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16/06/2025, 12:30
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16/06/2025, 14:00
Welcome from the Vice-Rector for Research, Prof. Luciano Colombo and INFN Cagliari Director, Dr. Alessandro Cardini
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Andrea Contu (Istituto Nazionale di Fisica Nucleare)16/06/2025, 14:13
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David Keitel (Universitat de les Illes Balears)16/06/2025, 14:15Plenary talk
It took 100 years from Einsteinβs prediction to the first detection of gravitational waves on 14 September 2015 - the 10-year anniversary is this September! With >90 published detections from the first 3 observing runs and >200 significant event candidates from the current O4 run, the field of GW astronomy is thriving. Machine learning applications to GWs, though often still in early stages,...
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Luigi Favaro16/06/2025, 15:30
The speed and fidelity of detector simulations in particle physics pose compelling questions about LHC analysis and future colliders. The sparse high-dimensional data combined with the required precision provide a challenging task for modern generative networks. We present solutions with different tradeoffs, including accurate and precise Conditional Flow Matching and faster coupling-based...
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Jan Stark (L2I Toulouse, CNRS/IN2P3, UniversitΓ© de Toulouse)16/06/2025, 15:55
With the upcoming High-Luminosity Large Hadron Collider (HL-LHC) and the corresponding increase in collision rates and pile-up, a significant surge in data quantity and complexity is expected. In response, substantial R&D efforts in artificial intelligence (AI) and machine learning (ML) have been initiated by the community in recent years to develop faster and more efficient algorithms capable...
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Jennifer Ngadiuba (CMS)16/06/2025, 16:20Plenary talk
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Aleksandra ΔiprijanoviΔ (Department of Astronomy, Faculty of Mathematics, University of Belgrade)17/06/2025, 09:10Plenary talk
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Dr Mateusz Bawaj (University of Perugia)17/06/2025, 10:25
This work explores the application of Reinforcement Learning (RL) to the control of a Fabry-Perot (FP) optical cavity, a key component in interferometric gravitational-wave detectors. By leveraging RLβs inherent ability to handle high-dimensional non-linear systems, the project aims to achieve robust and autonomous cavity lockingβa process typically hindered by elevated finesse values, mirror...
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Noemi Anau Montel (Max Planck Institute for Astrophysics)17/06/2025, 10:50
Model misspecification analysis strategies, such as anomaly detection, model validation, and model comparison are a key component of scientific model development. Over the last few years, there has been a rapid rise in the use of simulation-based inference (SBI) techniques for Bayesian parameter estimation, applied to increasingly complex forward models. To move towards fully simulation-based...
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Judit PΓ©rez-Romero (CAC/UNG)17/06/2025, 11:15
The increasing volume of gamma-ray data from space-borne telescopes, like Fermi-LAT, and the upcoming ground-based telescopes, like the Cherenkov Telescope Array Observatory (CTAO), presents us with both opportunities and challenges. Traditional analysis methods based on likelihood analysis are often used for gamma-ray source detection and further characterization tasks. A key challenge to...
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Dr Zhihua Liang (Istituto Nazionale di Fisica Nucleare)17/06/2025, 12:00
The TimeSPOT project has developed innovative sensors optimized for precise space and time measurements of minimum-ionizing particles in high-radiation environments. These sensors demonstrate exceptional spatial resolution (around 10 Β΅m) and time resolution (around 10 ps), while withstanding high fluences (> 10ΒΉβ· 1 MeV n_eq/cmΒ²). Tests on small-scale structures confirm their potential for...
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Rosa Petrini (Istituto Nazionale di Fisica Nucleare)17/06/2025, 12:00
Machine Learning (ML) is driving a revolution in the way scientists design, develop, and deploy data-intensive software. However, the adoption of ML presents new challenges for the computing infrastructure, particularly in terms of provisioning and orchestrating access to hardware accelerators for development, testing, and production. The INFN-funded project AI_INFN ("Artificial Intelligence...
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Yanick Thurn (Deutsch)17/06/2025, 12:00
An important challenge in machine learning is to predict the initial conditions under which a given neural network will be trainable. We present a method for predicting the trainable regime in parameter space for deep feedforward neural networks (DNNs) based on reconstructing the input from subsequent activation layers via a cascade of single-layer auxiliary networks. We show that a single...
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Rodrigo Tenorio (UniversitΓ degli studi di Milano-Bicocca)17/06/2025, 12:00
We introduce a framework based on Short-time Fourier Transforms (SFTs)
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to analyze long-duration gravitational wave signals from compact binaries.
Targeted systems include binary neutron stars observed by third-generation
ground-based detectors and massive black-hole binaries observed by the LISA
space mission, for which we present a pilot application. Leveraging differentiable
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Nachiketa Chakraborty (University of Reading)17/06/2025, 12:03
Astrophysical sources vary across vast timescales, providing insight into extreme dynamical phenomena, from solar outbursts to distant AGNs and GRBs. These time-varying processes are often complex, nonlinear, and non-Gaussian, making it difficult to disentangle underlying causal mechanisms, which may act simultaneously or sequentially. Using solar variability and AGNs as examples, we...
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Lorenzo Mobilia (Istituto Nazionale di Fisica Nucleare)17/06/2025, 12:03
The first detection of the gravitational wave event GW150914 in 2015 opened the doors to the gravitational astronomy. Since then, hundreds of such events have been detected. Some of these have been particularly significant, such as GW170817, the first binary neutron star merger. This detection enabled a measurement of electromagnetic counterpart marking the beginning of the multi-messenger...
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Andrea Papaluca (University of Milan)17/06/2025, 12:03
We discuss about applications of hybrid quantum-classical computing and present Qiboml, an open-source software library for Quantum Machine Learning (QML) integrated with the Qibo quantum computing framework. Qiboml interfaces most used classical Machine Learning frameworks such as TensorFlow, PyTorch and Jax with Qibo. This combination enables users to construct quantum or hybrid...
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Mauro Bernardini17/06/2025, 12:03
Galaxy formation is a complex problem that links large-scale cosmology with small-scale astrophysics over cosmic timescales. The most principled method, full hydrodynamical simulations, come with high computational costs and thus, the development of faster models is essential. Modern field level emulation techniques leverage Convolutional Neural Networks (CNNs) to "paint" baryonic channels...
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Sanjiban Sengupta (CERN)17/06/2025, 12:06
Within ROOT/TMVA, we have developed SOFIE - System for Optimized Fast Inference code Emit - an engine designed to convert externally trained deep learning modelsβsuch as those in ONNX, Keras, or PyTorch formatsβinto optimized C++ code for fast inference. The generated code features minimal dependencies, ensuring seamless integration into the data processing and analysis workflows of...
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Harry Bevins (University of Cambridge)17/06/2025, 12:06
Neural network emulators or surrogates are widely used in astrophysics and cosmology to approximate expensive simulations, accelerating both likelihood-based inference and training for simulation-based inference. However, emulator accuracy requirements are often justified heuristically rather than with rigorous theoretical bounds. We derive a principled upper limit on the information loss...
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Rahul Srinivasan (Istituto Nazionale di Fisica Nucleare)17/06/2025, 12:23
Understanding the population properties of double white dwarfs (DWDs) in the Milky Way is a key science goal for the upcoming gravitational wave detector, LISA. However, the vast number of galactic binaries (~$30 \times 10^6$) and the large data size (~$6 \times 10^6$) pose significant challenges for traditional Bayesian samplers. In this talk, I present a simulation-based inference framework...
