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Antonis Kalogerakis, Head of the Institute of Theology & Ecology - Department of the OAC (Orthodox Academy of Crete), Dr Konstantinos Zompas, General Director of the OAC (Orthodox Academy of Crete)02/09/2026, 09:00Talk
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Prof. Pietro Vischia (Departamento de Física and ICTEA, Universidad de Oviedo)02/09/2026, 09:30
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Gordon Watts (University of Washington (US))02/09/2026, 10:00
AI agents are beginning to move beyond writing isolated pieces of code toward executing substantial portions of high-energy physics analyses. Starting from the basic agentic loop - setting goals, using tools, executing code, inspecting results, and iterating - we examine increasingly sophisticated demonstrations, including experiment-software tasks and end-to-end autonomous analyses. This is a...
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Muhammad Awais (University of Padua, Italy & LTU, Sweden)02/09/2026, 11:30
The design of modern scientific experiments for fundamental physics
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entails the choice of geometry, materials, detection techniques, and software reconstruction procedures governed by thousands of parameters. The full optimization of such systems can in most cases be achieved only if all these parameters are considered together in an end-to-end optimization procedure; in particular, hardware... -
Prof. Pietro Vischia (Departamento de Física and ICTEA, Universidad de Oviedo)02/09/2026, 12:00
Progress in physics has long been driven by ingenious experiments conceived by human experts.
Recently, AI-driven design methods have begun to move beyond tuning a handful of parameters to proposing entirely new experimental layouts. The discovered configurations often challenge established design conventions while matching or even exceeding the performance of human-designed setups.This...
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Vassil Vasilev (Princeton University (US))02/09/2026, 12:30
At the previous MODE workshop we presented the beginning of a collaborative effort to enable automatic differentiation (AD) in CMS Combine through RooFit's Clad-based AD framework. In this talk we report on the progress made since: the integration has moved from a proof of feasibility toward a usable capability for realistic CMS statistical models.
We will discuss the RooFit-side...
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Hipolito Arturo Riveros Guevara02/09/2026, 16:00
The optimization of charged-particle tracking detectors has traditionally been addressed through sequential workflows, in which the hardware design (sensor geometry, materials, electromagnetic fields, readout schemes) is frozen before the development of reconstruction software (seeding, pattern recognition, track fitting, alignment, identification, and background subtraction) begins. This work...
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Federico Nardi (Technical University of Munich, Universita' e INFN, Padova (IT))02/09/2026, 16:30
End-to-end approaches to experimental design studies can be developed thanks to the latest advancements in computing capabilities, in particular within Automatic Differentiation. We present here the case study of an Electromagnetic Calorimeter for a proposed future Muon Collider experiment, where muon decays along the beamline provide a significant challenge when attempting to precisely...
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Irene Fagnani02/09/2026, 17:00
Particle detectors are essential in a wide range of applications, from scientific instruments to medical and industrial imaging. Next generation scintillator detectors aim to achieve improved spatio-temporal resolution and particle identification capabilities, which require sub-nanosecond processing of the complex light patterns generated by particles passing through such materials. One...
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Daniele Massaro (CERN)02/09/2026, 17:30
The High-Luminosity LHC (HL-LHC) upgrade presents new challenges to the computing infrastructure of the LHC experiments. Monte Carlo event generation is projected to account for 10–20% of total CPU usage at ATLAS and CMS, making the speed-ups of these tools a more pressing requirement.
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To address this challenge, Madgraph5_aMC@NLO (with the MadMatrix plugin) developed an event-generation... -
Pablo Martinez Ruiz Del Arbol (Universidad de Cantabria and CSIC (ES)), Ruben Lopez Ruiz (Universidad de Cantabria and CSIC (ES))02/09/2026, 18:00
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Carlo Mancini Terracciano03/09/2026, 09:00Applications in Medical Physics, and Other ApplicationsTalk
Automated radiotherapy treatment planning remains an open challenge. Current optimisation workflows typically require substantial human intervention to select beam configurations, define planning strategies, and iteratively balance competing clinical objectives. Moreover, many relevant treatment parameters are either optimised separately or selected using heuristic or experience-driven...
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Nathan Campioni (La Sapienza, Università degli studi di Roma)03/09/2026, 09:30Applications in Medical Physics, and Other ApplicationsTalk
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. The advancements in quantum technologies have sparked interest in employing quantum systems as sensors, with superconducting quantum...
