Connecting The Dots 2025

Contribution List

93. Welcome

Koji Terashi (University of Tokyo (JP))

10/11/2025, 09:00

92. The CERN Quantum Technology Initiative: initial results and research perspective on Quantum Computing for HEP

Dr Sofia Vallecorsa (CERN)

10/11/2025, 09:10

Talk

Session

The CERN Quantum Technology Initiative (QTI) was launched in 2020 with the aim of investigating the role that quantum technologies could have within the High Energy Physics (HEP) research program. During this initial exploratory phase a set of results were gathered, outlining benefits, constraints and limitations of introducing technologies in different HEP domains, from advanced sensor for...

43. Scalable Track Reconstruction at the LHC with QAOA

Miriam Lucio Martinez (Univ. of Valencia and CSIC (ES))

10/11/2025, 09:50

Talk

Session

Reconstructing the trajectories of charged particles as they traverse several detector layers is a key ingredient for event reconstruction at LHC and virtually any particle physics experiment. The limited bandwidth available, together with the high rate of tracks per second O($10^{10}$) - where each track consists of a variable number of measurements - makes this problem exceptionally...

45. Quantum-Enhanced Graph Neural Networks for Particle Tracking in High Energy Physics

Santosh Parajuli (Univ. Illinois at Urbana Champaign (US))

10/11/2025, 10:15

Talk

Session

Graph Neural Networks (GNNs) have emerged as powerful tools for particle tracking in High Energy Physics (HEP), effectively modeling the complex relational structure of detector hits. Recent progress in quantum computing raises the possibility that quantum circuits, leveraging entanglement and superposition, could enhance GNNs by capturing intricate patterns in tracking data. However, the...

94. Utilizing quantum computers for HEP simulations and event generation

Yutaro Iiyama (University of Tokyo (JP))

10/11/2025, 11:10

Session

Quantum computing is arguably the only viable approach to simulating dynamical phenomena of general quantum systems. Inspired by the rapid advancement of quantum computing capabilities in recent years, there is a highly active community of theoretical and experimental high-energy physicists who seek to understand which HEP observables can be calculated through quantum simulations and how such...

85. Track reconstruction at collider experiments using quantum-inspired simulated annealing

Daiya Akiyama (Waseda University (JP))

10/11/2025, 11:50

Talk

Session

Quantum computing technologies are emerging as promising tools to enhance computational speed and reduce resource demands in high-energy accelerator experiments. In particular, GPU-based quantum annealing simulations inspired by quantum annealing principles have reached a practical level of hardware development. They are now being explored for their potential in track reconstruction tasks.
In...

77. Quantum Chebyshev Probabilistic Models for Fragmentation Functions

Dr Sofia Vallecorsa (CERN)

10/11/2025, 12:15

Talk

Session

We propose a quantum protocol for efficiently learning and sampling multivariate probability distributions that commonly appear in high-energy physics. Our approach introduces a bivariate probabilistic model based on generalized Chebyshev polynomials, which is (pre-)trained as an explicit circuit-based model for two correlated variables, and sampled efficiently with the use of quantum...

95. Cutting‑Edge AI/ML for HEP

Masahiro Morinaga (University of Tokyo (JP))

10/11/2025, 14:00

Session

21. Token-Based Transformer Models for Pattern Recognition on Point Clouds

Xiangyang Ju (Lawrence Berkeley National Lab. (US))

10/11/2025, 14:40

Talk

Session

Building on the success of TrackingBERT (arXiv:2402.10239) and TrackingSorter (arXiv:2407.21290) , we propose a unified approach to address the track finding problem and extend beyond it. Our method integrates the latent representation of detector hits learned from TrackBERT as additional feastures in the TrackSorter algorithm.Furthermore, we replace the detector module ID-based tokenization...

83. Semi-Supervised Transfer Learning with Convolutional Autoencoders for Hybrid Pixel Detectors

Mr Tomáš Čelko (Charles University)

10/11/2025, 15:05

Talk

Session

Hybrid pixel detectors such as Timepix3 and Timepix4 enable pixel-level resolution of individual particle interactions, where each event manifests as a cluster or track spanning multiple pixels. Analyzing these clusters allows for the estimation of key particle parameters, including type, initial energy, and angle of incidence. However, such ground-truth parameters are typically unavailable...

81. ML-Assisted Tracking in the ATLAS Muon Spectrometer

Jonathan Renusch (CERN | Technical University of Munich (TUM))

10/11/2025, 15:30

Talk

Session

The High Luminosity Large Hadron Collider (HL-LHC), scheduled to begin operation after 2030, will increase the number of proton-proton collisions per event from approximately 60 to up to 200. This rise in interaction density will substantially elevate the occupancy within the ATLAS Muon Spectrometer, necessitating more efficient and robust real-time data processing strategies for the Event...