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Dr Fabio Cufino (University of Bologna)17/06/2025, 12:23
We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model the neurons receive time-encoded information on the position of particle hits in a tracking detector for a particle collider, modeled according to the geometry of...
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Lorenzo Colantonio (Sapienza University of Rome and INFN Rome)17/06/2025, 12:26
The graph coloring problem is an optimization problem involving the assignment of one of q colors to each vertex of a graph such that no two adjacent vertices share the same color. This problem is computationally challenging and arises in several practical applications. We present a novel algorithm that leverages graph neural networks to tackle the problem efficiently, particularly for large...
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Uraz Odyurt (University of Twente)17/06/2025, 12:26
The Transformer Machine Learning architecture has been gaining considerable momentum in recent years. Computational High-Energy Physics tasks such as jet tagging and particle track reconstruction (tracking), have either achieved proper solutions, or reached considerable milestones using Transformers. On the other hand, the use of specialised hardware accelerators, especially FPGAs, is an...
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Prof. Alessandro Chiolerio (Istituto Italiano di Tecnologia, Bioinspired Soft Robotics)17/06/2025, 12:43
Reservoir computing (RC) has emerged as a powerful paradigm for processing temporal data and pattern recognition, leveraging the intrinsic dynamics of complexcsystems to perform high-dimensional nonlinear transformations without the need for training highly sophisticated networks. Our recent achievements show that colloidal systems β specifically engineered suspensions of nanoparticles in...
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Dr Saul Alonso Monsalve (ETH Zurich)17/06/2025, 12:46
Deep learning is playing an increasingly important role in particle physics, offering powerful tools to tackle complex challenges in data analysis. This talk presents a range of advanced deep-learning techniques applied to neutrino physics, with a particular focus on the T2K experiment. The discussion includes the use of cutting-edge models such as transformers or sparse submanifold...
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Marvin Kohls (GSI Helmholtzzentrum fΓΌr Schwerionenforschung GmbH), Dr Simon Spies (Goethe University Frankfurt)17/06/2025, 12:46
In this conference contribution, we present our findings on applying Artificial Neural Networks (ANNs) to enhance off-vertex topology recognition using data from the HADES experiment at GSI, Darmstadt. Our focus is on decays of $\Lambda$ and K$^0_{\text{S}}$ particles produced in heavy ion as well as elementary reactions. We demonstrate how ANNs can enhance the separation of weak decays from...
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Jose Manuel Deltoro Berrio (IFIC (Instituto de FΓsica Corpuscular))17/06/2025, 12:46
Pulse pile-up is a common issue in nuclear spectroscopy and nuclear reaction studies, degrading energy and timing accuracy in particle identification. This work presents a novel method for reconstructing pile-up events using a one-dimensional convolutional autoencoder (1D-CAE). The method effectively separates and reconstructs overlapping pulses, enabling acceptance of these events and...
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Ms Shiqi Su (Department of Physics and Astronomy, University of Leicester)17/06/2025, 12:49
The adoption of AI-based techniques in theoretical research is often slower than in other fields due to the perception that AI-based methods lack rigorous validation against theoretical counterparts. In this talk, we introduce COEmuNet, a surrogate model designed to emulate carbon monoxide (CO) line radiation transport in stellar atmospheres.
COEmuNet is based on a three-dimensional...
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Dr Enrico Lupi (INFN Padova, University of Padova)17/06/2025, 12:49
In this work we simulate hadrons impinging on a homogeneous lead-tungstate (PbWO4) calorimeter to investigate how the resulting light yield and its temporal structure, as detected by an array of light-sensitive sensors, can be processed by a neuromorphic computing system. Our model encodes temporal photon distributions in the form of spike trains and employs a fully connected spiking neural...
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Niko Nanda Putra Nila Lay (Technical University of Munich)17/06/2025, 12:49
The LEGEND experiment aims to detect neutrinoless double-beta ($0\nu\beta\beta$) decay using high-purity germanium detectors (HPGes) enriched in $^{76}$Ge, immersed in instrumented liquid argon (LAr). Atmospheric LAr contains the cosmogenically activated isotope $^{42}$Ar, whose decay progeny, $^{42}$K, can undergo beta decay ($Q_{\beta} = 3.5$ MeV) on the HPGe surface. Without the baseline...
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Nicola Fulvio Calabria (Istituto Nazionale di Fisica Nucleare)17/06/2025, 14:15
Super-Kamiokande is a 50-kton Water Cherenkov detector, operating since 1996 in the Kamioka mine, Japan, whose broad scientific program spans from neutrino physics to baryon number violating processes, such as proton decay. In this preliminary study I show the development of a Deep Learning model, based on Convolutional Neural Networks (CNN) and Residual Neural Networks (ResNet), for event...
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Shreya Saha (University of Adelaide)17/06/2025, 14:15
The application of machine learning techniques in particle physics has accelerated the development of methodologies for exploring physics beyond the Standard Model. This talk will present an overview of anomaly detection and its potential to enhance the detection of new physics. The talk will discuss the adaptation and real-time deployment of anomaly detection algorithms. Additionally, a novel...
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Jeffrey Lazar (UniversitΓ© Catholique de Louvain)17/06/2025, 14:15
There are currently many Cherenkov neutrino telescopes being deployed and designed across the world. These detectors are exploring new optical sensors and geometric configurations to maximize their physics goals. Alongside detector R&D, machine learning (ML) has become established as a promising avenue for reconstructions in these detectors; however, there has not been a consistent comparison...
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Sascha Caron17/06/2025, 14:15
This work explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the demands of physics research. LPMs can function independently or as part of an...
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Ethan Meszaros (UniversitΓ© de MontrΓ©al (CA))17/06/2025, 14:18
The search for resonant mass bumps in invariant-mass histograms is a fundamental approach for uncovering Beyond the Standard Model (BSM) physics at the Large Hadron Collider (LHC). Traditional, model-dependent analyses that utilize this technique, such as those conducted using data from the ATLAS detector at CERN, often require substantial resources, which prevent many final states from being...
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Alejandro Romero-Ros (Universitat de Barcelona)17/06/2025, 14:18
One of the main challenges in solving quantum many-body (MB) problems is the exponential growth of the Hilbert space with system size.
In this regard, a new promising alternative are neural-network quantum states (NQS).
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This approach leverages the parameterization of the wave function with neural-network architectures.
Compared to other variational methods, NQS are highly scalable with... -
Laura Boggia (LPNHE)17/06/2025, 14:18
Anomaly detection in multivariate time series is crucial to ensure the quality of data coming from a physics experiment. Accurately identifying the moments when unexpected errors or defects occur is essential but challenging, as the types of anomalies are unknown beforehand and reliably labeled data is scarce. Additionally, the multi-dimensional nature of time series data adds to the problemβs...
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Mr Ivan Mozun Mateo (LPC CAEN / CNRS / IN2P3)17/06/2025, 14:21
KM3NeT is a new research infrastructure housing the next generation of neutrino telescopes in the Mediterranean deep sea. This facility comprises two detectors: KM3NeT/ARCA and KM3NeT/ORCA, consisting of vertically-arranged detection units, 230 and 115, respectively, each equipped with 18 digital optical modules. The photomultipliers within each optical module detect Cherenkov light emitted by...
