28th Conference on Computing in High Energy and Nuclear Physics (CHEP 2026)

Session

Track 2 - Online and real-time computing

25 May 2026, 13:45

Chulalongkorn University

120. The ATLAS Trigger System

Stefanie Morgenstern (Heidelberg University (DE))

25/05/2026, 13:45

Track 2 - Online and real-time computing

Oral Presentation

The ATLAS experiment in the LHC Run 3 uses a two-level trigger system to select
events of interest to reduce the 40 MHz bunch crossing rate to a recorded rate
of up to 3 kHz of fully-built physics events. The trigger system is composed of
a hardware based Level-1 trigger and a software based High Level Trigger.
The selection of events by the High Level Trigger is based on a wide variety...

527. Development of Data Scouting program in Run 3 at the CMS experiment

Patin Inkaew (Helsinki Institute of Physics (FI))

25/05/2026, 14:03

Track 2 - Online and real-time computing

Oral Presentation

Pioneered by CMS in Run 1, the “data scouting” technique has helped found a now-established trend in the LHC experiments. Implemented during Run 2, LHCb and ATLAS collaborations have “turbo” and “trigger-level analysis” streams, respectively.

The “data scouting” technique overcomes the limitations of the conventional data processing strategies with nonstandard uses of trigger and data...

218. A Hyperparameter Optimization Framework for Preparing ML Models for Real-Time Trigger Deployment

Anastasiia Petrovych (CERN)

25/05/2026, 14:21

Track 2 - Online and real-time computing

Oral Presentation

Machine learning models used in real-time and resource-constrained environments, such as hardware triggers, online reconstruction pipelines, and FPGA/GPU inference systems, must satisfy strict latency, memory, and numerical precision requirements. Achieving these targets typically requires extensive tuning of training schedules, quantization settings, sparsity levels, and architectural...

642. An End-to-End, Unified Workflow for Sub-Microsecond Inference on FPGAs

Dimitrios Danopoulos (CERN)

25/05/2026, 14:39

Track 2 - Online and real-time computing

Oral Presentation

Real-time inference with sub-microsecond latency is critical for the Level-1 trigger systems at the High-Luminosity LHC. We present an end-to-end, open-source framework that spans model optimization, quantization, and FPGA deployment, enabling the translation of high-level neural network or generic dataflow models into resource-efficient FPGA implementations.

Within the workflow, we...

127. AIE4ML: Leveraging Versal AI Engines to Enable More Expressive Real-Time ML Models for Next-Generation Trigger Systems

Dimitrios Danopoulos (CERN)

25/05/2026, 14:57

Track 2 - Online and real-time computing

Oral Presentation

Modern particle-physics experiments increasingly rely on machine learning (ML) to perform real-time data reduction under the extreme conditions of the High-Luminosity LHC (HL-LHC). Hardware-trigger inference must satisfy microsecond-level latency, deterministic execution, and tight on-chip memory constraints. FPGA-based deployments can meet these requirements for small, highly parallelized...

216. Autoencoders for real-time event selection at the LHCb experiment

Paloma Laguarta González (University of Barcelona (ES))

25/05/2026, 16:15

Track 2 - Online and real-time computing

Oral Presentation

The LHCb experiment operates a fully software-based trigger that must reduce the 40 MHz collision rate to an output bandwidth of around 10 GB/s, making real-time event selection a central computing challenge. Current selections in the second-level trigger (HLT2) are largely based on hand-crafted cuts, which can be difficult to optimise in high-dimensional spaces and may lack robustness against...

299. Online searches for long-lived particles at LHCb: BuSca (Buffer Scanner)

Valerii Kholoimov (EPFL - Ecole Polytechnique Federale Lausanne (CH))

25/05/2026, 16:51

Track 2 - Online and real-time computing

Oral Presentation

The new fully software-based trigger of the LHCb experiment at CERN operates at a 30 MHz data rate, opening a search window into previously unexplored regions of the physics phase space. The BuSca (Buffer Scanner) project at LHCb acquires and analyses data in real time, prior to any trigger decision, extending sensitivity to new particle lifetimes and mass ranges.
Displaced tracks that...

