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CERN Document Server Pronađeno je 1,151 zapisa  1 - 10slijedećikraj  idi na zapis: Pretraživanje je potrajalo 0.69 sekundi 
1.
25th IEEE Real Time Conference - RT2026   25 - 29 May 2026  - La Biodola, Elba Island, It  .-
2026
Detaljnije
2.
Online Track Reconstruction with Graph Neural Networks on FPGAs for ATLAS / Neubauer, Mark (Univ. Illinois at Urbana Champaign (US)) /ATLAS Collaboration
The High-Luminosity Large Hadron Collider (HL-LHC) at CERN marks a new era for high-energy particle physics, demanding significant upgrades to the ATLAS Trigger and Data Acquisition (TDAQ) system. Central to these upgrades is the enhancement of online software tracking capabilities to meet the unprecedented data rates and complexity of HL-LHC operations. [...]
ATL-DAQ-SLIDE-2025-469.- Geneva : CERN, 2025 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : The 32nd International Symposium on Lepton Photon Interactions at High Energies (Lepton Photon 2025), Madison, Wisconsin, Us, 25 - 29 Aug 2025
Detaljnije
3.
Jet finding in real-time using an object detection CNN / Bozianu, Leon (Universite de Geneve (CH)) /ATLAS Collaboration
The ATLAS trigger system will undergo a comprehensive upgrade in advance of the HL-LHC programme. In order to deal with the increased data bandwidth trigger algorithms will be required to satisfy stricter latency requirements. [...]
ATL-DAQ-SLIDE-2025-468.- Geneva : CERN, 2025 - 23 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : Fast Machine Learning for Science Conference 2025, Zurich, Switzerland, Ch, 1 - 5 Sep 2025
Detaljnije
4.
High Throughput FPGA Deployment of Distilled Deep Sets Networks for Jet Preselection in the High Level Trigger / Antel, Claire (CERN) ; Bezio, Lucas (Universite de Geneve (CH)) ; Berthet, Quentin (HEPIA - Haute école du paysage, d'ingénierie et d'architecture (CH)) ; Franchellucci, Stefano (Universite de Geneve (CH)) ; Sfyrla, Anna (Universite de Geneve (CH)) /ATLAS Collaboration
Deep Sets-based neural networks are well-suited to learning from unordered, variable-length inputs such as particle tracks associated with jets. Their permutation-invariant structure makes them attractive for high-energy physics (HEP) applications where input ordering is ambiguous and throughput is a critical constraint. [...]
ATL-DAQ-SLIDE-2025-467.- Geneva : CERN, 2025 - 1 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
Detaljnije
5.
GELATO: A Generic Event-Level Anomaly Detection Trigger for ATLAS / Jia, Kenny (SLAC National Accelerator Laboratory (US)) ; ATLAS Collaboration /ATLAS Collaboration
The absence of BSM physics discoveries at the LHC suggests new physics could lie outside current trigger schemes. By applying unsupervised ML–based anomaly detection, we gain a model-agnostic way of spotting anomalous signatures that deviate from the current trigger’s expectations. [...]
ATL-DAQ-SLIDE-2025-466.- Geneva : CERN, 2025 - 17 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : Fast Machine Learning for Science Conference 2025, Zurich, Switzerland, Ch, 1 - 5 Sep 2025
Detaljnije
6.
MEMFlow in Double Higgs search application /CMS Collaboration
The Matrix Element Method (MEM) offers optimal statistical power for hypothesis testing in particle physics, but its application is hindered by the computationally intensive multidimensional integrals required to model detector effects. We present a novel approach that addresses this challenge by employing Transformers and generative machine learning (ML) models. [...]
CMS-DP-2025-056; CERN-CMS-DP-2025-056.- Geneva : CERN, 2025 - 57 p.
Detaljnije
7.
Context-Dependent Outlier Detection Technique for Analysis of Single Event Frequency Transients in CMOS LC-Tank Oscillators / Rombouts, W ; Karsmakers, P (Leuven U.) ; Adom-Bamfi, G (Leuven U.) ; Biereigel, S (CERN) ; Prinzie, J (Leuven U.)
This article presents a contextual outlier detection technique employing machine learning (ML) method to improve the quality of experimentally obtained data from heavy-ion irradiation campaigns. Artifacts arising from the experimental setup often compromise the integrity and representativeness of the measured data. [...]
2025 - 8 p. - Published in : IEEE Trans. Nucl. Sci. 72 (2025) 1086-1093
Detaljnije
8.
17th Topical Seminar on Innovative Particle and Radiation Detectors - IPRD25   15 - 19 Sep 2025  - Siena, It  / Adriani, O (ed.); Aprile, E (ed.); Camporesi, T (ed.); Coutu, S (ed.); Dalla Betta, G F (ed.); De Bernardis, P (ed.); Iuppa, R (ed.); Lecoq, P (ed.); Nociforo, C (ed.); Pastrone, N (ed.) et al.
2025
Detaljnije
9.
From bins to flows: unbinned and multivariate scale factors /CMS Collaboration
Most CMS objects, taggers, and triggers are calibrated using likelihood fits. A widely used technique for measuring efficiencies, particularly for electrons and muons, is the Tag-and-Probe method. This approach exploits the clean signature of $Z\rightarrow l^{+}l^{-}$ decays: one lepton is required to pass stringent identification and trigger criteria (the tag), while the other (the probe) is used to study the efficiency of a given selection in both data and Monte Carlo (MC) simulation. [...]
CMS-DP-2025-053; CERN-CMS-DP-2025-053.- Geneva : CERN, 2025 - 25 p. Fulltext: PDF;
Detaljnije
10.
Debiasing Ultrafast Anomaly Detection with Posterior Agreement /CMS Collaboration
The Level-1 Trigger system of the CMS experiment at CERN makes the final decision on which LHC collision data are stored to disk for later analysis. One algorithm used with this scope is an anomaly detection model based on an autoencoder architecture. [...]
CMS-DP-2025-050; CERN-CMS-DP-2025-050.- Geneva : CERN, 2025 - 40 p. Fulltext: PDF;
Detaljnije

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