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Ludvig Doeser (Stockholm University)17/06/2025, 14:21
Traditional gradient-based optimization and statistical inference methods often rely on differentiable models, making it challenging to optimize models with non-differentiable components. In this talk, Iβll introduce Learning the Universe by Learning to Optimize (LULO), a novel deep learning-based framework designed to fit non-differentiable simulators at non-linear scales to data. By...
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James Smith (University of Manchester)17/06/2025, 14:21
The storage, transmission and processing of data is a major challenge across many fields of physics and industry. Traditional generic data compression techniques are lossless, but are limited in performance and require additional computation.
BALER [1,2] is an open-source autoencoder-based framework for the development of tailored lossy data compression models suitable for data from...
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Mr Giovanni LaganΓ (UniversitΓ degli Studi di Milano Bicocca), Martino Borsato (Milano Bicocca University and INFN), Maurizio Martinelli (UniversitΓ degli Studi di Milano Bicocca e INFN)17/06/2025, 14:24
Cherenkov rings play a crucial role in identifying charged particles in high-energy physics (HEP) experiments. The size of the light cone depends directly on the mass and momentum of the particle that produced it. Most Cherenkov ring pattern reconstruction algorithms currently used in HEP experiments rely on a likelihood fit to the photo-detector response, which often consumes a significant...
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Ke Li17/06/2025, 14:35
The data processing and analyzing is one of the main challenges at HEP experiments, normally one physics result can take more than 3 years to be conducted. To accelerate the physics analysis and drive new physics discovery, the rapidly developing Large Language Model (LLM) is the most promising approach, it have demonstrated astonishing capabilities in recognition and generation of text while...
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Sylvain Caillou (L2I Toulouse, CNRS/IN2P3, UniversitΓ© de Toulouse)17/06/2025, 14:41
Learning from heterogeneous data is one of the major challenges for AI in the coming years, particularly for what are called 'foundation models'. In fundamental physics, the heterogeneity of data can come from the instruments used to acquire them (the subsystems of large detectors in particle physics or the crossing of modalities in multi-messenger, multi-instrument systems in astrophysics,...
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Dmitrii Kobylianskii (Weizmann Institute of Science)17/06/2025, 14:41
We extend the Particle-flow Neural Assisted Simulations (Parnassus) framework of fast simulation and reconstruction to entire collider events. In particular, we use two generative Artificial Intelligence (genAI) tools, conditional flow matching and diffusion models, to create a set of reconstructed particle-flow objects conditioned on stable truth-level particles from CMS Open Simulations....
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Sam Leeney (University of Cambridge)17/06/2025, 14:44
We present a novel Bayesian anomaly detection framework, applied to supernova analysis, that exploits a custom-built, differentiable, and highly parallelisable JAX implementation of the commonly used SNcosmo framework. In our framework, each supernovaβs flux is modelled via the SALT3 formalism, with the core computationβintegrating the model flux over observational bandpassesβbeing fully...
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Dr Dmitriy Kostunin (DESY)17/06/2025, 14:55
We develop AI agents based on instruction-finetuned large language models (LLMs) to assist in the engineering and operation of the Cherenkov Telescope Array Observatory (CTAO) Control and Data Acquisition Software (ACADA). These agents align with project-specific documentation and codebases, understand contextual information, interact with external APIs, and communicate with users in natural...
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Yong Sheng Koay (Uppsala University)17/06/2025, 14:58
In physics, Lagrangians provide a systematic way to describe laws governing physical systems. In the context of particle physics, they encode the interactions and behavior of the fundamental building blocks of our universe. By treating Lagrangians as complex, rule-based constructs similar to linguistic expressions, we trained a transformer model-- proven to be effective in natural language...
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Micol Olocco (TU Dortmund)17/06/2025, 15:01
This work presents an open framework for generating synthetic transactional datasets, addressing the twin challenges of data scarcity and privacy in fraud research. Conducted as an industry secondment at IBM France Lab Saclay within the SMARTHEP Networkβa European project fostering collaboration between High Energy Physics and Industryβour approach leverages Generative AI Agents to simulate...
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Waleed Esmail (University of MΓΌnster)17/06/2025, 15:01
We introduce a SeismoGPT, a foundation model for seismology that leverages transformer-based architectures to model seismic waveforms. Inspired by natural language processing techniques. This approach tokenizes continuous seismograms by dividing them into fixed-length patches, where each patch represents a sequence of waveform samples. These patches serve as input tokens to the transformer...
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Malte Algren (UNIGE)17/06/2025, 15:01
We present a novel method for pile-up removal of pp interactions using variational inference with diffusion models, called Vipr. Instead of using classification methods to identify which particles are from the primary collision, a generative model is trained to predict the constituents of the hard-scatter particle jets with pile-up removed. This results in an estimate of the full posterior...
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Andre Scaffidi (SISSA)17/06/2025, 15:04
Modern high-energy physics experiments generate massive, high-dimensional datasets that demand advanced strategies for anomaly detection. This talk presents EagleEye, a novel density-based method designed to compare two multivariate distributions on a point-by-point basis using a distribution-free approach rooted in Bernoulli and binomial statistics. EagleEyeβs deterministic framework,...
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Marta Ewelina Babicz (University of Zurich)17/06/2025, 15:04
High-purity germanium (HPGe) detectors play a critical role in nuclear physics experiments, including searches for neutrinoless double-beta decay. Traditional pulse shape discrimination (PSD) methods help distinguish signal from background events in such detectors. However, the performance of these traditional PSD methods declines at lower energies (500 β² keV). This low-energy regime is...
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Mr David Giesegh (LMU Munich)17/06/2025, 15:04
In experimental particle physics, the development of analyses depends heavily on the accurate simulation of background processes, including both the particle collisions/decays and their subsequent interactions with the detector. However, for any specific analysis, a large fraction of these simulated events is discarded by a selection tailored to identifying interesting events to study. At...
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Giada Badaracco (ETH Zurich)17/06/2025, 15:07
As searches at the LHC probe increasingly rare signals against an overwhelming background of Standard Model events, progressively tighter selection criteria are applied to enhance signal-rich regions. Simulated background samples serve as the basis for hypothesis testing, enabling comparisons between observed data and expected Standard Model backgrounds. However, this approach becomes...
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Arsenii Gavrikov (Istituto Nazionale di Fisica Nucleare)17/06/2025, 15:10
Ensuring reliable data collection in large-scale particle physics experiments demands Data Quality Monitoring (DQM) procedures to detect possible detector malfunctions and preserve data integrity. Traditionally, this resource-intensive task has been handled by human shifters who may struggle with frequent changes in operational conditions. Instead, to simplify and automate the shifters' work,...
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Maria Ubiali17/06/2025, 15:45Plenary talk
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Luis Felipe Cattelan (University of Zurich)17/06/2025, 16:40
The SHiP experiment is a proposed fixed-target experiment at the CERN SPS aimed at searching for feebly interacting particles beyond the Standard Model. One of its main challenges is reducing the large number of muons produced in the beam dump, which would otherwise create significant background in the detector. The muon shield, a system of magnets designed to deflect muons away from the...
