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CERN Document Server 10 notices trouvées  La recherche a duré 1.29 secondes. 
1.
Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider / Govorkova, Ekaterina (CERN) ; Puljak, Ema (CERN) ; Aarrestad, Thea (CERN) ; James, Thomas (CERN) ; Loncar, Vladimir (CERN) ; Pierini, Maurizio (CERN) ; Pol, Adrian Alan (CERN) ; Ghielmetti, Nicolò (CERN) ; Graczyk, Maksymilian (CERN) ; Summers, Sioni (CERN) et al.
In this paper, we show how to adapt and deploy anomaly detection algorithms based on deep autoencoders, for the unsupervised detection of new physics signatures in the extremely challenging environment of a real-time event selection system at the Large Hadron Collider (LHC). We demonstrate that new physics signatures can be enhanced by three orders of magnitude, while staying within the strict latency and resource constraints of a typical LHC event filtering system. [...]
arXiv:2108.03986; FERMILAB-PUB-21-487-CMS; FERMILAB-PUB-21-487-CMS.- 2022-02-23 - 12 p. - Published in : Nature Mach. Intell. 4 (2022) 154-161 Fulltext: 2108.03986 - PDF; fermilab-pub-21-487-cms - PDF; External link: Fermilab Library Server
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2.
New Physics Agnostic Selections For New Physics Searches / Woźniak, Kinga Anna (CERN ; Vienna U.) ; Cerri, Olmo (Caltech) ; Duarte, Javier M (Fermilab) ; Möller, Torsten (Vienna U.) ; Ngadiuba, Jennifer (CERN) ; Nguyen, Thong Q (Caltech) ; Pierini, Maurizio (CERN) ; Spiropulu, Maria (Caltech) ; Vlimant, Jean-Roch (Caltech)
We discuss a model-independent strategy for boosting new physics searches with the help of an unsupervised anomaly detection algorithm. Prior to a search, each input event is preprocessed by the algorithm - a variational autoencoder (VAE). [...]
FERMILAB-CONF-20-633-CMS.- 2020 - 8 p. - Published in : EPJ Web Conf. 245 (2020) 06039 Fulltext: 10.1051_epjconf_202024506039 - PDF; fermilab-conf-20-633-cms - PDF;
In : 24th International Conference on Computing in High Energy and Nuclear Physics, Adelaide, Australia, 4 - 8 Nov 2019, pp.06039
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3.
Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning / Chen, Cheng (Peking U.) ; Cerri, Olmo (Caltech) ; Nguyen, Thong Q. (Caltech) ; Vlimant, Jean-Roch (Caltech) ; Pierini, Maurizio (CERN)
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. [...]
arXiv:2010.01835. - 15 p.
Fulltext
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4.
Generative Adversarial Networks for fast simulation / Carminati, Federico (CERN) ; Khattak, Gulrukh (CERN) ; Loncar, Vladimir (Belgrade, Inst. Phys.) ; Nguyen, Thong Q (Caltech) ; Pierini, Maurizio (CERN) ; Brito Da Rocha, Ricardo (CERN) ; Samaras-Tsakiris, Konstantinos (CERN) ; Vallecorsa, Sofia (CERN) ; Vlimant, Jean-Roch (Caltech)
Deep Learning techniques are being studied for different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. [...]
IOP, 2020 - 6 p. - Published in : J. Phys.: Conf. Ser. 1525 (2020) 012064 Published fulltext: PDF;
In : 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Saas Fee, Switzerland, 11 - 15 Mar 2019, pp.012064
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5.
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark / Knapp, Oliver (Zurich, ETH) ; Cerri, Olmo (Caltech) ; Dissertori, Guenther (Zurich, ETH) ; Nguyen, Thong Q. (Caltech) ; Pierini, Maurizio (CERN) ; Vlimant, Jean-Roch (Caltech)
We apply an Adversarially Learned Anomaly Detection (ALAD) algorithm to the problem of detecting new physics processes in proton-proton collisions at the Large Hadron Collider. Anomaly detection based on ALAD matches performances reached by Variational Autoencoders, with a substantial improvement in some cases. [...]
arXiv:2005.01598.- 2021-02-19 - 16 p. - Published in : Eur. Phys. J. Plus 136 (2021) 236 Fulltext: PDF; Fulltext from publisher: PDF;
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6.
Particle Generative Adversarial Networks for full-event simulation at the LHC and their application to pileup description / Arjona Martínez, Jesús (Trinity Coll., Cambridge ; Caltech ; CERN) ; Nguyen, Thong Q. (Caltech) ; Pierini, Maurizio (CERN) ; Spiropulu, Maria (Caltech) ; Vlimant, Jean-Roch (Caltech)
We investigate how a Generative Adversarial Network could be used to generate a list of particle four-momenta from LHC proton collisions, allowing one to define a generative model that could abstract from the irregularities of typical detector geometries. As an example of application, we show how such an architecture could be used as a generator of LHC parasitic collisions (pileup). [...]
arXiv:1912.02748.- 2020-07-08 - 7 p. - Published in : J. Phys.: Conf. Ser. 1525 (2020) 012081 Fulltext: PDF; Fulltext from publisher: PDF;
In : 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Saas Fee, Switzerland, 11 - 15 Mar 2019, pp.012081
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7.
