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CERN Document Server 274 registres trobats  1 - 10següentfinal  anar al registre: La cerca s'ha fet en 0.54 segons. 
1.
Towards Machine-Learning Particle Flow with the ATLAS Detector at the LHC / Clissa, Luca (Universita e INFN, Bologna (IT))
Particle flow reconstruction at colliders combines various detector subsystems (typically the calorimeter and tracker) to provide a combined event interpretation that utilizes the strength of each detector. [...]
ATL-SOFT-PROC-2025-018. - 2025 - 8.
Original Communication (restricted to ATLAS) - Full text
Registre complet - Registres semblants
2.
Fast simulation with generative models at the LHC / Mijovic, Liza (The University of Edinburgh (GB))
The increasing integrated luminosity of the data collected at the major Large Hadron Collider experiments - ALICE, ATLAS, CMS, and LHCb - necessitates increasingly large simulated samples. [...]
ATL-SOFT-PROC-2025-008. - 2025 - 6.
Original Communication (restricted to ATLAS) - Full text
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3.
Anomaly Detection, Prognostics, and Diagnostics: Machine Learning for the Hadron Calorimeter at the CMS Experiment / Asres, Mulugeta Weldezgina
Machine Learning (ML) tools have gained immense popularity due to the proliferation of sensor data for monitoring, prognostic, and diagnostic applications in various industrial domains [...]
CERN-THESIS-2024-282 CMS-TS-2024-028. - 2024 - 286.

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4.
Artificial Intelligence Algorithms for the Real-Time Selection of Physically Important Events Within the CMS L1 Trigger FPGA Devices / Burazin Misura, Arijana
The Large Hadron Collider is the largest particle accelerator in the world with the purpose ofcolliding protons accelerated to nearly the speed of light, resulting in the creation of new andexotic particles [...]
CERN-THESIS-2024-276 CMS-TS-2024-026. - 2024 - 147.

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5.
QDIPS: Deep Sets Network for FPGA investigated for high speed inference on ATLAS / Antel, Claire (Universite de Geneve (CH)) /ATLAS Collaboration
Deep sets network architectures have useful applications in finding correlations in unordered and variable length data input, thus having the interesting feature of being permutation invariant. Its use on FPGA would open up accelerated machine learning in areas where the input has no fixed length or order, such as inner detector hits for clustering or associated particle tracks for jet tagging. [...]
ATL-DAQ-SLIDE-2024-616.- Geneva : CERN, 2024 - 1 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : 27th International Conference on Computing in High Energy & Nuclear Physics, Kraków, Pl, 19 - 25 Oct 2024
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6.
An implementation of neural simulation-based inference for parameter estimation in ATLAS / ATLAS Collaboration
Neural simulation-based inference is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. [...]
arXiv:2412.01600 ; CERN-EP-2024-305. - 2024 - 49.
Fulltext - Previous draft version - Fulltext
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7.
The Application of Machine Learning in Schottky Spectra Analysis / Bradicic, Manuel ; Lasocha, Kacper (CERN)
Schottky Spectra, that is random fluctuations of the macroscopic beam properties, such as beam position or intensity, contain rich information on machine parameters crucial for preserving the beam stability. [...]
CERN-STUDENTS-Note-2024-224. - 2024
Access to fulltext
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8.
Jet Finding as a Real-Time Object Detection Task / Bozianu, Leon (Universite de Geneve (CH)) /ATLAS Collaboration
The High Luminosity upgrade to the LHC (HL-LHC) will deliver an unprecedented luminosity to the ATLAS experiment. Ahead of this increase in data the ATLAS trigger and data acquisition system will undergo a comprehensive upgrade. [...]
ATL-PHYS-SLIDE-2024-579.- Geneva : CERN, 2024 - 29 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : ML4Jets2024, Paris, Fr, 4 - 8 Nov 2024
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9.
CaloJetSSD: Jet Finding as a Real-Time Object Detection Task
The High Luminosity upgrade to the LHC (HL-LHC) will deliver an unprecedented luminosity to the ATLAS experiment. [...]
ATL-DAQ-PUB-2024-003. - 2024 - 28.
Original Communication (restricted to ATLAS) - Full text
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10.
Parameter Estimation with Neural Simulation-Based Inference in ATLAS / Coelho Lopes De Sa, Rafael (University of Massachusetts (US)) ; Ghosh, Aishik (University of California Irvine (US)) ; Louppe, Gilles Claude ; Martinez Outschoorn, Verena Ingrid (University of Massachusetts (US)) ; Maury, Arnaud Jean (Université Paris-Saclay (FR)) ; Rousseau, David (Université Paris-Saclay (FR)) ; Sandesara, Jay Ajitbhai (University of Massachusetts (US)) ; Schaffer, R D (Université Paris-Saclay (FR)) ; Whiteson, Daniel (University of California Irvine (US)) /ATLAS Collaboration
Neural Simulation-Based Inference (NSBI) is a powerful class of machine learning (ML)-based methods for statistical inference that naturally handle high dimensional parameter estimation without the need to bin data into low-dimensional summary histograms. Such methods are promising for a range of measurements at the Large Hadron Collider, where no single observable may be optimal to scan over the entire theoretical phase space under consideration, or where binning data into histograms could result in a loss of sensitivity. [...]
ATL-PHYS-SLIDE-2024-566.- Geneva : CERN, 2024 - 41 p. Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
In : ML4Jets2024, Paris, Fr, 4 - 8 Nov 2024
Registre complet - Registres semblants

CERN Document Server : 274 registres trobats   1 - 10següentfinal  anar al registre:
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