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Ryan Ferguson (University of Glasgow)17/06/2025, 16:40
Current studies of the hadron spectrum are limited by the accuracy and consistency of datasets. Information derived from theory models often requires fits to measurements taken at specific values of kinematic variables, which needs interpolation between such points. In sparse datasets the quantification of uncertainties is problematic. Machine Learning is a powerful tool that can be used to...
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Andrea Paccagnella (Istituto Nazionale di Fisica Nucleare)17/06/2025, 16:40
The LHCf experiment aims to study forward neutral particle production at the LHC, providing crucial data for improving hadronic interaction models used in cosmic ray physics. A key challenge in this context is the reconstruction of events containing (K^0) mesons, which often involve multiple calorimetric hits.
To address this, we developed a machine learning pipeline that employs multiple...
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Yuvraj Sharma17/06/2025, 16:40
The future of Gravitational Wave (GW) detectors [LVK] have made remarkable progress, with an expanding sensitivity band and the promise of exponential increase in detection rates for upcoming observing runs [O4 and beyond]. Among the diverse sources of GW signals, eccentric Binary mergers present an intriguing and computationally challenging aspect. We address the imperative need for efficient...
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Ivan Kisel17/06/2025, 16:43
The field of heavy-ion experiments, particularly those like the upcoming Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR), requires high-performance algorithms capable of efficient real-time data analysis. The incorporation of machine learning, especially artificial neural networks, into these experiments is a major breakthrough. This report...
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Ragansu Chakkappai (Universit\'e Paris-Saclay, CNRS/IN2P3, IJCLab)17/06/2025, 16:43
The Fair Universe project organised the HiggsML Uncertainty Challenge, which took place from Sep 2024 to 14th March 2025. This groundbreaking competition in high-energy physics (HEP) and machine learning was the first to place a strong emphasis on uncertainties, focusing on mastering both the uncertainties in the input training data and providing credible confidence intervals in the...
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Mathis Gerdes (University of Amsterdam)17/06/2025, 16:43
Recent advances in generative models have demonstrated the potential of normalizing flows for lattice field theory, particularly in mitigating critical slowing down and improving sampling efficiency. In this talk, I will discuss the role of continuous normalizing flows (CNF) or neural ODEs in learning field theories, highlighting their advantages and challenges compared to discrete flow...
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Shah Rukh Qasim (University of Zurich)17/06/2025, 16:43
We present a case for the use of Reinforcement Learning (RL) for the design of physics instrument as an alternative to gradient-based instrument-optimization methods (arXiv:2412.10237). It's applicability is demonstrated using two empirical studies. One is longitudinal segmentation of calorimeters and the second is both transverse segmentation as well longitudinal placement of trackers in a...
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Christopher Eckner (University of Nova Gorica)17/06/2025, 16:46
Over the past 16 years, 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 sophisticated astrophysical...
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Martino Errico (Sapienza UniversitΓ di Roma and INFN Roma)17/06/2025, 16:46
The upcoming High Luminosity phase of the Large Hadron Collider will require significant advancements in real-time data processing to handle the increased event rates and maintain high-efficiency trigger decisions. In this work, we extend our previous studies on deploying compressed deep neural networks on FPGAs for high-energy physics applications by exploring the acceleration of graph neural...
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28. ποΈ An implementation of neural simulation-based inference for parameter estimation in ATLASJay Sandesara (University of Wisconsin-Madison)17/06/2025, 16:49
Neural simulation-based inference is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements, including at the Large Hadron Collider, where no single observable may be optimal to scan over...
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Federico Nardi (Istituto Nazionale di Fisica Nucleare)17/06/2025, 17:03
Setup design is a critical aspect of experiment development, particularly in high-energy physics, where decisions influence research trajectories for decades. Within the MODE Collaboration, we aim to generalize Machine Learning methodologies to construct a fully differentiable pipeline for optimizing the geometry of the Muon Collider Electromagnetic Calorimeter.
Our approach leverages...
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Oleg Savchenko17/06/2025, 17:03
Knowledge of the primordial matter density field from which the present non-linear observations formed is of fundamental importance for cosmology, as it contains an immense wealth of information about the physics, evolution, and initial conditions of the universe. Reconstructing this density field from galaxy survey data is a notoriously difficult task, requiring sophisticated statistical...
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Francesco Curcio (IFIC University of Valencia and CSIC)17/06/2025, 17:06
The HL-LHC project is driving significant upgrades to the ATLAS experiment to enhance data processing and maintain its discovery potential under high-luminosity conditions. A key aspect of this upgrade is the replacement of the readout electronics for the ATLAS Tile Hadronic Calorimeter. The new Tile PreProcessor (TilePPr) system, equipped with Kintex Ultrascale FPGAs, serves as the interface...
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Dr Filippo Santoliquido (Gran Sasso Science Institute (GSSI))17/06/2025, 17:09
The Einstein Telescope (ET), along with other third-generation gravitational wave (GW) detectors, will be a key instrument for detecting GWs in the coming decades. However, analyzing the data and estimating source parameters will be challenging, especially given the large number of expected detections β of order 10^5 per year β which makes current methods based on stochastic sampling...
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Johannes Alexander Jaeger (Paul Scherrer Institute)17/06/2025, 17:23
Monte-Carlo (MC) simulations are essential for designing particle physics experiments, as they enable us to evaluate and optimize key objectivesβsuch as enhancing experimental sensitivity and performance. Since exhaustively sampling the full parameter space of experimental configurations is computationally prohibitive, sample-efficient methods to identify promising configurations are...
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Vera Maiboroda (IJCLab, CNRS)17/06/2025, 17:23
The interTwin project develops an open-source Digital Twin Engine to integrate application-specific Digital Twins (DTs) across scientific domains. Its framework for the development of DTs supports interoperability, performance, portability and accuracy. As part of this initiative, we implemented the CaloINN normalizing-flow model for calorimeter simulations within the interTwin framework....
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Valentin Kladov (Ruhr-UniversitΓ€t Bochum)17/06/2025, 17:26
The real-time data processing of next-generation experiments at FAIR requires precise event topology reconstruction, which in turn depends on accurate in-situ calibration procedures. Machine learning techniques offer a promising approach to achieving fast and reliable calibrations using continuously available environmental data. In this study, we investigate a neural network-based method for...
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Jonathan Klimesch (Max Planck Institute for the Science of Light)17/06/2025, 17:26
The traditional design of gravitational wave detectors follows a human-centric, rational approach based on domain expertise and ingenuity. However, the vast space encompassing all possible experimental configurations suggests that some powerful and unconventional detection strategies lay outside the reach of such human-driven design. An AI-based approach that scales with increasing computation...
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Samuele Grossi (Istituto Nazionale di Fisica Nucleare)17/06/2025, 17:29
Deep generative models have become powerful tools for alleviating the computational burden of traditional Monte Carlo generators in producing high-dimensional synthetic data. However, validating these models remains challenging, especially in scientific domains requiring high precision, such as particle physics. Two-sample hypothesis testing offers a principled framework to address this task....
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Benedetta Argiento (Istituto Nazionale di Fisica Nucleare)17/06/2025, 17:29
Magnetic Resonance Spectroscopy is a powerful, non-invasive tool for in vivo biochemical and metabolic tissue analysis, yet its widespread clinical application remains hindered by susceptibility to motion artifacts. Traditional retrospective corrections struggle with real-time constraints, limiting diagnostic precision in key medical scenarios such as neurodegenerative disease monitoring.