122. Accelerating Graph Neural Networks on FPGAs for Real-Time Level-0 Muon Triggering

25/05/2026, 17:09

Track 2 - Online and real-time computing

Oral Presentation

The High-Luminosity LHC will generate unprecedented data rates, pushing real-time trigger systems to their limits. We present a novel approach deploying graph neural networks (GNNs) on FPGAs to achieve fast, sub-microsecond inference for Level-0 muon triggers. Exploiting the sparse, relational structure of detector hits, the method preserves key spatial correlations while enabling...

207. Particle-Based Representation Learning for Anomaly Detection in the CMS High-Level Trigger

Mehrnoosh Moallemi (Science and Technology Facilities Council STFC (GB))

25/05/2026, 17:27

Track 2 - Online and real-time computing

Oral Presentation

Anomaly detection at the LHC aims to identify events that deviate from dominant Standard Model (SM) processes while minimizing assumptions inherent to predefined trigger selections, enabling model-agnostic searches for new physics. The CMS experiment employs a two-stage trigger system that reduces the LHC bunch-crossing rate of up to 40 MHz to an output rate of approximately 9 kHz for offline...

189. Advances in the Optimisation of the LHCb First Level Trigger

Andreea-Irina Hedes (The University of Manchester (GB))

25/05/2026, 17:45

Track 2 - Online and real-time computing

Oral Presentation

The first level of the LHCb experiment’s trigger system (HLT1) performs real-time reconstruction and selection of events at the LHC bunch crossing rate using GPUs. It must balance the diverse goals of the LHCb physics programme, which spans from kaon physics to the electroweak scale.

To maximise the physics output across the entirety of LHCb's physics programme, an automated bandwidth...

530. Advancing the CMS Level-1 Trigger: Jet Tagging and pT regression with Deep Sets at the HL-LHC

Stella Felice Schaefer (Hamburg University (DE))

26/05/2026, 13:45

Track 2 - Online and real-time computing

Oral Presentation

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...

246. Modular Neural Network Deployment in hls4ml for Next-Generation LHC Trigger Systems

Enrico Lupi (CERN)

26/05/2026, 14:03

Track 2 - Online and real-time computing

Oral Presentation

Machine-learning algorithms are becoming central to real-time event selection at the LHC, where future trigger systems must process substantially more complex detector information at fixed, sub-microsecond latencies. These constraints create a growing need for flexible workflows that can map large neural networks onto heterogeneous trigger hardware while preserving strict timing budgets. We...

108. Optimizing GNNs for the Wild: PyTorch-to-ONNX Acceleration of GarNet on CPUs and GPUs

Ronald Caravaca-Mora (Consejo Nacional de Rectores (CONARE) (CR)/Universidad de Costa Rica (UCR) (CR)), Cilicia Uzziel Perez (La Salle, Ramon Llull University (ES))

26/05/2026, 14:21

Track 2 - Online and real-time computing

Oral Presentation

Graph-based reconstruction methods are well-suited to the sparse and irregular geometry of modern calorimeters, but their deployment often depends on achieving low and predictable inference latency across heterogeneous computing environments. We evaluate GarNet, a lightweight Graph Neural Network (GNN) for calorimeter energy reconstruction, focusing on its cross-backend performance using...

51. It’s not a FAD: first demonstration of Flows for unsupervised Anomaly Detection at 40 MHz for use at the Large Hadron Collider

Dimitrios Danopoulos (CERN)

26/05/2026, 14:39

Track 2 - Online and real-time computing

Oral Presentation

We present the first implementation of a Continuous Normalizing Flow (CNF) model for unsupervised anomaly detection within the realistic, high-rate environment of the Large Hadron Collider's L1 trigger systems. While CNFs typically define an anomaly score via a probabilistic likelihood, calculating this score requires solving an Ordinary Differential Equation, a procedure too complex for FPGA...

422. COLLIDE-2V - 750 Million Dual-View LHC Event Dataset for Low-Latency ML

Eric Anton Moreno (Massachusetts Institute of Technology (US))

26/05/2026, 14:57

Track 2 - Online and real-time computing

Oral Presentation

Modern foundation models (FMs) have pushed the frontiers of language, vision, and multi-model tasks by training ever-larger neural networks (NN) on unprecedented volumes of data. The use of FM models has yet to be established in collider physics, which both lack a comparably sized, general-purpose dataset on which to pre-train universal event representations, and a clear demonstrable need....

