CERN Accelerating science

Hlavná stránka > Výsledky hľadania: recid:2919392
Tipy pre vyhľadávanie
Rozšírené Hľadanie
Add to Search +
Hľadaj kolekcie:
Utriediť podľa: Zobraziť výsledky: Výstupný formát:
CERN Document Server 2,031 záznamov nájdených  1 - 10ďalšíkoniec  skoč na záznam: Hľadanie trvalo 0.38 sekúnd. 
1.
(100)
Machine learning in high energy physics: a review of heavy-flavor jet tagging at the LHC / Mondal, Spandan (Brown U.) ; Mastrolorenzo, Luca (CERN)
The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable growth and innovation in the past decade. This review provides a detailed examination of current and past ML techniques in this domain. [...]
arXiv:2404.01071.- 2024-07-19 - 38 p. - Published in : Eur. Phys. J. Spec. Top.
Fulltext: PDF;
Úplný záznam - Podobné záznamy
2.
(91)
Run 3 performance and advances in heavy-flavor jet tagging in CMS / Sarkar, Uttiya (Aachen, Tech. Hochsch.) /CMS Collaboration
Identification of hadronic jets originating from heavy-flavor quarks is extremely important to several physics analyses in High Energy Physics, such as studies of the properties of the top quark and the Higgs boson, and searches for new physics. Recent algorithms used in the CMS experiment were developed using state-of-the-art machine-learning techniques to distinguish jets emerging from the decay of heavy flavour (charm and bottom) quarks from those arising from light-flavor (udsg) ones. [...]
CMS-CR-2024-247.- Geneva : CERN, 2024 - 14 p. Fulltext: PDF;
In : 42nd International Conference on High Energy Physics (ICHEP 2024), Prague, Czech Republic, 18 - 24 Jul 2024
Úplný záznam - Podobné záznamy
3.
(91)
Run 3 performance and advances in heavy-flavor jet tagging in CMS / Sarkar, Uttiya (RWTH Aachen U.)
Identification of hadronic jets originating from heavy-flavor quarks is extremely important to several physics analyses in High Energy Physics, such as studies of the properties of the top quark and the Higgs boson, and searches for new physics. [...]
arXiv:2412.05863. - 13.
Fulltext
Úplný záznam - Podobné záznamy
4.
(64)
Heavy-flavour jet tagging in ATLAS / Windischhofer, Philipp Jonas (University of Oxford, Particle Physics) /ATLAS Collaboration
The identification of jets originating from heavy-flavor quarks (b-quark, c-quark) is central to the LHC physics program. High-performance heavy-flavor tagging is necessary both in precise standard model measurements as well as in searches for new physics. [...]
ATL-PHYS-SLIDE-2020-022.- Geneva : CERN, 2020 Fulltext: PDF; External link: Original Communication (restricted to ATLAS)
Úplný záznam - Podobné záznamy
5.
(56)
A unified approach for jet tagging in Run 3 at $\sqrt{s}$=13.6 TeV in CMS /CMS Collaboration
The steady progress in machine learning leads to substantial performance improvements in various areas of high-energy physics, especially for object identification. Jet flavor identification (tagging) is a prominent benchmark that profits from elaborate architectures, leveraging information from low-level input variables and their correlations. Throughout the data-taking eras of the Large Hadron Collider (LHC) (Run 1 - Run 3), various deep-learning-based algorithms were established and led to a significantly improved tagging performance of heavy flavor jets, originating from the hadronization of b and c quarks. [...]
CMS-DP-2024-066; CERN-CMS-DP-2024-066.- Geneva : CERN, 2024 - 49 p. Fulltext: PDF;
Úplný záznam - Podobné záznamy
6.
(44)
Thumbnail
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
Úplný záznam - Podobné záznamy
7.
(42)
Reweighting heavy-flavor production fractions to reduce flavor modelling uncertainties for ATLAS
The ability to identify jets originating from $b$- and $c$-hadrons (flavor tagging) is one of the key experimental techniques that enables a wide range of searches and measurements by the ATLAS experiment. [...]
ATL-PHYS-PUB-2022-035. - 2022.
Original Communication (restricted to ATLAS) - Full text
Úplný záznam - Podobné záznamy
8.
(41)
Run 3 commissioning results of heavy-flavor jet tagging at $\sqrt{s}=$ 13.6 TeV with CMS data using a modern framework for data processing / Lee, Ming-yan (Aachen, Tech. Hochsch.) /CMS Collaboration
The identification of jets arising from heavy-flavor (bottom or charm) quarks primarily relies on detector inputs from reconstructed charged particle tracks and information about secondary vertices contained within reconstructed jets. In Run 3, improved machine-learning techniques have been introduced to distinguish heavy-flavor jets from those originating from the hadronization of light-flavor (uds) quarks or gluons (g). [...]
CMS-CR-2024-269.- Geneva : CERN, 2024 - 8 p. Fulltext: PDF;
In : 12th Edition of the Large Hadron Collider Physics Conference, Boston, Us, 3 - 7 Jun 2024
Úplný záznam - Podobné záznamy
9.
(39)
Heavy-flavor production / Smith, J ; Tung, W K
ITP-SB-93-65 ; MSU-HEP-93-16 ; NIKHEF-H-93-20. - 1993. - 19 p.
CERN library copies
Úplný záznam - Podobné záznamy
10.
(39)
Heavy-flavor physics / Quinn, Helen R
CERN, 2004 Published version from CERN: PDF;
In : European School of High-Energy Physics, Pylos, Greece, 25 Aug - 7 Sep 2002, pp.35-65 (CERN-2004-001)
Úplný záznam - Podobné záznamy

Nenašli ste čo ste hľadali? Skúste Vašu otázku na iných serveroch:
recid:2919392 v Amazon
recid:2919392 v CERN EDMS
recid:2919392 v CERN Intranet
recid:2919392 v CiteSeer
recid:2919392 v Google Books
recid:2919392 v Google Scholar
recid:2919392 v Google Web
recid:2919392 v IEC		recid:2919392 v IHS
recid:2919392 v INSPIRE
recid:2919392 v ISO
recid:2919392 v KISS Books/Journals
recid:2919392 v KISS Preprints
recid:2919392 v NEBIS
recid:2919392 v SLAC Library Catalog