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The... -
Vilius Cepaitis (University of Geneva)17/06/2025, 17:32
The ATLAS detector at the LHC has comprehensive data quality monitoring procedures for ensuring high quality physics analysis data. This contribution introduces a long short-term memory (LSTM) autoencoder-based algorithm designed to identify detector anomalies in ATLAS liquid argon calorimeter data. The data is represented as a multidimensional time series, corresponding to statistical moments...
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Kai Lehman (LMU Munich)17/06/2025, 17:32
How much cosmological information can we reliably extract from existing and upcoming large-scale structure observations? Many summary statistics fall short in describing the non-Gaussian nature of the late-time Universe in comparison to existing and upcoming measurements. We demonstrate that we can identify optimal summary statistics and that we can link them with existing summary statistics....
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Fernando Souza (LIP)17/06/2025, 17:35
In High Energy Physics, when testing theoretical models of new physics against experimental results, the customary approach is to simply sample random points from the parameter space of the model, calculate their predicted values for the desired observables and compare them to experimental data. However, due to the typically large number of parameters in these models, this process is highly...
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14. π pop-cosmos: scaleable Bayesian inference of galaxy properties under a diffusion model priorStephen Thorp (Oskar Klein Centre, Stockholm University)18/06/2025, 09:00
Projects such as the imminent Vera C. Rubin Observatory are critical tools for understanding cosmological questions like the nature of dark energy. By observing huge numbers of galaxies, they enable us to map the large scale structure of the Universe. However, this is only possible if we are able to accurately model our photometric observations of the galaxies, and thus infer their redshifts...
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Drew Jamieson (Max-Planck Institute for Astrophysics)18/06/2025, 09:25
Upcoming galaxy surveys promise to greatly inform our models of the Universeβs composition and history. Leveraging this wealth of data requires simulations that are accurate and computationally efficient. While N-body simulations set the standard for precision, their computational cost makes them impractical for large-scale data analysis. In this talk, I will present a neural network-based...
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18/06/2025, 11:15Plenary talk
Presentation of the Einstein Telescope project by Domenico D'Urso followed by a panel discussion coordinated by Elena Cuoco with Domenico D'Urso, David Keitel and Ik Siong Heng
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Simone Bordoni (Sapienza universita di Roma and INFN Roma)18/06/2025, 15:00
Generative models based on diffusion processes have recently emerged as powerful tools in artificial intelligence, enabling high-quality sampling in a variety of domains. In this work, we propose a novel hybrid quantum-classical diffusion model, where artificial neural networks are replaced with parameterized quantum circuits to directly generate quantum states. To overcome the limitations of...
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Theo Cuisset ((LLR, Ecole Polytech., IN2P3-CNRS, France)18/06/2025, 15:00
The high-luminosity era of the LHC will offer greatly increased number of events for more precise Standard Model measurements and Beyond Standard Model searches, but will also pose unprecedented challenges to the detectors. To meet these challenges, the CMS detector will undergo several upgrades, including the replacement of the current endcap calorimeters with a novel High-Granularity...
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Francesco D'Angelo (Istituto Nazionale di Fisica Nucleare)18/06/2025, 15:00
Most of the antimatter in cosmic rays is produced by collisions of high energy particles with the interstellar medium while they propagate through it. The detection of an antimatter component over the collisional background can be used to investigate new sources, as the presence of dark matter annihilations in the halo. A possible smoking gun for dark matter is given by the detection of...
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Tomasz Stebel (Jagiellonian University)18/06/2025, 15:03
The density matrix of a quantum system provides complete information about its entanglement. Using generative autoregressive networks, we show how to estimate the matrix elements for the small quantum spin chain. Using a density matrix, we calculate Renyi entanglement entropies as well as Shanon entropy at zero temperature.
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Jonas WΓΌrzinger (Technical University of Munich)18/06/2025, 15:03
(Sub-)millimeter single-dish telescopes offer two key advantages compared to interferometers: they can efficiently map larger portions of the sky, and they can recover larger spatial scales. Nonetheless, fluctuations in the atmosphere, the dominant noise source in ground-based observations, limit the accurate retrieval of signals from astronomical sources. We introduce maria...
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Pooja Bhattacharjee18/06/2025, 15:03
Axion-like particles (ALPs) appear in various extensions of the Standard Model and can interact with photons, leading to ALP-photon conversions in external magnetic fields. This phenomenon can introduce characteristic energy-dependent βwigglesβ in gamma-ray spectra. The Cherenkov Telescope Array Observatory (CTAO) is the next-generation ground-based gamma-ray observatory, designed to enhance...
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Pelayo Leguina (Universidad de Oviedo, Spain)18/06/2025, 15:03
In view of the HL-LHC, the Phase-2 CMS upgrade will replace the entire trigger and data acquisition system. The L1T system has been designed to process 63 Tb/s input bandwidth with state-of-the-art commercial FPGAs and high-speed optical links reaching up to 28 Gb at a fixed latency below 12.5 Β΅s. In view of the upgraded trigger system and in preparation for the HL-LHC, a GNN has been trained...
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David Yallup (University of Cambridge)18/06/2025, 15:06
Nested Sampling is a Monte Carlo method that performs parameter estimation and model comparison robustly for a variety of high dimension and complicated distributions. It has seen widespread usage in the physical sciences, however in recent years increasingly it is viewed as part of a legacy code base, with GPU native paradigms such as neural simulation based inference coming to the fore. In...
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Tobias Golling (University of Geneva)18/06/2025, 15:06
This contribution discusses an anomaly detection search for narrow-width resonances beyond the Standard Model that decay into a pair of jets. Using 139 fbβ1 of proton-proton collision data at sqrt(s) = 13 TeV, recorded from 2015 to 2018 with the ATLAS detector at the Large Hadron Collider, we aim to identify new physics without relying on a specific signal model. The analysis employs two...
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Kai Zhou (CUHK-Shenzhen)18/06/2025, 15:06
Recent advances in machine learning have unlocked transformative approaches to longstanding challenges in fundamental physics. In this talk, I will present our latest work that harnesses physicsβdriven deep learning to tackle two intertwined frontiers: solving inverse problems in Quantum Chromodynamics (QCD) and deploying generative models for statistical physics and field theory.
Inverse...
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James Alvey (University of Cambridge)18/06/2025, 15:09
The Laser Interferometer Space Antenna (LISA) will provide an unprecedented window into the gravitational wave sky. However, it also presents a serious data analysis challenge to separate and classify various classes of deterministic sources, instrumental noise, and potential stochastic backgrounds. This "global fit" problem presents an extremely high-dimensional inference task that sits right...
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Jessica Irwin (University of Glasgow)18/06/2025, 15:09
We discuss data compression methods and evidence of learned structure in using Normalising Flows to perform the conditional mapping of nuclear equation of state data given observed parameters from gravitational wave signals of binary neutron star mergers. We use a convolutional autoencoder to compress unified high density equations of state - including data from the neutron star crust - to a...