92. Workflow setup and configuration for the ALICE online processing

Ernst Hellbär (CERN)

26/05/2026, 16:15

Track 2 - Online and real-time computing

Oral Presentation

The ALICE experiment at CERN continuously reads out and records data at interaction rates of up to 50 kHz of Pb-Pb collisions. Online processing and reconstruction play a vital role for handling the enormous amounts of data, compressing about 3.5 TB/s of detector raw data down to 160 GB/s of compressed input data for offline reconstruction. The online processing is performed on dedicated Event...

113. Towards Collaborative, Web-Native Monitoring Dashboards for Online and Archived Histograms in ATLAS using Grafana and JSROOT

Serguei Kolos (University of California Irvine (US))

26/05/2026, 16:33

Track 2 - Online and real-time computing

Oral Presentation

In LHC Run 3, several hundred thousand histograms are continuously updated during data taking and used by automated algorithms for data quality assessment. A subset of these histograms is also presented to experts. The current online histogram display, based on a standalone C++ application using ROOT and Qt, provides reliable functionality but offers limited integration with modern web...

380. Hydra: A scalable, open-source vision system for continual data quality monitoring

Thomas Britton

26/05/2026, 16:51

Track 2 - Online and real-time computing

Oral Presentation

Maintaining high data quality in modern Nuclear and High Energy
Physics experiments increasingly requires scalable, automated solutions
as data rates and detector complexity continue to grow. Traditionally, hu-
mans monitored data quality with varying skill sets and expertise, while
any automation was typically overly bespoke, covering only specific de-
tector systems or processes. These...

116. ClickHouse-Based ATLAS Operational Monitoring Data Archiving Service Prototype

Igor Soloviev (University of California Irvine (US))

26/05/2026, 17:09

Track 2 - Online and real-time computing

Oral Presentation

Since the beginning of LHC Run 2, the Trigger and Data Acquisition system of the ATLAS experiment at the Large Hadron Collider (LHC) at CERN has provided an operational monitoring data archiving service used by thousands of online clients. During data-taking periods, this system publishes various operational monitoring data to continuously monitor the status of hardware and software components...

418. Optimising Memory Usage in the LHCb Online Mover with a Rust-Based Architecture

Robert Laszlo Gulyas (CERN)

26/05/2026, 17:27

Track 2 - Online and real-time computing

Oral Presentation

The LHCb Online Mover is a critical component of the LHCb online computing stack, responsible for streaming data accepted by the High Level Trigger 2 (HLT2) from online storage to long-term offline infrastructure. During data-taking, data is produced at sustained rates of up to 20 GB/s, with bursts reaching 50 GB/s. For efficient long-term storage, the data must be compressed and packed into...

616. Reinforcement Learning-Based Control of Polarized Target and Goniometer Systems at Jefferson Lab

Torri Jeske

26/05/2026, 17:45

Track 2 - Online and real-time computing

Oral Presentation

Jefferson Lab is developing autonomous control systems for polarized cryogenic targets and linearly polarized photon beams, enabling stable, high-performance operation over extended experiment run periods. Historically, maintaining optimal polarization of these critical systems required manual tuning by expert operators. This process is sensitive to experience and prone to human error, and...

117. ATLAS Event Filter muon reconstruction for the HL-LHC

Leonardo Monaco (University of Glasgow (GB))

27/05/2026, 13:45

Track 2 - Online and real-time computing

Oral Presentation

The High Luminosity Large Hadron Collider (HL-LHC) is scheduled to begin operation in 2030 and will increase the number of proton-proton collisions per bunch-crossing from around 60 to 200. The upgraded trigger system of the ATLAS experiment will record around 10kHz of the collisions to disk for physics analysis and this reduction is achieved with an L0 trigger that will feed the Event Filter...

278. Filtering hits for speeding up track reconstruction at hadron colliders

Alessandro Zaio, Alessandro Zaio (INFN e Universita Genova (IT))

27/05/2026, 14:03

Track 2 - Online and real-time computing

Oral Presentation

Trigger systems enable to quickly inspect the reconstructed physical quantities obtained from collisions at hadron colliders, in order to decide whether to save the corresponding detector data for offline analysis. The processing of the data coming from pixel detectors is a crucial challenge for the experiments running at the Large Hadron Collider (LHC) at CERN, because of the large number of...

