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CERN Document Server 6 Datensätze gefunden  Die Suche hat 1.38 Sekunden gedauert. 
1.
Machine learning techniques for model-independent searches in dijet final states / Harris, Philip (MIT) ; Mccormack, William Patrick (MIT) ; Park, Sang Eon (MIT) ; Quadfasel, Tobias (Hamburg U.) ; Sommerhalder, Manuel (Hamburg U.) ; Moureaux, Louis Jean (Hamburg U.) ; Kasieczka, Gregor (Hamburg U.) ; Amram, Oz (Fermilab) ; Maksimovic, Petar (Johns Hopkins U.) ; Maier, Benedikt (KIT, Karlsruhe, EKP) et al.
We present the performance of Machine Learning--based anomaly detection techniques for extracting potential new physics phenomena in a model-agnostic way with the CMS Experiment at the Large Hadron Collider. We introduce five distinct outlier detection or density estimation techniques, namely CWoLa, Tag N' Train, CATHODE, QUAK, and QR-VAE, tailored for the identification of anomalous jets originating from the decay of unknown heavy particles. [...]
CMS-NOTE-2023-013; CERN-CMS-NOTE-2023-013.- Geneva : CERN, 2023 - 11 p. Fulltext: PDF;
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2.
Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning / Shanahan, Phiala (MIT) ; Terao, Kazuhiro (SLAC) ; Whiteson, Daniel (UC, Irvine) ; Aarts, Gert (Swansea U. ; ECT, Trento ; Fond. Bruno Kessler, Trento) ; Adelmann, Andreas (Northeastern U. ; PSI, Villigen) ; Akchurin, N. (Texas Tech.) ; Alexandru, Andrei (George Washington U. ; Maryland U.) ; Amram, Oz (Johns Hopkins U.) ; Andreassen, Anders (Google Inc.) ; Apresyan, Artur (Fermilab) et al.
The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. [...]
arXiv:2209.07559 ; FERMILAB-CONF-22-719-ND-PPD-QIS-SCD. -
Fermilab Library Server - eConf - Fulltext - Fulltext
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3.
Applications and Techniques for Fast Machine Learning in Science / Deiana, Allison McCarn (Southern Methodist U.) ; Tran, Nhan (Fermilab ; Northwestern U. (main)) ; Agar, Joshua (Lehigh U. (main)) ; Blott, Michaela (Xilinx, Dublin) ; Di Guglielmo, Giuseppe (Columbia U. (main)) ; Duarte, Javier (UC, San Diego) ; Harris, Philip (MIT) ; Hauck, Scott (George Washington U. (main)) ; Liu, Mia (Purdue U.) ; Neubauer, Mark S. (Illinois U., Urbana) et al.
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. [...]
arXiv:2110.13041; FERMILAB-PUB-21-502-AD-E-SCD.- 2022-04-12 - 56 p. - Published in : Front. Big Data 5 (2022) 787421 Fulltext: 2110.13041 - PDF; fermilab-pub-21-502-ad-e-scd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server
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4.
The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics / Kasieczka, Gregor (Hamburg U.) ; Nachman, Benjamin (LBL, Berkeley ; UC, Berkeley, Miller Inst.) ; Shih, David (Rutgers U., Piscataway) ; Amram, Oz (Johns Hopkins U.) ; Andreassen, Anders (Google Inc.) ; Benkendorfer, Kees (LBL, Berkeley ; Reed Coll.) ; Bortolato, Blaz (Stefan Inst., Ljubljana) ; Brooijmans, Gustaaf (Nevis Labs, Columbia U.) ; Canelli, Florencia (Zurich U.) ; Collins, Jack H. (SLAC) et al.
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. [...]
arXiv:2101.08320.- 2021-12-07 - 108 p. - Published in : Rep. Prog. Phys. 84 (2021) 124201 Fulltext: PDF;
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5.
Probing the chiral magnetic wave in pPb and PbPb collisions at $\sqrt{s_{NN}} = 5.02 $TeV using charge-dependent azimuthal anisotropies / CMS Collaboration
Charge-dependent anisotropy Fourier coefficients ($v_n$) of particle azimuthal distributions are measured in pPb and PbPb collisions at $ \sqrt{\smash[b]{s_{_{\mathrm{NN}}}}} = $ 5.02 TeV with the CMS detector at the LHC. The normalized difference in the second-order anisotropy coefficients ($v_2$) between positively and negatively charged particles is found to depend linearly on the observed event charge asymmetry with comparable slopes for both pPb and PbPb collisions over a wide range of charged particle multiplicity. [...]
arXiv:1708.08901; CMS-HIN-16-0017; CERN-EP-2017-180; CMS-HIN-16-017-003.- 2019-12-18 - 17 p. - Published in : Phys. Rev. C 100 (2019) 064908 Fulltext: PDF; External links: Figures, tables and other information; Fulltext from Publisher
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6.
Charge asymmetry dependence of anisotropic flow in pPb and PbPb collisions with the CMS experiment / Park, Sang Eon (Rice U.) /CMS Collaboration
In nucleus-nucleus collisions, the linear dependence found for the elliptic flow harmonic of both positive or negative charged particles as a function of event charge asymmetry is predicted by the phenomenon known as the Chiral Magnetic Wave (CMW) due to its induced electric quadrupole moment. Here, the event charge asymmetry $A_{\rm ch}$ is defined as $\frac{N_{+}-N_{-}}{N_{+}+N_{-}}$, where $N_{+}$ and $N_{-}$ are the number of positive and negative charged particles, respectively. [...]
arXiv:1704.06712.- Geneva : CERN, 2017-11 - 4 p. - Published in : Nucl. Phys. A 967 (2017) 345-348 Fulltext: 1704.06712 - PDF; CMS Note - PDF; arXiv:1704.06712 - PDF;
In : The XXVI international conference on ultrarelativistic heavy-ion collisions, Chicago, United States Of America, 6 - 11 Feb 2017, pp.345-348
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