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Efe Yigitbasi (Rice Univ., USA)18/06/2025, 15:23
We report on the development, implementation, and performance of a fast neural network used to measure the transverse momentum in the CMS Level-1 Endcap Muon Track Finder. The network aims to improve the triggering efficiency of muons produced in the decays of long-lived particles. We implemented it in firmware for a Xilinx Virtex-7 FPGA and deployed it during the LHC Run 3 data-taking in...
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VΓctor BresΓ³ Pla (University of Heidelberg)18/06/2025, 15:26
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields
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state-of-the-art performance for a wide range of machine learning tasks at the Large
Hadron Collider. L-GATr represents data in a geometric algebra over space-time and is
equivariant under Lorentz transformations. The underlying architecture is a versatile
and scalable transformer, which is able to break... -
Thibeau Wouters (Utrecht University)18/06/2025, 15:29
Third-generation (3G) gravitational wave (GW) observatories will unveil a cosmic orchestra, detecting thousands of sources annually. However, their increased detection rate poses a major challenge for data analysis. Existing, widely used techniques to obtain the source parameters are prohibitively expensive, creating a bottleneck for extracting scientific insights from 3G detector data. We...
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Antonio D'Avanzo (Istituto Nazionale di Fisica Nucleare)18/06/2025, 15:29
Searches for new physics at the LHC traditionally use advanced simulations to model Standard Model (SM) processes in high-energy collisions. These are then compared with predictions from new-physics theories like dark matter and supersymmetry. However, despite extensive research, no definitive signs of physics beyond the Standard Model (BSM) have been found since the Higgs boson's...
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Giuseppe Di Somma (Istituto Nazionale di Fisica Nucleare)18/06/2025, 15:43
GINGER data analysis is based on the experience gained with GINGERINO data analysis, the general analysis scheme will be described.
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The reconstruction of the beat frequency of a laser gyroscope signal in the shortest possible time is a non-trivial challenge. Advancements in artificial intelligence are used to develop a DAQ system capable of determining the beat signal frequency with higher... -
Dr Douglas Boubert (PhysicsX), Thomas Gessey-Jones (PhysicsX)18/06/2025, 15:46
Many scientific and engineering problems are fundamentally linked to geometry, for example, designing a part to maximise strength or modelling fluid flow around an airplane wing. Thus, there is substantial interest in developing machine learning models that can not only operate on or output geometric data, but generate new geometries. Such models have the potential to revolutionise advanced...
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Prince Kumar (Indian Inst. of Technology, Madras, India)18/06/2025, 15:46
Understanding the substructure of jets initiated by heavy quarks is essential for quantum chromodynamics (QCD) studies, particularly in the context of the dead-cone effect and jet quenching. The kinematics of b-hadron decays present a challenge for substructure measurements with inclusive b-jets. We propose an approach using geometric deep learning to extract the optimal representation of the...
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Chiara Anselmo (Istituto Nazionale di Fisica Nucleare)18/06/2025, 15:49
Simulation-based inference (SBI) has emerged as a powerful tool for parameter estimation, particularly in complex scenarios where traditional Bayesian methods are computationally intractable. In this work, we build upon a previous application of SBI, based on truncated neural posterior estimation (TNPE), to estimate the parameters of a gravitational wave post-merger signal, using real data...
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Tore von Schwartz (UniversitΓ€t Hamburg)18/06/2025, 15:49
The Standard Model of particle physics has been successful in describing fundamental particles and their interactions, yet it fails to explain concepts like dark matter or the hierarchy problem, motivating the search for physics beyond the Standard Model. Despite an extensive search program at the LHC, no hints for new physics have been found so far. Anomaly detection has been introduced as a...
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Florencia Castillo (Laboratoire d'Annecy De Physique Des Particules (LAPP))18/06/2025, 15:49
Jet constituents provide a more detailed description of the radiation pattern within a jet compared to observables summarizing global jet properties. In Run 2 analyses at the LHC using the ATLAS detector, transformer-based taggers leveraging low-level variables outperformed traditional approaches based on high-level variables and conventional neural networks in distinguishing quark- and...
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Prof. Danny Baranes (Department of Molecular Biology, School of Medicine, Ariel University, Ariel, Israel)18/06/2025, 15:52
It was recently demonstrated [1] that brain networks and the cosmic web share key structural featuresβsuch as degree distributions, path lengths, modularity, and information density. Inspired by this work, we apply AI-based methods to study the geometry of neuronal networks formed by isolated brain neurons in culture, with a focus on the spontaneous formation of dendritic lattices and noted...
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Dr Michael Williams (University of Portsmouth)18/06/2025, 15:52
Bayesian inference is essential for understanding the compact binaries that produce gravitational waves detected by the LIGO-Virgo-KAGRA collaboration. Performing this inference is computationally expensive and often has to be repeated multiple times with different models, e.g. different approximations of General Relativity. These repeated analyses always start from scratch, which is highly...
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Roberto Seidita (ETH ZΓΌrich, Switzerland)18/06/2025, 15:52
Unsupervised machine learning algorithms are powerful tools for identifying potential new physics at the LHC, enabling the separation of standard model (SM) background events from anomalous signal events without relying on predefined signal hypotheses. Autoencoders (AEs) are frequently employed in such tasks, but their effectiveness is often hindered by the reconstruction of outliers. In this...
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Adam Ormondroyd (University of Cambridge)18/06/2025, 15:55
Recent cosmological surveys have opened a new window onto the nature of dark energy. In our work we reconstruct the dark energy equation of state using a βflexknotβ parameterisation that represents $w(a)$ as a linear spline with freeβmoving nodes. By combining the latest DESI Baryonic Acoustic Oscillation measurements with Pantheon+ supernovae dataβand crossβchecking our results with an...
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Dr Darius JurΔiukonis (Vilnius University (LT))18/06/2025, 16:30
Machine learning techniques are used to predict theoretical constraintsβsuch as unitarity, boundedness from below, and the potential minimumβin multi-scalar models. This approach has been demonstrated to be effective when applied to various extensions of the Standard Model that incorporate additional scalar multiplets. A high level of predictivity is achieved through appropriate neural network...
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Margita KubΓ‘tovΓ‘ (FZU - Institute of Physics of the Czech Academy of Sciences)18/06/2025, 16:30
The Pierre Auger Observatory is a cosmic-ray detector that uses multiple systems to simultaneously observe extensive air showers (EAS). EAS are particle cascades initiated by ultra-high-energy cosmic rays (UHECRs) interacting with the atmosphere of the Earth. Determining the sources of UHECRs requires precise knowledge of their mass composition. One key observable for estimating the mass of an...
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50. β¨ Interaction-Aware and Domain-Invariant Representation Learning for Inclusive Flavour TaggingDr Quentin FΓΌhring (Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany)18/06/2025, 16:30
Measurements of neutral, oscillating mesons are a gateway to quantum mechanics and give access to the fundamental interactions of elementary particles. For example, precise measurements of $CP$ violation in neutral $B$ mesons can be taken in order to test the Standard Model of particle physics. These measurements require knowledge of the $B$-meson flavour at the time of its production, which...
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Dily Duan Yi Ong (University of Cambridge)18/06/2025, 16:30
In recent years, disparities have emerged within the context of the concordance model regarding the estimated value of the Hubble constant H0 [1907.10625] using Cosmic Microwave Background (CMB) and Supernovae data (commonly referred to as the Hubble tension), the clustering Ο8 [1610.04606] using CMB and weak lensing, and the curvature Ξ©K [1908.09139, 1911.02087] using CMB and lensing/BAO, and...