98. Real-Time event reconstruction for Nuclear Physics Experiments using Artificial Intelligence

Gagik Gavalian (Jefferson National Lab)

27/05/2026, 14:21

Track 2 - Online and real-time computing

Oral Presentation

Charged-particle track reconstruction is a central component of nuclear physics experiments, providing the foundation for identifying and analyzing particles produced in high-energy interactions. While traditional techniques—such as pattern-recognition algorithms and Kalman-filter–based tracking—have long been the standard, modern machine learning (ML) methods are increasingly addressing the...

235. Development of streaming data reconstruction for ePIC experiment at EIC

Takuya Kumaoka (University of Tsukuba (JP))

27/05/2026, 14:39

Track 2 - Online and real-time computing

Oral Presentation

The Electron-Ion Collider (EIC) will introduce new paradigms in large-scale nuclear physics experiments. With luminosities reaching up to 10³⁴ cm⁻²s⁻¹, the ePIC experiment must process extremely large data volumes and therefore adopts a flexible, scalable, and efficient streaming data acquisition model. This system replaces custom level-1 trigger electronics, enables the use of commercial...

119. Regional reconstruction of tracks for the ATLAS Event Filter using GPU accelerators

Eleni Xochelli (Universitat Autonoma de Barcelona (ES))

27/05/2026, 14:57

Track 2 - Online and real-time computing

Oral Presentation

The upcoming high-luminosity phase of the LHC (HL-LHC) presents several challenges for the ATLAS experiment's Trigger and Data Acquisition system, necessitating a
full upgrade of the system. A key challenge for the Event Filter, where high-level event reconstruction and final event selection will run at 1 MHz, lies in the computational demand for online track reconstruction within the Inner...

298. The backward realtime tracking strategy in the LHCb experiment

Jiahui Zhuo (Univ. of Valencia and CSIC (ES))

27/05/2026, 16:15

Track 2 - Online and real-time computing

Oral Presentation

In Run 3 data taking, the LHCb experiment at CERN operates with a fully software-based first level trigger (HLT1) on GPUs that processes 30 million collision events per second with a data throughput of 4 TB/s. Realtime track reconstruction is essential for HLT1 because most trigger decisions rely on reconstructed tracks or on higher level objects built from them, such as secondary...

111. Studies of FPGA accelerated track reconstruction for the ATLAS Event Filter

Ioannis Maznas (Northern Illinois University (US))

27/05/2026, 16:33

Track 2 - Online and real-time computing

Oral Presentation

The upcoming high-luminosity phase of the LHC (HL-LHC) presents several challenges for the ATLAS experiment's Trigger and Data Acquisition system, necessitating a full upgrade of the system. A key challenge for the Event Filter, where high-level event reconstruction and final event selection will run at 1 MHz, lies in the computational demand for online track reconstruction within the Inner...

46. Real-time 40 MHz Track Reconstruction: The Readout and Processing Chain of the CMS Outer Tracker for HL-LHC

Marco Riggirello (Scuola Normale Superiore & INFN Pisa (IT))

27/05/2026, 16:51

Track 2 - Online and real-time computing

Oral Presentation

The High Luminosity LHC (HL-LHC) presents an unprecedented computing challenge, characterized by a pile-up of up to 200 interactions per bunch crossing and extreme data rates. To cope with these conditions, the CMS experiment is replacing its tracking system with a novel Outer Tracker capable of contributing to the Level-1 (L1) Trigger. This upgrade introduces a paradigm shift in data...

102. Neural network cluster finding for the ALICE TPC online computing

Christian Sonnabend (CERN, Heidelberg University (DE))

27/05/2026, 17:09

Track 2 - Online and real-time computing

Oral Presentation

The ALICE time projection chamber (TPC) is the main tracking and particle identification device used in the ALICE experiment at CERN. With a 900 GB/s data rate and a fully GPU-based online reconstruction, the online processing is capable of handling even the densest environments of central Pb--Pb interactions at 50 kHz nominal interaction rate (Run 3) and creates an ideal environment for the...