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Daniel Lanchares (Universidad de Oviedo - ICTEA)03/09/2026, 10:00Applications in Astro-HEP and Neutrino PhysicsTalk + poster
Over the last decades, the race for achieving a deeper understanding through more precise measurements has led physics experiments to grow increasingly complex, making their design and operation a superhuman task, even for large collaborations. Optimizing is one of the greatest hurdles of the design process, given the large dimensionality of the space of possible choices for geometry,...
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Barry Dillon (Ulster University)03/09/2026, 11:00
Spiking Neural Networks (SNNs) replace continuous activations with stateful neurons that integrate inputs over time and emit binary spikes when a threshold is crossed. This makes time part of the computation. I will present new SNN support in hls4ml, which translates models trained with PyTorch and snnTorch into clock-driven FPGA firmware. The implementation adds integrate-and-fire and leaky...
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Vassil Vasilev (Princeton University (US))03/09/2026, 11:30
End-to-end differentiability is changing how we think about analysis and
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experiment design: instead of scanning parameters by brute force and tuning calibrations by hand, we can let gradients of the likelihood flow back through reconstruction, digitization, and detector response, and optimize calibrations, selections, and eventually design for the physics objective directly. However, at the... -
Pablo Martinez Ruiz Del Arbol (Universidad de Cantabria and CSIC (ES)), Ruben Lopez Ruiz (Universidad de Cantabria and CSIC (ES))03/09/2026, 12:00
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Mr Kristian Tchiorniy (Technical University of Munich)03/09/2026, 16:00Applications in Astro-HEP and Neutrino PhysicsTalk + poster
The geometrical layout of any experiment or detector can have a large impact on its ability to produce meaningful outcomes for physics. Oftentimes we see that optimal geometries can be unintuitive. Studying and optimizing this is therefore essential. This has become a relevant topic for the optimization of cubic-kilometer-scale neutrino optical telescopes that are yet to be built. In...
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Oliver Janik03/09/2026, 16:30Applications in Astro-HEP and Neutrino PhysicsTalk + poster
Optimizing the design of a neutrino telescope requires a cost function that quantifies detector performance. Generic proxies such as angular resolution or background rejection are convenient, but do not necessarily track the sensitivity of a specific analysis, which depends on the physics goal, be it source discovery or a flux measurement.
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The natural cost function is therefore the... -
Vincent Riechers (Universite de Geneve (CH))03/09/2026, 17:00Applications in Astro-HEP and Neutrino PhysicsTalk + poster
We present an end-to-end optimization pipeline that designs the geometry of a tungsten-based silicon pixel detector to maximize a downstream physics objective: the separation of tau-neutrino charged-current interactions from electron- and muon-neutrino backgrounds. For candidate detector designs, a deep-learning classifier is trained on the detector hits, and a surrogate model over the...
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Kalliopi Petrou (Artist), Tommaso Dorigo (INFN Padova, Luleå University of Technology, MODE Collaboration, Universal Scientific Education and Research Network)03/09/2026, 19:00
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Pablo Martinez Ruiz Del Arbol (Universidad de Cantabria and CSIC (ES)), Ruben Lopez Ruiz (Universidad de Cantabria and CSIC (ES))04/09/2026, 09:00
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Jean-Marco Alameddine04/09/2026, 11:00
Muon scattering tomography (MST) is a non-invasive imaging technique that utilizes the naturally occurring cosmic-ray muon flux. Although MST is now a well-established method, further improvements, particularly in image reconstruction, are necessary to fully exploit its potential. One promising approach is the use of statistical models for image reconstruction, in which a likelihood function...
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Angel Bueno04/09/2026, 11:30
Deep learning-based anomaly detection in cargo containers using muon tomography is limited by the scarcity of anomalous data samples and the domain gap between simulation and real detector data. This research proposes a methodology to transfer anomaly detection from simulation to operational cargo container inspection. To this end, we exploit neural density estimators to approximate the...
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Konstantin Borozdin04/09/2026, 12:00
Muon scattering tomography uses the natural cosmic-ray muon flux to probe dense structures without delivering ionizing dose, but its performance in low-contrast biological media remains poorly quantified. We present a simulation-based feasibility study of cosmic-ray muon tomography for medical imaging, with emphasis on pediatric spine monitoring, and connect it to human-phantom results showing...
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Roland Grinis04/09/2026, 12:30
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Martin Ravn05/09/2026, 09:00Applications in Astro-HEP and Neutrino PhysicsTalk + poster
In-ice radio detection is a rapidly advancing field, aiming to observe the first ultra-high-energy neutrinos within the coming years. With several experiments under construction and in the planning stages, like RNO-G and the IceCube-Gen2 radio array, it is crucial to systematically explore potential detector designs to maximize the performance of the experiments and their scientific...