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Federico Stachurski (Univeristy of Glasgow)18/06/2025, 16:33
We present a machine learning approach using normalizing flows for inferring cosmological parameters
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from gravitational wave events. Our methodology is general to any type of compact binary coalescence
event and cosmological model and relies on the generation of training data representing distributions of
gravitational wave event parameters. These parameters are conditional on the... -
Marta Ewelina Babicz (University of Zurich)18/06/2025, 16:33
TheΒ LEGEND experimentΒ searches for neutrinoless double beta decay (0Ξ½Ξ²Ξ²) using high-purity germanium (HPGe) detectors enriched inΒ 7676Ge, where minimizing backgrounds is essential for improving discovery sensitivity. A key technique for this isΒ pulse shape discrimination (PSD), traditionally done using A/E-based cuts that exploit differences in current pulse morphology. However, such methods...
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Ms Huifang Lyu (University of Amsterdam)18/06/2025, 16:33
Simulation-based inference (SBI) has seen remarkable development in recent years and has found widespread application across a range of physical sciences. A defining characteristic of SBI is its ability to perform likelihood-free inference, relying on simulators rather than explicit likelihood functions. Several representative methods have emerged within this framework, such as Approximate...
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Leonora Kardum18/06/2025, 16:33
Today, many physics experiments rely on Machine Learning (ML) methods to support their data analysis pipelines. Although ML has revolutionized science, most models are still difficult to interpret and lack clarity of the process with which they calculate results and the way they utilize information from used datasets. In this work, we introduce physics-guided ML methods that keep the...
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Metha Prathaban (University of Cambridge)18/06/2025, 16:36
The 2030s are anticipated to be a golden era in ground-based gravitational wave astronomy, with the advent of next-generation observatories such as the Einstein Telescope and Cosmic Explorer set to revolutionize our understanding of the universe. However, this unprecedented sensitivity and observational depth will come with a significant increase in the computational demands of...
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Andreas Ipp (TU Wien, Austria)18/06/2025, 16:36
Extracting continuum properties from discretized quantum field theories is significantly hindered by lattice artifacts. Fixed-point (FP) actions, defined via renormalization group transformations, offer an elegant solution by suppressing these artifacts even on coarse lattices. In this work, we employ gauge-covariant convolutional neural networks to parameterize an FP action for...
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Ayodele Ore (Heidelberg University, ITP)18/06/2025, 16:36
The Square Kilometer Array (SKA) will bring about a new era of radio astronomy by allowing 3D imaging of the Universe during Cosmic Dawn and Reionization. Machine learning promises to be a powerful tool to analyze the highly structured and complex signal, however accurate training datasets are expensive to simulate and supervised learning may not generalize. We introduce SKATR, a...
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Alessio Spurio Mancini18/06/2025, 16:36
In this talk I will introduce a new paradigm for cosmological inference, enabled by recent advances in machine learning and its underlying technology. By combining emulation, differentiable and probabilistic programming, scalable gradient-based sampling, and decoupled Bayesian model selection, this framework scales to extremely high-dimensional parameter spaces and enables complete Bayesian...
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Federico De Santi (Istituto Nazionale di Fisica Nucleare)18/06/2025, 16:39
In the next decade, the third generation of ground-based gravitational wave detectors, such as the european Einstein Telescope, is expected to revolutionize our understanding of compact binary mergers. With a 10 factor improvement in sensitivity and an extended range towards lower frequencies, Einstein Telescope will enable the detection of longer-duration signals from binary black hole and...
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Dr Sebastian Wagner-Carena (Flatiron / NYU)18/06/2025, 16:56
A major challenge in both simulation and inference within astrophysics is the lack of a reliable prior model for galaxy morphology. Existing galaxy catalogs are heterogeneous and provide an impure representation of underlying galaxy structures due to instrument noise, blending, and other observational limitations. Consequently, priors on galaxy morphology typically rely on either simplistic...
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Dr Donatella Genovese (Sapienza UniversitΓ di Roma)18/06/2025, 16:56
The Large Hadron Collider (LHC) at CERN generates vast amounts of data from high-energy particle collisions, requiring advanced machine learning techniques for effective analysis. While Graph Neural Networks (GNNs) have demonstrated strong predictive capabilities in high-energy physics (HEP) applications, their "black box" nature often limits interpretability. To address this challenge, we...
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Jan Steinheimer-Froschauer (Frankfurt Institute for Advanced Studies)18/06/2025, 16:56
In 2020, the German ministry of education and research launched an action plan to advance digitization in basic research. Alongside the plan a line of funding (ErUM-Data) was established to support interdisciplinary joint consortia that focus on progressing this process with concrete research projects. At the same time funding was allocated for the ErUM-Data HUB, as a networking and transfer...
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Yue Zhao (SURF, the Netherlands)18/06/2025, 16:59
High-Energy Physics experiments are rapidly escalating in generated data volume, a trend that will intensify with the upcoming High-Luminosity LHC upgrade. This surge in data necessitates critical revisions across the data processing pipeline, with particle track reconstruction being a prime candidate for improvement. In our previous work, we introduced "TrackFormers", a collection of...
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Satvik Mishra (SISSA)18/06/2025, 17:16
Modeling the distribution of neutral hydrogen is essential for understanding the physics of structure formation and the nature of dark matter, but accurate numerical simulations are computationally expensive. We describe a novel Variational Diffusion Model (VDM), built on a 3D CNN attention U-Net architecture, which we use in concert with the CAMELS simulation suite to generate accurate 21 cm...
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Mariagrazia Monteleone (Politecnico di Milano)18/06/2025, 17:16
Background: In High Energy Physics (HEP), jet tagging is a fundamental classification task that has been extensively studied using deep learning techniques. Among these, transformer networks have gained significant popularity due to their strong performance and intrinsic attention mechanisms. Furthermore, pre-trained transformer models are available for a wide range of classification...
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Davide Valsecchi (ETH ZΓΌrich, Switzerland), Prof. Marta Felcini (University College Dublin, School of Physics)18/06/2025, 17:19
The Matrix Element Method (MEM) is a powerful technique for computing the event-by-event likelihood for a given theory hypothesis, simultaneously accounting for both detector effects and theoretical models. Despite its strong theoretical foundation, MEM is seldom used in analyses involving final states with many jets due to the complex, multi-dimensional integrals required to accurately model...
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Fabio Novissimo (Ludwig-Maximilians-UniversitΓ€t MΓΌnchen)18/06/2025, 17:19
The Pixel Vertex Detector (PXD) is the innermost detector of the Belle II experiment. Information from the PXD, together with data from other detectors, allows to have a very precise vertex reconstruction. The effect of beam background on reconstruction is studied by adding measured or simulated background hit patterns to hits produced by simulated signal particles. This requires a huge sample...
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Guillermo Hijano (University of Zurich)18/06/2025, 17:19
Experimental studies of π-hadron decays face significant challenges due to a wide range of backgrounds arising from the numerous possible decay channels with similar final states. For a particular signal decay, the process for ascertaining the most relevant background processes necessitates a detailed analysis of final state particles, potential misidentifications, and kinematic overlaps...