54. A heterogeneous and vectorized sequence for the HL-LHC full tracking reconstruction of the CMS experiment

Manos Vourliotis (Univ. of California San Diego (US))

27/05/2026, 17:27

Track 2 - Online and real-time computing

Oral Presentation

This talk presents the new baseline strategy for the Phase-2 tracking of the CMS experiment for online event reconstruction, and for the main iteration of offline tracking. This tracking sequence takes advantage of the combination of cutting-edge tracking algorithms that are either optimized for parallel execution on GPUs (Patatrack and LST), or are vectorized for efficient CPU performance...

114. Studies of track reconstruction performance in the ATLAS Event Filter for the HL-LHC

Federica Piazza (University of Oregon (US))

27/05/2026, 17:45

Track 2 - Online and real-time computing

Oral Presentation

The instantaneous luminosity
at the High-Luminosity LHC (HL-LHC) will reach unprecedented levels, boosting the physics reach at the LHC. To cope with the resulting challenging pile-up condition and fully exploit the new high-granularity Inner Tracker (ITk), a major upgrade of the ATLAS
Trigger and Data Acquisition (TDAQ) system is ongoing, with track reconstruction in the Event Filter...

161. A Unified Interface to ML Runtimes for Inference across Heterogeneous Architectures

Sanjiban Sengupta (CERN, University of Manchester)

28/05/2026, 13:45

Track 2 - Online and real-time computing

Oral Presentation

Machine learning approaches have been widely adopted across several areas of high-energy physics research, including simulations, anomaly detection, and trigger systems. Deploying machine learning in trigger systems requires inference approaches capable of processing data at enormous rates, often on the order of 10–100 thousand events per second while making real-time decisions about which...

562. Mitigating Deadtime in Distributed Optical Arrays: A Liveness-Aware Trigger Approach for High-Energy Neutrino Detection

Mr Thammarat Yawisit (King Mongkut's Institute of Technology Ladkrabang)

28/05/2026, 13:45

Track 2 - Online and real-time computing

Oral Presentation

Large-scale neutrino observatories operate under unavoidable detector deadtime arising from photomultiplier saturation, digitizer limits, and front-end readout constraints. Conventional coincidence-based trigger logic implicitly assumes continuous sensor availability and therefore suffers systematic efficiency loss when channels become temporarily non-live. This work presents the design of a...

148. Real-Time Uncertainty Quantification for Jet Tagging Models on the Level-1 Trigger

Tarik Ourida

28/05/2026, 14:03

Track 2 - Online and real-time computing

Oral Presentation

Current deep learning based models at the LHC produce deterministic point estimates without any accompanying measure of epistemic uncertainty. Without this information, the system cannot determine when its predictions may be unreliable, particularly in rare or weakly sampled regions of feature space. This work introduces a high performance Bayesian Neural Network architecture for the Level-1...

107. When Less is More: Towards Lightweight and Distilled Graph Neural Networks and for Efficient Particle Reconstruction in LHCb’s Next-Generation Calorimeter

Cilicia Uzziel Perez (La Salle, Ramon Llull University (ES)), Irvin Jadurier Umana Chacon (Consejo Nacional de Rectores (CONARE) (CR))

28/05/2026, 14:03

Track 2 - Online and real-time computing

Oral Presentation

Graph Neural Networks (GNNs) excel at modeling the complex, irregular geometry of modern calorimeters, but their computational cost poses challenges for real-time or resource-constrained environments. We present lightweight, attention-enhanced GNNs built on node-centric GarNet layers, which eliminate costly edge message passing and provide learnable, permutation-invariant aggregation optimized...

63. Conceptual Design and Operation of the Calibration Loop for the Next Generation Triggers in the CMS Experiment

Jessica Prendi (ETH Zurich (CH))

28/05/2026, 14:21

Track 2 - Online and real-time computing

Oral Presentation

The Next Generation Triggers (NGT) initiative in CMS aims to enable the processing of all Level-1 Trigger accepted collisions for the HL-LHC. Central to this effort is the expansion of the High-Level Trigger (HLT) data scouting strategy, where events are reconstructed and stored in an analysis-ready format. This necessitates an in situ processing loop to derive high-quality calibration...

565. Recursive Manifold Coherence: A Geometric Framework for Deadtime Recovery in Distributed Trigger Systems

Mr Thammarat Yawisit (King Mongkut's Institute of Technology Ladkrabang)

28/05/2026, 14:21

Track 2 - Online and real-time computing

Oral Presentation

Large-scale neutrino observatories operate under unavoidable detector deadtime and signal pile-up, leading to systematic inefficiencies in conventional coincidence-based trigger systems. Such triggers typically rely on binary temporal windows and assume continuous sensor availability, causing partial or complete loss of correlated signal information during non-live intervals. We introduce...