4. Beyond-CMOS Systems for Fast Machine Learning in Physics

Dr Valerio Ippolito (INFN Sezione di Roma (IT))

10/11/2025, 16:30

Talk

Session

The implementation of neural networks and artificial intelligence on hardware accelerators is a key element for the development of the trigger strategy of future high-energy physics detectors. In parallel, the development of hardware technologies capable of maximizing AI performance and adapting to its computational needs is crucial, particularly in scenarios requiring the efficient processing...

11. GPT-like transformer model for silicon tracking detector simulation

Tadej Novak (Jozef Stefan Institute (SI))

10/11/2025, 16:55

Talk

Session

Simulating physics processes and detector responses is essential in high energy physics but accounts for significant computing costs. Generative machine learning has been demonstrated to be potentially powerful in accelerating simulations, outperforming traditional fast simulation methods. While efforts have focused primarily on calorimeters initial studies have also been performed on silicon...

79. DNN-based particle flow and jet flavor tagging for Higgs factories

Taikan Suehara (ICEPP, The University of Tokyo (JP))

10/11/2025, 17:20

Talk

Session

Electron-positron Higgs factories, such as ILC and FCCee, are the next-generation collider projects to reveal properties of Higgs and other particles in great details to observe BSM effects. Proposed detectors are designed to maximize obtained information from incident particles with highly granular detector elements. DNN-based reconstruction algorithms are critical to derive properties of...

111. Rapid ML inference using logic gate neural nets

Liv Helen Vage (Princeton University (US))

10/11/2025, 17:45

Talk

Session

Fast machine learning (ML) inference is of great interest in the HEP community, especially in low-latency environments like triggering. Faster inference often unlocks the use of more complex ML models that improve physics performance, while also enhancing productivity and sustainability. Logic gate networks (LGNs) currently achieve some of the fastest inference times for standard image...

96. Welcome

Masaya Ishino (University of Tokyo (JP))

11/11/2025, 09:00

97. Introduction

Ryu Sawada (University of Tokyo (JP))

11/11/2025, 09:10

22. A Fully GPU-Based Track Reconstruction Pipeline for HEP Experiments

Neza Ribaric (Duke University (US))

11/11/2025, 09:20

Talk

Session

The ATLAS-internally-know G-200 pipeline represents a major milestone in the evolution of online track reconstruction for the experiment, enabling the entire reconstruction chain at trigger level to be executed on GPU architectures. G-200 is the ATLAS ITk-specific implementation of the Traccc framework, which is part of ACTS GPU R&D project and designed to be detector agnostic. Traccc...

66. Track fitting at the full LHC collision frequency: Design and performance of the GPU-based Kalman Filter at the LHCb experiment

Lennart Uecker (Heidelberg University (DE))

11/11/2025, 09:50

Talk

Session

The LHCb experiment at the Large Hadron Collider (LHC) operates a fully software-based trigger system that processes proton-proton collisions at a rate of 30 MHz, reconstructing both charged and neutral particles in real time. The first stage of this trigger system, running on approximately 500 GPU cards, performs a track pattern recognition to reconstruct particle trajectories with low...

10. Improvements of the ALICE GPU TPC tracking and GPU framework for online and offline processing of Run 3 Pb—Pb data

David Rohr (CERN)

11/11/2025, 10:20

Talk

Session

ALICE is the dedicated heavy ion experiment at the LHC at CERN and records lead-lead collisions at a rate of up to 50 kHz in LHC Run 3. To cope with such collision and data rates, ALICE uses a GEM TPC with continuous readout and a GPU-based online computing farm for data compression. Operating the first GEM TPC of this size with large space charge distortions due to the high collision rate has...

26. FPGA-accelerated track reconstruction and clustering using classical and machine-learning approaches for the ATLAS Event Filter

Julian Wollrath (CERN)

11/11/2025, 11:20

Talk

Session

As part of the ATLAS Phase-II upgrade, the Event Filter (EF) Tracking project is exploring heterogeneous computing systems. This EF system will receive events selected by the L0 trigger and process the data from the ATLAS detector at a maximum rate of 1 MHz. FPGA-based implementations are being developed for various stages of track reconstruction algorithms such as pixel clustering, strip...

35. The Tiny Triplet Finder - a low silicon resource track finding scheme and its test implementation in FPGA

Dr Jinyuan Wu (Fermi National Accelerator Lab. (US))

11/11/2025, 11:50

Talk

Session

In high-energy physics experiment trigger systems, track segment seeding is a resource-intensive function. The primary reason lies in the high computational complexity of the segment-finding process— O(n³) in software implementations using nested loops, and O(n) × O(N²) in typical FPGA implementations, where n is the number of hits per detector layer in an event, and N is the number of bins...

70. An ultrafast FPGA fitter for the LHCb Downstream Tracker

Federico Lazzari (Universita di Pisa & INFN Pisa (IT))

11/11/2025, 12:20

Talk

Session

After an intensive R&D programme, LHCb approved building of the Downstream Tracker (DWT): a system based on the ''artificial retina'' to pre-reconstruct tracks in the SciFi (the detector downstream to the magnet) at readout level during Run 4. Running before any trigger level, it has to process events at the average LHC crossing rate of 30 MHz.

The ''artificial retina'' is an extremely...