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Berk TΓΌrk (Middle East Technical University)18/06/2025, 17:22
Alpha Magnetic Spectrometer (AMS-02) is a precision high-energy cosmic-ray experiment on the ISS operating since 2011 and has collected more than 228 billion particles. Among them, positrons are important to understand the particle nature of dark matter. Separating the positrons from cosmic background protons is challenging above 1 TeV. Therefore, we use state-of-the-art convolutional and...
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Ambre Visive (Nikhef - University of Amsterdam)18/06/2025, 17:22
Advances in Machine Learning, particularly Large Language Models (LLMs), enable more efficient interaction with complex datasets through tokenization and next-token prediction strategies. This talk presents and compares various approaches to structuring particle physics data as token sequences, allowing LLM-inspired models to learn event distributions and detect anomalies via next-token (or...
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David Calonge18/06/2025, 17:25
The WASA-FRS HypHI Experiment focuses on the study of light hypernuclei by means of heavy-ion induced reactions in 6Li collisions with 12C at 1.96GeV/u. It is part of the WASA-FRS experimental campaign, and so is the eta-prime experiment [1]. The distinctive combination of the high-resolution spectrometer FRagment Separator (FRS) [2] and the high-acceptance detector system WASA [3] is used....
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Michael Spannowsky19/06/2025, 09:00Plenary talk
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Mario Krenn19/06/2025, 10:15Plenary talk
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Anna Hallin (UniversitΓ€t Hamburg)19/06/2025, 11:00
Foundation models are machine learning models trained on large datasets, capable of being finetuned for a variety of downstream tasks. In a rather short time, several approaches to building foundation models for High energy physics have been presented. I will provide an overview of some of these approaches, and present some visions and open questions for the development of foundation models in HEP.
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Andrii Tykhonov19/06/2025, 11:25
DArk Matter Particle Explorer (DAMPE) is a pioneering instrument launched in space in 2015, designed for precise cosmic ray measurements reaching unprecedented hundreds of TeV in energy. One of the key challenges with DAMPE lies in cosmic ray data analysis at such high energies. It has been shown recently that deep learning can boost the experiment precision in regression (particle...
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Andrea Santamaria Garcia (University of Liverpool)19/06/2025, 15:00Plenary talk
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Uros Zivanovic (University of Trieste)19/06/2025, 16:15
Analyzing irregular and sparse time-series is a widespread problem in fundamental physics, astronomy, climate science and many other fields. This talk utilizes a novel Transformer-based architecture for multi-dimensional irregular time-series and sparse data, designed as a foundation model for general time-series interpolation, object classification, and representation learning. Our method...
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William Sutcliffe (University of Zurich)19/06/2025, 16:15
Graph neural networks (GNNs) have become state-of-the-art tools across diverse scientific disciplines due to their ability to model complex relationships in datasets that lack simple spatial or sequential structures. In this talk, we present recent advancements in the deep full event interpretation (DFEI) framework [GarcΓa PardiΓ±as, J., et al. Comput. Softw. Big Sci. 7 (2023) 1, 12]. The DFEI...
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Mirko Bunse (Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany)19/06/2025, 16:15
The resolution of any detector is finite, leading to distortions in the measured distributions. Within physics research, the indispensable correction of these distortions is know as Unfolding. Machine learning research uses a different term for this very task: Quantification Learning. For the past two decades, this difference in terminology (and some differences in notation) have prevented...
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Martina Mozzanica (University of Hamburg)19/06/2025, 16:15
Simulating showers of particles in highly-granular detectors is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models can enable them to augment traditional simulations and alleviate a major computing constraint.
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Recent developments have shown how diffusion based generative shower simulation... -
Tom Dooney19/06/2025, 16:35
Gravitational wave (GW) interferometers, such as LIGO, Virgo, and KAGRA, detect faint signals from distant astrophysical events. However, their high sensitivity also makes them susceptible to background noise, which can obscure these signals. This noise often includes transient artifacts called ``glitches", that can mimic genuine astrophysical signals or mask their true characteristics. Fast...
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Will Handley19/06/2025, 16:35
https://arxiv.org/abs/2501.03921
Simulation-based inference is undergoing a renaissance in statistics and machine learning. With several packages implementing the state-of-the-art in expressive AI [mackelab/sbi] [undark-lab/swyft], it is now being effectively applied to a wide range of problems in the physical sciences, biology, and beyond.
Given the rapid pace of AI/ML, there is little...
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Luca Clissa (University of Bologna; Istituto Nazionale di Fisica Nucleare)19/06/2025, 16:35
The ATLAS detector at CERNβs Large Hadron Collider (LHC) is a complex system composed of multiple subdetectors, each designed to capture complementary aspects of particle interactions. Thus, accurate understanding of the physical phenomena under study requires effectively combining information from these components.
This work focuses on the key challenge of associating data from the inner...
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Guillermo Franco AbellΓ‘n (GRAPPA Institute, University of Amsterdam)19/06/2025, 16:35
Simulation-based inference (SBI) allows amortized Bayesian inference for simulators with implicit likelihoods. However, some explicit likelihoods cannot easily be reformulated as simulators, hindering its integration into combined analyses within the SBI framework. One key example in cosmology is given by the Planck CMB likelihoods.
In this talk, I will present a simple method to construct...
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Stella Felice Schaefer (CERN / University of Hamburg)19/06/2025, 16:55
At the Phase-2 Upgrade of the CMS Level-1 Trigger (L1T), particles will be reconstructed by linking charged particle tracks with clusters in the calorimeters and muon tracks from the muon station. The 200 pileup interactions will be mitigated using primary vertex reconstruction for charged particles and a weighting for neutral particles based on the distribution of energy in a small area. Jets...
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Alessandro Bombini (Istituto Nazionale di Fisica Nucleare)19/06/2025, 16:55
The next generation of tracking detectors at upcoming and future high luminosity hadron colliders will be operating under extreme radiation levels with an unprecedented number of track hits per proton-proton collision that can only be processed if precise timing information is made available together with state-of-the-art spatial resolution. 3D Diamond pixel sensors are considered as a...
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Prof. Lukasz Graczykowski (Warsaw University of Technology (PL))19/06/2025, 16:55
Identifying products of ultrarelativistic collisions delivered by the LHC and RHIC colliders is one of the crucial objectives of experiments such as ALICE and STAR, which are specifically designed for this task. They allow for a precise Particle Identification (PID) over a broad momentum range.
Traditionally, PID methods rely on hand-crafted selections, which compare the recorded signal of...
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Renze Oosterhuis (Radboud University Nijmegen)19/06/2025, 17:15
Black holes represent some of the most extreme environments in the universe, spanning vast ranges in mass, size, and energy output. Observations from the Event Horizon Telescope (EHT) have provided an unprecedented opportunity to directly image black holes, with future plans aiming to create time-resolved movies of their evolution. To fully leverage these observations, we need theoretical...
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Takehiko Saito (High Energy Nuclear Physics Laboratory, RIKEN and GSI)20/06/2025, 09:00Plenary talk
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François Lanusse (CNRS)20/06/2025, 11:15Plenary talk
http://eiffl.github.io/talks/Cagliari2025
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Christoph Weniger (GRAPPA, University of Amsterdam), James Alvey (University of Cambridge)
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