334. New trigger algorithms for detecting Long-Lived particle decays inside the LHCb magnet region

Izaac Sanderswood (Univ. of Valencia and CSIC (ES)), Volodymyr Svintozelskyi (Univ. of Valencia and CSIC (ES))

28/05/2026, 14:39

Track 2 - Online and real-time computing

Oral Presentation

The reconstruction of particle decays inside LHCb’s dipole magnet region enables novel measurements of hyperon decays and sensitive searches for long-lived particles with lifetimes above 100 ps, relevant both to the Standard Model and to many of its extensions. Reconstructing such displaced vertices using only track segments in LHCb’s outermost tracker (SciFi) is challenging due to limited...

539. Towards AI-Driven Automation for Data Quality Monitoring in ALICE

Zeta Sourpi (Universite de Geneve (CH))

28/05/2026, 14:39

Track 2 - Online and real-time computing

Oral Presentation

The ALICE (A Large Ion Collider Experiment) is a general-purpose heavy-ion detector at the CERN Large Hadron Collider (LHC) that operates at interaction rates producing raw data streams of O(TB/s). Due to these data volumes, an online reconstruction is performed to achieve a compressed representation of the continuous data stream. Given the lossy nature of this process, early assessment of...

242. PQuantML: A Tool for End-to-End Hardware-aware Model Compression

Roope Oskari Niemi

28/05/2026, 14:57

Track 2 - Online and real-time computing

Oral Presentation

We present PQuantML, an open-source library for end-to-end hardware-aware model compression that enables the training and deployment of compact, high-performance neural networks on resource-constrained hardware in physics and beyond. PQuantML abstracts away the low-level details of compression by letting users compress models with a simple configuration file and an API call. It enables the use...

118. The phase-1 upgrade of the ATLAS level-1 calorimeter trigger

Jared Little (Indiana University (US))

28/05/2026, 14:57

Track 2 - Online and real-time computing

Oral Presentation

The ATLAS level-1 calorimeter trigger is a custom-built hardware system

that identifies events containing calorimeter-based physics objects,

including electrons, photons, taus, jets, and total and missing transverse energy.

In Run 3, L1Calo has been upgraded to process higher granularity

input data. The new trigger comprises several FPGA-based feature extractor modules,

which...

22. New DAQ software for the NEXT-100 experiment

Jose Maria Benlloch Rodriguez (Donostia International Physics Center (DIPC) (ES))

28/05/2026, 16:15

Track 2 - Online and real-time computing

Oral Presentation

The Neutrino Experiment with a Xenon TPC (NEXT) investigates neutrinoless double-beta decay (0νββ) in xenon using high-pressure xenon time projection chambers. This approach enables excellent energy resolution and allows for the 3D reconstruction of the track, improving the sensitivity using the topological information.

Previous prototypes of the NEXT experimental programme were using DATE...

121. The upgrade of the ATLAS Trigger and Data Acquisition system for the High Luminosity LHC

Marcos Vinicius Silva Oliveira (Brookhaven National Laboratory (US))

28/05/2026, 16:15

Track 2 - Online and real-time computing

Oral Presentation

The ATLAS experiment at CERN is constructing upgraded system
for the "High Luminosity LHC", with collisions due to start in
2030. In order to deliver an order of magnitude more data than
previous LHC runs, 14 TeV protons will collide with an instantaneous
luminosity of up to 7.5 x 10e34 cm^-2s^-1, resulting in much higher pileup and
data rates than the current experiment was designed to...

152. Scalable DAQ Performance for Supernova Neutrino Observations in DUNE

Mr Robert-Mihai Amarinei (University of Toronto (CA))

28/05/2026, 16:33

Track 2 - Online and real-time computing

Oral Presentation

The Deep Underground Neutrino Experiment (DUNE) is an international next-generation project that will use a powerful neutrino beam produced at Fermilab and two detectors: a near detector at Fermilab and a far detector ~1300 kilometers away, at the Sanford Underground Research Facility in South Dakota. DUNE features a high-throughput, modular data acquisition system (DAQ) specifically designed...