76. A Geometry Agnostic Heterogenous Framework for Track Reconstruction for HEP Experiments

Adriano Di Florio (CC-IN2P3)

11/11/2025, 12:40

Talk

Session

The future development projects for the Large Hadron Collider towards HL-LHC will constantly bring nominal luminosity increase, with the ultimate goal of reaching, e.g., a peak luminosity of $5 \cdot 10^{34} cm^{−2} s^{−1}$ for ATLAS and CMS experiments. This rise in luminosity will directly result in an increased number of simultaneous proton collisions (pileup), up to 200, that will pose new...

9. Optimization and Outer Tracker Extension of the Cellular Automaton Algorithm for CMS Phase-2 Tracking

Jan Schulz (Rheinisch Westfaelische Tech. Hoch. (DE))

11/11/2025, 14:30

Talk

Session

The Phase-2 Upgrade for the CMS experiment at the HL-LHC brings with it new and improved detectors, including a new tracker. It will provide the means to reconstruct higher-quality tracks in the pixel detector, especially in the endcaps (pseudorapidity |η| ≳ 2), by allowing to record hits in many more layers than what is currently possible. The Phase 2 CMS tracking at the HLT currently plans...

31. TICL: A Reconstruction Framework for the CMS Phase-2 High-Granularity Calorimeter Endcap

Felice Pantaleo (CERN)

11/11/2025, 15:00

Talk

Session

The High-Luminosity LHC (HL-LHC) era presents new challenges for the CMS detector. To address them, the endcap calorimeters will be replaced with a High-Granularity Calorimeter (HGCAL) that provides exceptional spatial resolution and precise timing. A new reconstruction framework, The Iterative Clustering (TICL), is being developed in CMS Software (CMSSW) to exploit HGCAL’s features and...

8. Muon Tracking at the CMS High-Level Trigger for the HL-LHC Upgrade

Luca Ferragina (Universita Di Bologna (IT))

11/11/2025, 15:30

Talk

Session

The High-Luminosity Upgrade for the LHC is rapidly approaching and the CMS experiment is undergoing fundamental changes to take advantage of the new physics possibilities offered by the collider’s upgrade. In this context, the event reconstruction, both offline and at the High-Level Trigger (HLT), is undergoing significant changes to fully exploit the detectors’ upgrades while aiming to...

29. Track Reconstruction with the ATLAS Inner Tracker and ACTS at the High-Luminosity LHC

Noemi Calace (CERN)

11/11/2025, 16:20

Talk

Session

The High-Luminosity LHC (HL-LHC) will significantly increase the demands on charged particle track reconstruction, with proton–proton collisions expected to reach up to 200 simultaneous interactions per bunch crossing and heavy-ion collisions producing similarly high track densities. In preparation for Run 4, the ATLAS experiment is replacing its current Inner Detector with a new all-silicon...

98. Challenges Deploying a Hybrid PV-finder Algorithm for Primary Vertex Reconstruction in LHCb’s GPU-Resident First Level Trigger

Mohamed Elashri (University of Cincinnati)

11/11/2025, 16:50

Talk

Poster lightning talks

The PV-finder algorithm employs a hybrid deep neural network to reconstruct primary vertex positions (PVs) in proton-proton collisions at the LHC. The algorithm was originally developed for use in LHCb, but it has been adapted successfully for use in the much higher pile-up environment of ATLAS. PV-finder integrates fully connected layers that do track-by-track calculations with a...

110. GNN-based Track Finding for a new multiple TPC detector

Shujie Li (Berkeley Lab)

11/11/2025, 16:53

Talk

Poster lightning talks

The Tagged Deep Inelastic Scattering (TDIS) experiment at Jefferson Lab studies nucleon mesonic content by detecting low-momentum recoil hadrons with a multiple Time Projection Chamber (mTPC) in coincidence with scattered electrons. The expected high rate, high occupancy environment poses significant challenges to traditional track finding algorithms. In this talk, I will present our...

100. Advances in Low-Energy Particle Track Reconstruction with Interaction Graph Networks at the PANDA experiment.

Nikolai in der Wiesche (Institute of Nuclear Physics, University of Münster)

11/11/2025, 16:56

Talk

Poster lightning talks

The success of neural network based tracking algorithms for high energy colliders has prompted us to explore the merits of these methods for tracking in the lower energy regime of the PANDA experiment. In this talk, I will present the current state of a tracking pipeline that has been adapted from the Exa.TrkX group and that has an interaction graph neural network at its core. It has an...

101. Machine Learning implementation in Front-End Electronics of Belle II Central Drift Chamber for cross-talk noise reduction

Yun-Tsung Lai

11/11/2025, 16:59

Talk

Poster lightning talks

Central Drift Chamber (CDC) in the Belle II experiment is one of the charged tracking device for both offline and real-time hardware trigger systems. Belle II CDC has been using a Front-End Electronics (FEE) device based on Xilinx Virtex-5 FPGA to record the digitized waveform of anode wires and to deliver the data to both central data acquisition system and hardware trigger system. In the...