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Izan Florez05/09/2026, 09:30
Abstract: Optimizing the detector array layout for the TAMBO experiment across the Colca Valley walls poses a high-dimensional placement problem intractable through conventional simulation methods. To address this, we propose an end-to-end differentiable optimization pipeline for the array layout. Built on a flow-matching shower surrogate trained on CORSIKA simulations, the pipeline chains a...
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Nicolai Weitkemper05/09/2026, 10:00Applications in Astro-HEP and Neutrino PhysicsTalk + poster
Ultra-high-energy (EeV-scale) neutrinos provide a unique probe of the most energetic astrophysical accelerators and of particle interactions at energies far beyond those accessible in terrestrial experiments. Detecting these neutrinos requires instrumenting enormous target volumes, making in-ice radio arrays a promising approach due to the long attenuation length of radio signals in glacial...
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Miaoyuan Liu (Purdue University (US))05/09/2026, 11:00
Custom ASIC accelerators offer significant power and performance advantages for machine learning in scientific and edge computing; a driving example is superconducting qubit readout, where moving real-time classification of qubit states from room-temperature FPGAs into the cryostat requires custom ASICs on cryo-compatible technology nodes.
However, obtaining accurate area and timing...
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Jeffrey Krupa (SLAC)05/09/2026, 11:30Talk
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178. 3D Scene Reconstruction Across Different Physical Modalities with Probabilistic Particle FieldsFelix Sattler (Detusches Zentrum für Luft- und Raumfahrt e.V. (German Aerospace Center))05/09/2026, 12:00
Inverse problems such as computed tomography, optical inverse rendering, thermal imaging, and muon tomography arise in a wide range of scientific, medical, and security applications and are usually solved with highly specialized algorithms. By approaching these problems from a physical perspective and reformulating them in terms of particle transport and interactions, we formulate a unified...
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William O’Donnell05/09/2026, 12:30
Condition monitoring and inspection (CM&I) of nuclear wastes is required to demonstrate that wastes evolve as expected and can be safely stored in the long term. Muon scattering tomography, performed using scintillating fibre tracking detectors, has been identified as a viable approach to perform non-destructive CM&I.
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However, the monitoring of nuclear wastes can be difficult due to the high... -
Lorenzo Pepa05/09/2026, 16:00
In the context of the PHINDER project (Picosecond-scale Photonic Heterogeneous Integrated Neuromorphic Detector), we present a differentiable model describing the generation of scintillation light and its propagation, collection, and focusing onto nanowires, which constitute the sensing elements of a neuromorphic readout system. The model is designed to reproduce the main optical processes...
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Maria Pereira Martinez (Universidade de Santiago de Compostela (ES))05/09/2026, 16:30
We present the optimization of a neutron tomography system based on a stack of Parallel-Plate Avalanche Counters with Optical Readout (O-PPACs). Building on previous work that optimized the design of a single O-PPAC using differentiable programming, we extend this framework to the complete detector stack, covering both simulation and reconstruction within the differentiable pipeline. We employ...
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Luigi Favaro (Universite Catholique de Louvain (UCL) (BE))05/09/2026, 17:00
Simulating the detector response of collider events is a computationally expensive task that involves optimizing thousands of parameters to best mimic real-world data. For parametrized fast simulators, this reduces to fitting the coefficients of predefined smearing functions, a task well-suited to gradient-based optimization via automatic differentiation. Using Delphes3 as a case study, I will...
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Gordon Watts (University of Washington (US))05/09/2026, 17:30
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Pablo Martinez Ruiz Del Arbol (Universidad de Cantabria and CSIC (ES)), Ruben Lopez Ruiz (Universidad de Cantabria and CSIC (ES))05/09/2026, 18:00
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Richard Hildebrandt06/09/2026, 09:00
We introduce a new strategy for compositional neural surrogates for radiation-matter interactions, a key task spanning domains from particle physics through nuclear and space engineering to medical physics. Exploiting the locality and the Markov nature of particle interactions, we create a \emph{next-particle prediction} kernel using hybrid discrete-continuous transformer models based on...
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Stephen Casey (University of Miami)06/09/2026, 09:30
Presented here is a method for model discovery and intelligent sampling of physical systems that uses recursive generation of cellular modules combined with Ant Colony Optimization (ACO). The program begins with a random sample of data and a single computational module, somewhat analogous to a single cell. This module contains several subcomponents, called organelles, that perform simple...
