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Jet classiﬁcation with GarNet / Leite, Julia Carvalho
In this report, it is passed though all important aspects of Hadronic Jet produced in LHC collision events and collisions simulations approach using the main programs, all the way through tagging heavy particles in Fat jets algorithm process. [...]
CERN-STUDENTS-Note-2021-215. - 2021
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(91)

HLS4ML: deploying deep learning on FPGAs for L1 trigger and Data Acquisition / Ngadiuba, Jennifer (speaker) (CERN)
Machine learning is becoming ubiquitous across HEP. There is great potential to improve trigger and DAQ performances with it. [...]
2018 - 3612. EP-IT Data science seminars External link: Event details In : HLS4ML: deploying deep learning on FPGAs for L1 trigger and Data Acquisition

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(77)

Physics Object Localization with Point Cloud Segmentation Networks
In modern particle physics experiments, the identification and trajectory of physics object, e.g. [...]
ATL-PHYS-PUB-2021-002. - 2021. - 16 p.
Original Communication (restricted to ATLAS) - Full text

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(67)

Implementation of Long Short-Term Memory Neural Networks in High-Level Synthesis Targeting FPGAs / Rao, Richa
Field programmable gate arrays (FPGAs) offer flexibility in programmable systems, making them ideal for hardware implementations of machine learning algorithms [...]
CERN-THESIS-2020-103 - 41 p.

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(67)

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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(59)

DeepJet: a deep-learned multiclass jet-tagger for slim and fat jets / Gouskos, Loukas (speaker) (Univ. of California Santa Barbara (US)) ; Qu, Huilin (speaker) (Univ. of California Santa Barbara (US)) ; Stoye, Markus (speaker) (CERN) ; Kieseler, Jan (speaker) (CERN) ; Verzetti, Mauro (speaker) (CERN)
We present a customized neural network architecture for both, slim and fat jet tagging. It is based on the idea to keep the concept of physics objects, like particle flow particles, as a core element of the network architecture. [...]
2018 - 1590. Machine Learning; 2nd IML Machine Learning Workshop External links: Talk details; Event details In : 2nd IML Machine Learning Workshop

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(56)

Deep-learning Top Taggers or The End of QCD? / Kasieczka, Gregor (speaker) (Eidgenoessische Technische Hochschule Zuerich (CH))
https://arxiv.org/abs/1701.08784 Machine learning based on convolutional neural networks can be used to study jet images from the LHC. Top tagging in fat jets offers a well-defined framework to establish our DeepTop approach and compare its performance to QCD-based top taggers. [...]
2017 - 1362. Machine Learning; IML Machine Learning Workshop External links: Talk details; Event details In : IML Machine Learning Workshop

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(54)

ParticleNet: Jet Tagging via Particle Clouds / Qu, Huilin (UC, Santa Barbara) ; Gouskos, Loukas (CERN)
How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point clouds, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle cloud". [...]
arXiv:1902.08570.- 2020-03-26 - 11 p. - Published in : Phys. Rev. D 101 (2020) 056019 Article from SCOAP3: PDF; Fulltext: PDF; External link: GitHub

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(53)

ParticleNet: Jet Tagging via Particle Clouds / Qu, Huilin (speaker) (Univ. of California Santa Barbara (US))
How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point cloud, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle cloud". [...]
2019 - 1272. LPCC Workshops; 3rd IML Machine Learning Workshop External links: Talk details; Event details In : 3rd IML Machine Learning Workshop

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(53)

Convolutional Layer Implementations in High-Level Synthesis for FPGAs / Lin, Kelvin
Field programmable gate arrays (FPGAs) offer a flexible hardware platform on which machine learning algorithms can be efficiently implemented [...]
CERN-THESIS-2021-098 - 41 p.

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