103. Graph Neural Network Acceleration on FPGAs for Real-Time Muon Triggering at the HL-LHC

Davide Fiacco (Sapienza Universita e INFN, Roma I (IT))

11/11/2025, 17:02

Poster lightning talks

The upcoming High Luminosity phase of the Large Hadron Collider requires significant advancements in real-time data processing to handle the increased event rates and maintain high-efficiency trigger decisions. In this work, we explore the acceleration of graph neural networks on field-programmable gate arrays for fast inference within future muon trigger pipelines with O(100) ns latencies....

104. Application of Point Cloud Classification to Particle Identification in the SuperFGD neutrino detector

Hikaru Tanigawa (KEK)

11/11/2025, 17:05

Poster lightning talks

The T2K experiment is a long-baseline neutrino oscillation experiment located in Japan that has major goal to find hints of CP-violation in the leptonic sector. To further improve its sensitivity, it is crucial to precisely measure electron neutrino cross section at the near detector. This measurement has been challenging, as the T2K beam composition is mostly muon neutrinos, requiring a...

105. Development and performance of electron reconstruction with Acts and the OpenDataDetector (ODD)

Andreas Stefl (CERN)

11/11/2025, 17:08

Poster lightning talks

Over the last years, a general purpose track finding algorithm based on the combinatorial Kalman filter (CKF) has been developed for the Acts toolkit - a community-driven project that provides experiment-independent tracking algorithms written in modern C++. It has been validated and optimized with the OpenDataDetector (ODD), and the ATLAS Phase-2 Inner Tracker (ITk). The CKF shows good...

49. Graph-Based Multi-Modal Track Reconstruction in the Belle II Drift Chamber and Vertex Detectors

Lea Reuter

12/11/2025, 09:00

Talk

Session

Large backgrounds and the degradation of detector gain impact the track finding in the Belle II central drift chamber, reducing both purity and efficiency in events. This necessitates the development of new track finding algorithms to mitigate the reduction in detector performance.
Our implementation of an end-to-end multi-track reconstruction algorithm for the Belle II experiment at the...

63. Exploring the potential of cooperative track building

Liv Helen Vage (Princeton University (US))

12/11/2025, 09:30

Talk

Session

Efficient and accurate charged particle tracking is one of the most computationally demanding challenges at the High-Luminosity Large Hadron Collider (HL-LHC). Graph neural nets (GNNs) have emerged as one of the more promising solutions as they exploit correlations between nearby hits and tracks rather than treating each track independently. However, tracking GNNs can be slow to train and...

20. Expected physics and computing performance of the ATLAS ITk GNN-based Track Reconstruction Chain

Benjamin Huth (CERN)

12/11/2025, 10:00

Talk

Session

The HL-LHC upgrade of the ATLAS inner detector (ITk) brings an
unprecedented challenge, both in terms of the large number of silicon
cluster readouts and the throughput required for budget-constrained
track reconstruction. Applying Graph Neural Networks (GNNs) has been
shown to be a promising solution to this problem with competitive
physics performance at sub-second inference time.

In...

88. Uncertainty Quantification in an ML Pattern Recognition Pipeline

Lukas Peron (ENS Paris)

12/11/2025, 11:00

Talk

Session

Geometric learning pipelines have achieved state-of-the-art performance in High-Energy and Nuclear Physics reconstruction tasks ike flavor tagging and particle tracking [1]. Starting from a point cloud of detector or particle-level measurements, a graph can be built where the measurements are nodes, and where the edges represent all possible physics relationships between the nodes. Depending...

87. Efficient Point Transformer for Charge Particles Track Reconstruction

Yuan-Tang Chou (University of Washington (US))

12/11/2025, 11:30

Talk

Session

Charge particle track reconstruction is the foundation of high-energy experiments. Yet, it's also the most computationally expensive part of the particle reconstruction. It's the backbone of many downstream reconstruction algorithms. The innovation in tracking reconstruction with graph neural networks (GNNs) has shown the promising capability to cope with the computing challenges posed by the...

38. Transformer for seed reconstruction in ACTS

Corentin Allaire (IJCLab, Université Paris-Saclay, CNRS/IN2P3)

12/11/2025, 12:00

Talk

Session

Reconstructing particle trajectories is a significant challenge in most particle physics experiments and a major consumer of CPU resources. It can typically be divided into three steps: seeding, track finding, and track fitting. Seeding involves identifying potential trajectory candidates, while track finding entails associating detected hits with the corresponding particle. Finally, track...

78. Tracking in Dense Environments with Transformers

Max Hart (University College London (GB))

12/11/2025, 12:30

Talk

Session

Highly boosted jets represent some of the most challenging tracking conditions in modern collider experiments. At higher momentum, particles become increasing collimated in the jet core, which results in greater track density and a large degree of multiple particles sharing a single cluster, which results in traditional tracking approaches suffering either in terms of either decreased...