Interaction networks for the identification of boosted $H\to b\overline{b}$ decays / Moreno, Eric A. (Caltech) ; Nguyen, Thong Q. (Caltech) ; Vlimant, Jean-Roch (Caltech) ; Cerri, Olmo (Caltech) ; Newman, Harvey B. (Caltech) ; Periwal, Avikar (Caltech) ; Spiropulu, Maria (Caltech) ; Duarte, Javier M. (UC, San Diego (main) ; Fermilab) ; Pierini, Maurizio (CERN)
We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinary jets that reflect the configurations of quarks and gluons at short distances. The algorithm's inputs are features of the reconstructed charged particles in a jet and the secondary vertices associated with them. [...]
arXiv:1909.12285; FERMILAB-PUB-19-492-CMS-E.- 2020-07-29 - 18 p. - Published in : Phys. Rev. D 102 (2020) 012010 Article from SCOAP3: PDF; Fulltext: fermilab-pub-19-492-cms-e - PDF; 1909.12285 - PDF; External link: Fermilab Accepted Manuscript
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8.
JEDI-net: a jet identification algorithm based on interaction networks / Moreno, Eric A. (Caltech) ; Cerri, Olmo (Caltech) ; Duarte, Javier M. (Fermilab ; UCLA) ; Newman, Harvey B. (Caltech) ; Nguyen, Thong Q. (Caltech) ; Periwal, Avikar (Caltech) ; Pierini, Maurizio (CERN) ; Serikova, Aidana (Caltech ; CERN) ; Spiropulu, Maria (Caltech) ; Vlimant, Jean-Roch (Caltech)
We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons. The jet dynamics are described as a set of one-to-one interactions between the jet constituents. [...]
arXiv:1908.05318; FERMILAB-PUB-19-360-PPD.- 2020-01-25 - 15 p. - Published in : Eur. Phys. J. C 80 (2020) 58 Article from SCOAP3: scoap3-fulltext - PDF; scoap - PDF; Fulltext: 1908.05318 - PDF; fermilab-pub-19-360-ppd - PDF; External link: Fermilab Library Server (fulltext available)
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9.
Variational Autoencoders for New Physics Mining at the Large Hadron Collider / Cerri, Olmo (Caltech) ; Nguyen, Thong Q. (Caltech) ; Pierini, Maurizio (CERN) ; Spiropulu, Maria (Caltech) ; Vlimant, Jean-Roch (Caltech)
Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn't make specific assumptions on the nature of new physics. [...]
arXiv:1811.10276.- 2019-05-07 - 29 p. - Published in : JHEP 05 (2019) 036 Article from SCOAP3: scoap3-fulltext - PDF; scoap - PDF; Fulltext: PDF;
Notice détaillée - Notices similaires
10.
Topology classification with deep learning to improve real-time event selection at the LHC / Nguyen, Thong Q. (Caltech) ; Weitekamp, Daniel (UC, Berkeley) ; Anderson, Dustin (Caltech) ; Castello, Roberto (CERN) ; Cerri, Olmo (Caltech) ; Pierini, Maurizio (CERN) ; Spiropulu, Maria (Caltech) ; Vlimant, Jean-Roch (Caltech)
We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class classifiers are trained. [...]
arXiv:1807.00083.- 2019-08-31 - 15 p. - Published in : Comput. Softw. Big Sci. 3 (2019) 12 Fulltext: PDF; Fulltext from Publisher: PDF;
Notice détaillée - Notices similaires

Voir aussi: noms d'auteurs similaires
65 Nguyen, T
7 Nguyen, T A
1 Nguyen, T C
125 Nguyen, T D
1 Nguyen, T G
4 Nguyen, T H
4 Nguyen, T H V
3 Nguyen, T K
1 Nguyen, T K T
1 Nguyen, T L
1 Nguyen, T L Hoai
1 Nguyen, T M
4 Nguyen, T Phong
5 Nguyen, T Q
44 Nguyen, T T
1 Nguyen, T T M
3 Nguyen, T V
11 Nguyen, T.
36 Nguyen, T.D.
1 Nguyen, T.M.
3 Nguyen, T.M.T.
7 Nguyen, T.Q.
4 Nguyen, T.T.
1 Nguyen, Tai
1 Nguyen, Tam
1 Nguyen, Tan
1 Nguyen, Thach
2 Nguyen, Thach G
5 Nguyen, Thanh
1 Nguyen, Thanh H
1 Nguyen, Thanh Hoang Duy
1 Nguyen, Thanh Hung
1 Nguyen, Thanh Long
1 Nguyen, Thanh Nam
1 Nguyen, Thanh T-H
2 Nguyen, Thanh Tien Dat
1 Nguyen, Thanh Trung
1 Nguyen, Thanh Tung
1 Nguyen, Thanh-Nhan
1 Nguyen, Thanh-Thuy
1 Nguyen, The Cang
1 Nguyen, Thi
1 Nguyen, Thi A
310 Nguyen, Thi Dung
1 Nguyen, Thi Mong Ngoc
1 Nguyen, Thi Phuong Nhung
1 Nguyen, Thi Thuy Minh
136 Nguyen, Thi-Dung
1 Nguyen, Thi-Hiep
2 Nguyen, Thi-Mai-Huong
3 Nguyen, Thien
2 Nguyen, Thien An
1 Nguyen, Thien Lam Ngoc
1 Nguyen, Thinh
1 Nguyen, Thinh Duc
1 Nguyen, Tho
2 Nguyen, Tho H
1 Nguyen, Tho Manh
635 Nguyen, Thong
2 Nguyen, Thong Q
2 Nguyen, Thu
2 Nguyen, Thuan
1 Nguyen, Thuy
1 Nguyen, Thuy D
1 Nguyen, Tien-Minh
10 Nguyen, Timothy
13 Nguyen, Toan
1 Nguyen, Toan D
1 Nguyen, Toan T
3 Nguyen, Tommy
7 Nguyen, Trang
2 Nguyen, Tri
2 Nguyen, Triet
1 Nguyen, Trung Dac
2 Nguyen, Truong
1 Nguyen, Truyen
2 Nguyen, Tu
4 Nguyen, Tuan
1 Nguyen, Tuan-Khoa
1 Nguyen, Tuán Anh
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