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Robin Brase06/09/2026, 10:00
Autonomous vehicles increasingly operate in large-scale environments that combine wide-open regions with confined, cluttered areas. This is exemplified by maritime missions for monitoring and inspecting critical infrastructure. Such applications require global path planning methods that can handle environments with strongly varying spatial scales. Narrow passages require fine-grained...
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Zhujun Fang06/09/2026, 11:00
The Super Tau-Charm Facility (STCF) is a next-generation electron-positron collider proposed by China. The STCF will operate in the center-of-mass energy range of 2 to 7 GeV, with a design luminosity of 0.5×10³⁵ cm⁻²s⁻¹ at 4 GeV. While the high luminosity significantly enhances its physics potential, it also introduces considerable challenges, including elevated background levels and large...
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Gamze Sokmen (Centre National de la Recherche Scientifique (FR))06/09/2026, 11:30
The High-Luminosity LHC (HL-LHC) will significantly increase event rates and pileup, requiring reconstruction algorithms that remain efficient, scalable and robust in highly populated detector environments. To address this challenge, CMS will replace its endcap calorimeters with the High Granularity Calorimeter (HGCAL), a highly segmented calorimeter providing fine three-dimensional spatial...
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Francesco Ferranti06/09/2026, 12:00Applications in Medical Physics, and Other ApplicationsTalk
Miniaturized imaging probes play a critical role in the development of fiber-optic microendoscopy. Scanning fiber endoscopy techniques, incorporate compact distal scanning probes paired with micro-objective elements such as gradient refractive index (GRIN) lenses. However, systems relying solely on GRIN optics often experience off-axis aberrations, which reduce image quality particularly...
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Jan Eckwert (Technical University of Munich)06/09/2026, 12:30Applications in Medical Physics, and Other ApplicationsTalk + poster
Machine-learning interatomic potentials offer a flexible and efficient way to represent complex potential-energy surfaces and enable molecular simulations at scales beyond those accessible to electronic-structure methods. They are commonly trained in a bottom-up manner using labeled reference data, such as energies and forces obtained from density functional theory. While such models can...
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Yuliia Maidannyk (Université Paris-Saclay (FR))06/09/2026, 16:00
The reconstruction of electrons and photons in the CMS Electromagnetic Calorimeter (ECAL) currently relies on a geometrical clustering algorithm called PFClustering. While it is efficient for isolated particles, it has a limited ability to resolve close-by showers and mitigate detector noise, which reduces the sensitivity of physics analyses and will worsen with detector ageing. We present...
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Dr Florian Bury (University of Bristol)06/09/2026, 16:30
Recent advances in generative machine learning have opened new avenues for detector optimization by fully exploiting high-dimensional parameter spaces. In this work, we build on the AI Detector Optimization (AIDO) framework and extend it to include particle identification using calorimeter shower shapes, as well as more advanced exploration strategies. In addition, preliminary work involving...
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Ozgur Sahin (Université Paris-Saclay (FR))06/09/2026, 17:00
Calorimeter reconstruction and simulation are usually developed separately for each detector geometry, segmentation and physics task. This often requires dedicated algorithms for energy regression, position reconstruction, clustering, shower shape prediction, leakage correction and simulation. We present CaloFound, a foundation model approach for calorimeters based on geometry standardization...
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Prof. Pietro Vischia (Departamento de Física and ICTEA, Universidad de Oviedo)06/09/2026, 17:30
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Mirmohammadreza bagherzadehhir (Faculty of Physics, University of Guilan, PhD student)
The design optimization of particle detectors for future High-Energy Physics (HEP) experiments remains computationally challenging due to the reliance on repeated Monte Carlo simulations and black-box optimization techniques, which become prohibitively expensive in high-dimensional detector parameter spaces. Moreover, existing optimization strategies fail to efficiently exploit gradient...
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Izan FlorezApplications in Astro-HEP and Neutrino PhysicsPoster
Abstract: Extensive air shower simulations from primary cosmic rays are traditionally performed using computationally intensive Monte Carlo methods, consuming significant resources in astroparticle physics. To address this, we propose a continuous normalizing flow matching model for the TAMBO experiment to accelerate simulations. Trained on high-energy showers generated with CORSIKA, the model...
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Francesca Nicolanti
In classical mechanics, the physical trajectory of a system is the one that makes the action stationary. Strang, Caruso and Greydanus (arXiv:2303.02115) proposed to find such paths numerically by discretising the action and minimising it directly with gradient descent, bypassing the analytical derivation of the Euler-Lagrange equations. While demonstrating the viability of this idea on several...
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