36. Fast Track Fitting and Anomaly Detection with Machine Learning

Makayla Vessella 🐏 (University of California Irvine (US))

12/11/2025, 14:30

Talk

Session

Accurate and efficient particle tracking is a crucial component of precise measurements of the Standard Model and searches for new physics. This task consists of two main computational steps: track finding, the identification of a subset of all hits that are due to a single particle; and track fitting, the extraction of crucial parameters such as direction and momenta. Novel solutions to...

2. Charged Particle Tracking with Reinforcement Learning for Drift Chambers

Yao Zhang

12/11/2025, 15:00

Talk

Session

Charged particle tracking is a critical task in high-energy physics. In this work, we propose using reinforcement learning (RL) to the reconstruction of charged particle trajectories in drift chambers. By framing the tracking problem as a sequential decision-making process constrained by physical interactions, RL enables the development of more efficient and adaptive tracking algorithms. This...

3. GNN Track Reconstruction of Generalized Non-Helical Signatures

Levi Condren (University of California Irvine (US))

12/11/2025, 15:30

Talk

Session

Standard tracking pipelines are only capable of finding particles with helical trajectories, yet many theories of new physics predict particles with non-helical trajectories. Graph neural network based trackers have recently been shown to be able to find non-helical tracks when trained on specific examples, such as quirks. But unanticipated new physics may feature unexpected trajectories,...

71. New Approaches of End-to-end GNN Track Reconstruction Based on Spacepoint Doublet Embedding and Double Metric Learning for Building Directed Graphs with Chain Connections for the ATLAS ITk Detector

Jay Chan (Lawrence Berkeley National Lab. (US))

12/11/2025, 16:20

Talk

Session

Graph construction is an essential step in the Graph Neural Network (GNN) based tracking pipelines. The goal of the graph construction is to construct a graph that contains only true edge connections between nodes (detector spacepoints). A promising approach for the graph construction is through the metric learning, where a node embedding space is learned, and nodes are connected according to...

46. LHCb Tracking Reconstruction and Ghost Rejection at 30 MHz

Da Yu Tou (Tsinghua University (CN))

12/11/2025, 16:40

Talk

Session

The new fully software-based trigger of the LHCb experiment operates at a 30 MHz data rate and imposes tight constraints on GPU execution time. Tracking reconstruction algorithms in this first-level trigger must efficiently select detector hits, group them, build tracklets, account for the LHCb magnetic field, extrapolate and fit trajectories, and select the best track candidates to filter...

41. Application of ACTS to the CEPC Reference Detector

Yizhou Zhang (Institute of High Energy Physics)

12/11/2025, 17:10

Talk

Session

The Circular Electron Positron Collider (CEPC) is a proposed collider designed to investigate Higgs boson properties with precision and explore new physics. Its Technical Design Report (TDR) is currently under development, focusing on a reference detector. The tracking detector system of this reference detecor includes a vertex detector (VTX), an inner silicon tracker (ITK), a time projection...

37. Antihydrogen annihilation reconstruction in the ALPHA apparatus at CERN

Andrea Capra (TRIUMF (CA))

13/11/2025, 09:00

Talk

Session

The ALPHA collaboration at CERN operates two machines dedicated to testing fundamental symmetries using trapped antihydrogen atoms. The ALPHA-2 experiment was built in 2012 and is optimized to perform high-precision spectroscopy on antihydrogen [1]. The ALPHA-g apparatus, completed in 2021, is designed to measure its gravitational acceleration [2]. In both instances, the physics signal is...

64. Tracking Cosmic-Ray Nuclei with the RadMap Telescope

Luise Meyer-Hetling (Technical University of Munich)

13/11/2025, 09:30

Talk

Session

The RadMap Telescope is a compact radiation monitor that can characterize the radiation environment aboard spacecraft and determine the biologically relevant dose received by astronauts. Its main sensor is a tracking calorimeter made from 1024 scintillating-plastic fibers of alternating orientation read out by silicon photomultipliers. It allows the three-dimensional tracking and...

34. Ultra-displaced dimuon vertexing for LLP signatures in ATLAS

Makayla Vessella 🐏 (University of California Irvine (US))

13/11/2025, 10:00

Talk

Session

Many beyond the Standard Model (BSM) theories, such as hidden sector models, heavy neutral leptons, and neutral naturalness frameworks, predict invisible long-lived particles that travel macroscopic distances before decaying into visible Standard Model particles with a common spatial origin. Fast and accurate reconstruction of secondary vertices therefore plays a central role in ATLAS LLP...

65. Direct reconstruction of LLPs and charged heavy flavour at LHCb

Maarten Van Veghel (Nikhef National institute for subatomic physics (NL))

13/11/2025, 10:20

Talk

Session

For Run 3 of the LHC, the LHCb experiment has introduced, among others, two novel reconstruction methods. One targets long-lived particles. It uses the new fully software-based trigger operating at a 30 MHz data rate, opening a search window into previously unexplored regions of physics phase space. The BuSca (Buffer Scanner) method acquires and analyzes data in real time, extending...

