEMOS (DEmocratizing MOdelS) project's goal of making research models interoperable and encoding them into framework-independent formats, we address a necessary prerequisite: reconstructing an analysis-specific model and translating it between frameworks without changing its...
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Abdullah Umut Hamzaogullari (Bogazici University)27/08/2026, 14:50Explainability & Theory
Lagrangian Neural Networks (LNNs) learn dynamical laws from trajectory data by representing a system’s Lagrangian with a neural network. In their standard formulation, however, LNNs are typically applied using carefully chosen generalized coordinates, such as angular coordinates for a pendulum, which already encode the system’s constraints and true degrees of freedom. Existing approaches...
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James Alvey (University of Cambridge)27/08/2026, 14:50Real-time Data Processing
In this talk I will present a specialised GPU-native nested sampling kernel targeting rapid parameter estimation for gravitational wave inference problems. Building upon a Slice-within-Gibbs (SwiG) structure for rapid mixing, we investigated how far we can push baseline stochastic sampling techniques on modern GPU hardware. I will show that for typical long-duration binary neutron star signals...
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Prof. M. B. Cruz (State University of Paraiba)27/08/2026, 15:00Explainability & Theory
The detection of gravitational waves has turned the study of quasinormal modes (QNMs) into a fundamental tool for testing General Relativity. However, traditional numerical methods often face stability challenges when dealing with massive fields or high-order overtones. In this work, we present a robust computational framework based on Physics-Informed Neural Networks (PINNs) to solve the...
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Aleksei Mikhasenko (Universität Bonn)27/08/2026, 15:00Agentic AI
Across many fields of fundamental physics, researchers must reason over large and growing corpora of specialized publications and increasingly turn to language models for help — yet these models rarely connect their answers to the primary sources they draw on and often lack the information to give a reliable answer. Building on the PathRAG graph-retrieval framework, we adapt it to...
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Dr Xue-Ting Zhang (Max Planck Institute for Gravitational Physics (Albert Einstein Institute))27/08/2026, 15:00Real-time Data Processing
The space-borne gravitational-wave (GW) detectors will open a new mass and redshift regime, allowing us to observe massive black hole binaries (MBHBs) throughout the Universe. A subset of these systems is expected to produce electromagnetic (EM) counterparts, offering a unique opportunity to follow the continuous evolution of massive black holes through joint GW and EM observations. Realizing...
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Gianluca Inguglia (MBI Vienna)27/08/2026, 15:10Agentic AI
We present a first head-to-head comparison of different agentic AI systems applied to the study of gravitational waves. Agentic systems, supervised either via a human-in-the-loop or in fully autonomous mode, are tasked with performing matched-filter analyses in the context of the Einstein Telescope mock data challenge and with fully writing a scientific paper in the style of PRD. While all...
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Xisco Jimenez Forteza (Universitat de les Illes Balears)27/08/2026, 15:10Explainability & Theory
Machine learning techniques are increasingly being explored as complementary tools for solving complex problems in gravitational physics. In this talk, I will present recent work on the application of neural-network-based methods to black hole dynamics across different regimes. On the one hand, perturbative models provide an ideal framework for investigating the spectral properties of black...
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Jonas Spinner (Durham University)27/08/2026, 15:20Explainability & Theory
The dynamics of particles in the early universe are described by Boltzmann equations, which involve high-dimensional phase space integrals. Classical approaches use quadrature integration and evolve the system on a fixed momentum grid, which scales poorly to high-dimensional integrals and parameter scans, severely limiting the complexity of processes that can be studied. We introduce Neural...
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Shivam Rawat (University of Bonnn)27/08/2026, 15:20Agentic AI
Agentic AI systems are increasingly deployed in scientific workflows, yet existing evaluations primarily measure task completion and provide limited insight into scientific reliability. We argue that such metrics are insufficient for assessing autonomous systems in research settings, where plausible but incorrect results may be more dangerous than overt failures.
We present a structured...
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Michelle Kuchera (Florida State University)27/08/2026, 16:30
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Roberto Trotta (SISSA)27/08/2026, 17:15
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