546. The data handling library of the DUNE Data Acquisition System

Deniz Tuana Ergonul

28/05/2026, 16:33

Track 2 - Online and real-time computing

Oral Presentation

The Deep Underground Neutrino Experiment (DUNE) is a long-baseline neutrino physics experiment with detectors located 1.5 km underground at the Sanford Underground Research Facility. The Data Acquisition (DAQ) system interfaces with multiple front-end electronics, each producing data with distinct rates and formats, and handles the reception, transportation, and preparation of this data for...

226. The online event classification software in the JUNO experiment

Wenxing Fang

28/05/2026, 16:51

Track 2 - Online and real-time computing

Oral Presentation

The Jiangmen Underground Neutrino Observatory (JUNO) is a large-scale neutrino experiment with multiple physics goals. After many years of dedicated effort, the construction of the JUNO detector has been successfully completed, and physics data-taking officially commenced on August 26, 2025.
The detector readout system produces waveform data at a rate of approximately 40 GB/s at a 1 kHz...

594. Wall-time-driven data aggregation for the CBM FLES input interface

Dirk Hutter (Goethe University Frankfurt (DE))

28/05/2026, 16:51

Track 2 - Online and real-time computing

Oral Presentation

The CBM First-Level Event Selector (FLES) serves as the central data processing and event selection system for the upcoming CBM experiment at FAIR. Designed as a scalable high-performance computing cluster, it facilitates online event reconstruction and selection of unfiltered physics data at rates surpassing 1 TByte/s. The FLES input data originates from approximately 5000 detector links,...

564. Networking in KM3NeT

Emidio Maria Giorgio (INFN LNS)

28/05/2026, 17:09

Track 2 - Online and real-time computing

Oral Presentation

The KM3NeT neutrino detectors, currently under construction in the Mediterranean Sea, are designed to measure high-energy cosmic neutrinos and their properties. To exploit the Cherenkov effect as the detection technique, the ARCA and ORCA detectors are deployed at two abyssal sites, off the coasts of southern Italy and France, respectively. Operating in such an extreme deep-sea environment,...

547. Performance evaluation and optimization of ethernet-based data acquisition servers in DUNE

Deniz Tuana Ergonul, Shyam Bhuller (University of Oxford (GB))

28/05/2026, 17:27

Track 2 - Online and real-time computing

Oral Presentation

The Data Acquisition (DAQ) system of the Deep Underground Neutrino Experiment (DUNE) at the Sanford Underground Research Facility must receive detector data aggregated over multiple 100 Gbps Ethernet streams from the Far Detector modules front-end electronics. This contribution outlines the performance tuning and evaluation of high-performance COTS (Commercial Off-The-Shelf) readout servers,...

462. The SPD Online filter.

Dr Danila Oleynik (Joint Institute for Nuclear Research (RU))

28/05/2026, 17:27

Track 2 - Online and real-time computing

Oral Presentation

The Spin Physics Detector (SPD) is currently under construction at the second interaction point of the NICA collider at JINR. Its primary physics goal is to test fundamental aspects of Quantum Chromodynamics by studying the polarized structure of the nucleon and investigating spin-dependent phenomena in collisions of longitudinally and transversely polarized protons and deuterons. These...

599. Development of a common platform for streaming readout DAQ systems within the SPADI Alliance

Tomonori Takahashi (RCNP, University of Osaka)

28/05/2026, 17:45

Track 2 - Online and real-time computing

Oral Presentation

The SPADI Alliance in Japan is developing a common, trigger-less streaming data acquisition (DAQ) platform to address the increasing demands of modern nuclear and particle physics experiments. The Alliance integrates R&D efforts from front-end electronics to computing and networking, promoting open collaboration across laboratories.
At the hardware level, the platform is developing a family...

237. Service-Based DUNE Run Control Architecture for High-Availability Data Acquisition

Pawel Maciej Plesniak (Imperial College (GB))

28/05/2026, 17:45

Track 2 - Online and real-time computing

Oral Presentation

DUNE is a long-baseline neutrino oscillation experiment utilizing several detectors at both the Near Detector (ND) and Far Detector (FD) facilities. The design and architecture of the FD control and data acquisition (DAQ) system have progressed with the successful operation of the ProtoDUNE-II FD prototypes at CERN. The control system architecture has evolved from a single monolithic structure...