57. CNN-based event separation using 2D and 3D images for the charged-current electron neutrino cross-section measurement with the SuperFGD of the T2K near detector

Tomochika Arai (University of Tokyo (JP))

13/11/2025, 11:10

Talk

Session

The T2K experiment is a long-baseline neutrino oscillation experiment, primarily aiming to search for CP violation in the neutrino sector from the precision measurement of neutrino and antineutrino oscillations. The neutrino beam is generated at J-PARC and is measured at Super-Kamiokande, and it is also measured at near detectors to reduce systematic uncertainties. Currently, the cross-section...

73. Improving positron tracking using machine learning in the MEG II experiment

Atsushi Oya (The university of Tokyo)

13/11/2025, 11:40

Talk

Session

In the MEG II experiment, which searches for $\mu\to e\gamma$, a cylindrical drift chamber measures positrons from muon decays. A key challenge arises from the declining positron reconstruction efficiency in the high-pileup environment, primarily due to algorithm limitations. To address this, a machine learning-based noise filtering technique has been developed. This presentation introduces...

54. Track direction identification via track-fitting quality for cosmic-ray background suppression in the COMET experiment

Manabu Moritsu (Kyushu University)

13/11/2025, 12:00

Talk

Session

The COMET experiment at J-PARC aims to search for the charged lepton flavor violating process of muon-to-electron conversion with unprecedented sensitivity. One of the most serious backgrounds originates from cosmic-ray muons. In particular, a track produced by a backward-going positive muon can mimic the 105 MeV/c signal electron in a cylindrical drift chamber. To address this, we developed a...

6. Real time learning on heterogeneous devices for detector calibration

Akshay Malige (Brookhaven National Laboratory (US))

14/11/2025, 09:00

Talk

Session

Modern beam telescopes play a crucial role in high-energy physics experiments to precisely track particle interactions. Accurate alignment of detector elements in real-time is essential to maintain the integrity of reconstructed particle trajectories, especially in high-rate environments like the ATLAS experiment at the Large Hadron Collider (LHC). Any misalignment in the detector geometry can...

53. The OpenDataDetector High-Luminosity Physics Benchmark Dataset

Paul Gessinger (CERN)

14/11/2025, 09:20

Talk

Session

We present the first release of a large-scale, fully simulated benchmark dataset using the OpenDataDetector (ODD) under high-luminosity collider conditions (aka ColliderML). The ODD integrates several advanced next-generation detector technologies to realistically capture the complexity of collisions expected at future collider experiments, notably at the High-Luminosity Large Hadron Collider...

48. Sustainability studies of big data processing in real time for HEP

Arantza De Oyanguren Campos (Univ. of Valencia and CSIC (ES))

14/11/2025, 09:50

Talk

Session

The LHCb collaboration is currently using a pioneer system of data filtering in the trigger system, based on real-time particle reconstruction using Graphics Processing Units (GPUs). This corresponds to processing 40 Tbits/s of data and has required a huge amount of hardware and software developments. Among them, the corresponding power consumption and sustainability is an imperative matter in...

56. Performance of 4D tracking and vertexing with ACTS

Pierfrancesco Butti (CERN)

14/11/2025, 10:40

Talk

Session

Four-dimensional trackers are devices capable of simultaneously measuring spatial and temporal coordinates with extremely good resolution (O(10 μm) and O(10 ps)) and represent a promising avenue for charged-particle tracking. The ACTS library has the capability to include time information in track and vertexing reconstruction algorithms thanks to its 6-parameter track representation. In this...

67. Hypergraph Neural Network 4D track reconstruction pipeline for HL-LHC experiments

Rodrigo Estevam De Paula (Universidade de Sao Paulo (USP) (BR))

14/11/2025, 11:10

Talk

Session

Particle track reconstruction is one of the most important and challenging tasks to be performed in the high luminosity phase of LHC experiments. Extensive research is being done to develop reconstruction methods that provide the same efficiency as the current adaptive filter methods but with enough throughput to suit the increase in the event information density of this new environment. A...

99. ACTS-based 4D tracking studies

Yanqi Wang (University of Science and Technology of China (CN))

14/11/2025, 11:40

Talk

Session

At the future colliders such as High-Luminosity LHC (HL-LHC), the average number of the simultaneous pp interactions per event, or pile-up (µ), will rise from current 30-60 to as much as 200. Among the full event simulation and reconstruction chains, reconstruction of charged particles quickly becomes the most computationally intensive chain because it scales combinatorially with an increasing...

107. Closing

Alexis Vallier (L2I Toulouse, CNRS/IN2P3, UT3)

14/11/2025, 12:00

44. Advances in Low-Energy Particle Track Reconstruction with Interaction Graph Networks at the PANDA experiment.

Nikolai in der Wiesche (University of Münster / Uppsala University)

Poster

The success of neural network based tracking algorithms for high energy colliders has prompted us to explore the merits of these methods for tracking in the lower energy regime of the PANDA experiment. In this talk, I will present the current state of a tracking pipeline that has been adapted from the Exa.TrkX group and that has an interaction graph neural network at its core. It has an...

58. Application of Point Cloud Classification to Particle Identification in the SuperFGD neutrino detector

Hikaru Tanigawa (KEK)

Poster

The T2K experiment is a long-baseline neutrino oscillation experiment located in Japan that has major goal to find hints of CP-violation in the leptonic sector. To further improve its sensitivity, it is crucial to precisely measure electron neutrino cross section at the near detector. This measurement has been challenging, as the T2K beam composition is mostly muon neutrinos, requiring a...

74. Challenges Deploying a Hybrid PV-finder Algorithm for Primary Vertex Reconstruction in LHCb’s GPU-Resident First Level Trigger

Mohamed Elashri (University of Cincinnati)

Poster

The PV-finder algorithm employs a hybrid deep neural network to reconstruct primary vertex positions (PVs) in proton-proton collisions at the LHC. The algorithm was originally developed for use in LHCb, but it has been adapted successfully for use in the much higher pile-up environment of ATLAS. PV-finder integrates fully connected layers that do track-by-track calculations with a...

55. ColliderML: Machine Learning Applications for the OpenDataDetector High-Luminosity Physics Benchmark Dataset

Paul Gessinger (CERN)

Poster

With the recent release of the OpenDataDetector High-Luminosity Physics Benchmark Dataset (aka ColliderML), it is now feasible to study the behaviour of machine learning (ML) algorithms in a variety of full simulation conditions. We present a suite of pilot studies that establish the utility of such a multi-scale, full-detector dataset for ML development. First, we examine the ability of...

50. Deep Learning for Primary Vertex Identification in the ATLAS Experiment

Heather Gray (UC Berkeley/LBNL)

Poster

The exponential time scaling of traditional primary vertex reconstruction algorithms, such as the Adaptive Multi-Vertex Finder (AMVF), raises significant performance concerns for future high-pileup environments, particularly with the upcoming High Luminosity upgrade to the Large Hadron Collider. In this talk, we introduce PV-Finder, a deep learning-based approach that leverages reconstructed...

40. Development and performance of electron reconstruction with Acts and the OpenDataDetector (ODD)

Andreas Stefl (Technische Universitaet Wien (AT))

Poster

Over the last years, a general purpose track finding algorithm based on the combinatorial Kalman filter (CKF) has been developed for the Acts toolkit - a community-driven project that provides experiment-independent tracking algorithms written in modern C++. It has been validated and optimized with the OpenDataDetector (ODD), and the ATLAS Phase-2 Inner Tracker (ITk). The CKF shows good...

42. Development of track finding based on RANSAC algorithm

Tianyu Xing (Università degli Studi e INFN Milano)

Poster

Reconstruction of Charged particle trajectory is a difficult task in high-energy physics, especially under some harsh conditions. By using RANSAC algorithm, We develop a track finding algorithm for low momentum electrons with multiple-turn trajectories, which could work in drift chamber with only stereo layers and without seed candidate. The performance is tested with simulation samples and...

84. Enhancing CMS Passive Material Modeling in Track Reconstruction Using ACTS

Lorenzo Damenti (Universita e INFN, Firenze (IT))

Poster

The tracker passive material cannot be neglected within the charged particle track reconstruction algorithm. In particular, track propagation must account for material effects such as multiple scattering and energy loss. A simplistic treatment leads to biases in the reconstructed track parameters and potential reconstruction efficiency losses.

In the CMS Offline Software (CMSSW), the...

91. Expected physics performances and robustness studies of the ATLAS ITk GNN-based Track Reconstruction Chain

Warren Guerin (L2I Toulouse, Université de Toulouse, CNRS/IN2P3)

Poster

The HL-LHC upgrade of the ATLAS inner detector (ITk) brings an unprecedented challenge, both in terms of the large number of silicon cluster readouts and the throughput required for budget-constrained track reconstruction. Applying Graph Neural Networks (GNNs) has been shown to be a promising solution to this problem with competitive physics performance at sub-second inference time. In this...

108. GNN-based Track Finding for a new multiple TPC detector

Shujie Li (Berkeley Lab)

Poster

The Tagged Deep Inelastic Scattering (TDIS) experiment at Jefferson Lab studies nucleon mesonic content by detecting low-momentum recoil hadrons with a multiple Time Projection Chamber (mTPC) in coincidence with scattered electrons. The expected high rate, high occupancy environment poses significant challenges to traditional track finding algorithms. In this talk, I will present our...

25. Graph Neural Network Acceleration on FPGAs for Real-Time Muon Triggering at the HL-LHC

Davide Fiacco (Sapienza Universita e INFN, Roma I (IT))

Poster

The upcoming High Luminosity phase of the Large Hadron Collider requires significant advancements in real-time data processing to handle the increased event rates and maintain high-efficiency trigger decisions. In this work, we explore the acceleration of graph neural networks on field-programmable gate arrays for fast inference within future muon trigger pipelines with O(100) ns latencies....

68. Kalman-Filter based detector alignment

Zequn Sun (Chinese Academy of Sciences (CN))

Poster

Track-based alignment is essential for determining the precise geometry of particle detectors by minimizing the residuals between measured hits and reconstructed particle trajectories. Currently, the most widely used alignment algorithm is based on the global $\chi^2$ method. This method solves a system of linear equations derived from the first-order expansion of the alignment problem,...

52. Machine Learning implementation in Front-End Electronics of Belle II Central Drift Chamber for cross-talk noise reduction

Yun-Tsung Lai

Poster

Central Drift Chamber (CDC) in the Belle II experiment is one of the charged tracking device for both offline and real-time hardware trigger systems. Belle II CDC has been using a Front-End Electronics (FEE) device based on Xilinx Virtex-5 FPGA to record the digitized waveform of anode wires and to deliver the data to both central data acquisition system and hardware trigger system. In the...

86. Monte Carlo Simulation for the LHC Experiments at CERN: A Comprehensive Overview

Marija Rapseviciute (Vilnius University (LT))

Poster

This overview of the Monte Carlo (MC) simulation process at the four main experiments - ATLAS, CMS, ALICE, and LHCb - at the Large Hadron Collider (LHC) is provided in this talk. Given the complexity and noise of proton-proton collisions, simulated samples are essential for understanding and interpreting the experimental results, enabling precise measurements and searches for new physics. The...

80. Optimization of track reconstruction including low-pT particles using quantum-inspired simulated annealing

Koichiro Kanke (Waseda University (JP))

Poster

In general, tracking requires significant computational resources, and the ATLAS experiment currently reconstructs only particles with transverse momentum above 0.5 GeV. However, in searches for new physics, such as long-lived charginos, analyzing lower momentum regions is expected to offer a novel approach. In this study, we formulate tracking as a combinatorial optimization problem and apply...

39. Patatune: A Framework for Multi-Objective Optimization of Track Reconstruction Algorithms

Felice Pantaleo (CERN)

Poster

Accurate and efficient track reconstruction is critical for the results of high-energy physics experiments, particularly as upcoming high-luminosity environments become more complex with increased data rates and pile-up. Reconstruction algorithms depend on numerous parameters whose optimal configuration is crucial and non-trivial. Manual tuning in such high-dimensional solution spaces is often...

17. Performance of Lossless Data Compression Algorithms in the CMS Experiment

Felice Pantaleo (CERN)

Poster

The High-Level Trigger (HLT) of the Compact Muon Solenoid (CMS) processes event data in real time, applying selection criteria to reduce the data rate from hundreds of kHz to around 5 kHz for raw data offline storage. Efficient lossless compression algorithms, such as LZMA and ZSTD, are essential in minimizing these storage requirements while maintaining easy access for subsequent analysis....

12. Simulation truth for optimal CMS pixel tracking

Jan Schulz (Rheinisch Westfaelische Tech. Hoch. (DE))

Poster

The CMS detector will undergo several key upgrades for its Phase-2 in the upcoming High-Luminosity Large Hadron Collider (HL-LHC), including the replacement of the entire silicon tracking system, which will consist of an inner tracker based on silicon pixel modules and an outer tracker made from silicon modules with strip and macro-pixel sensors. In addition to a new detector, reconstruction...

75. Track Fitting with the Global $\chi^2$ Fitter in ACTS

Alexander J Pfleger (University of Graz (AT))

Poster

The global $\chi^2$ fitter (GX2F) is a least-squares fitter designed to determine the initial parameters of a particle track. We present a thorough mathematical derivation and implement a modern version within the widely used tracking framework ACTS (A Common Tracking Software). The GX2F provides an efficient method for accounting for material effects by fitting all scattering angles, which...

89. Track reconstruction of cosmic-ray data for BESIII drift chamber

Linghui Wu (Institute of High Energy Physics, Chinese Academy of Sciences (CN))

Poster

The drift chamber is applied in the BESIII experiment to provide both momentum measurement and particle identification with dE/dx for charged particles. It has just undergone an upgrade of the inner chamber to solve the aging problem. A cosmic ray test has been performed at the beginning of this year for the detector commissioning and the software alignment. A track reconstruction algorithm...

59. Transformer-based Track Fitting for HL-LHC

Jeremy Couthures (Laboratoire d'Annecy de physique des particules, CNRS / Univ. Savoie Mont Blanc (FR))

Poster

As the High-Luminosity LHC (HL-LHC) era approaches, significant improvements in reconstruction software are required to keep pace with the increased data rates and detector complexity. Some promising algorithms for high-throughput event reconstruction are GPU-based algorithms. Among those algorithms, the Kalman Filter is commonly used for the estimation of track parameters. While it leads to a...

5. Transformers for Charged Particle Track Reconstruction in High Energy Physics

Philippa Kathryn Duckett (University of London (GB))

Poster

Reconstructing charged particle trajectories is a fundamental challenge in high-energy physics, particularly with the increased particle multiplicities anticipated at the High-Luminosity Large Hadron Collider (HL-LHC). Traditional tracking algorithms may struggle with the computational demands posed by these conditions. In our study, we introduce an innovative approach that leverages recent...