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CERN Document Server 527 registres trobats  1 - 10següentfinal  anar al registre: La cerca s'ha fet en 0.79 segons. 
1.
Axion Dark Matter / Adams, C.B. (Columbia U.) ; Aggarwal, N. ; Agrawal, A. (Chicago U.) ; Balafendiev, R. (ITMO U., St. Petersburg) ; Bartram, C. (Washington U., Seattle) ; Baryakhtar, M. (Washington U., Seattle) ; Bekker, H. (Helmholtz Inst., Mainz ; Mainz U.) ; Belov, P. (ITMO U., St. Petersburg) ; Berggren, K.K. (MIT) ; Berlin, A. (Fermilab) et al.
Axions are well-motivated dark matter candidates with simple cosmological production mechanisms. [...]
arXiv:2203.14923 ; FERMILAB-CONF-22-996-PPD-T. - 96.
eConf - Fermilab Library Server - Fulltext - Fulltext
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2.
Promising Technologies and R&D Directions for the Future Muon Collider Detectors / Muon Collider Collaboration
Among the post-LHC generation of particle accelerators, the muon collider represents a unique machine with capability to provide very high energy leptonic collisions and to open the path to a vast and mostly unexplored physics programme. [...]
arXiv:2203.07224. - 31.
Fermilab Library Server - eConf - Fulltext - Fulltext
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3.
A Muon Collider Facility for Physics Discovery / Muon Collider Collaboration
Muon colliders provide a unique route to deliver high energy collisions that enable discovery searches and precision measurements to extend our understanding of the fundamental laws of physics. [...]
arXiv:2203.08033. - 23.
Fermilab Library Server - eConf - Fulltext - Fulltext
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4.
Simulated Detector Performance at the Muon Collider / Muon Collider Collaboration
In this paper we report on the current status of studies on the expected performance for a detector designed to operate in a muon collider environment. [...]
arXiv:2203.07964 ; FERMILAB-FN-1185-AD-ND-PPD-TD. - 45.
Fermilab Library Server - eConf - Fulltext - Fulltext
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5.
The Forward Physics Facility at the High-Luminosity LHC / Feng, Jonathan L. (UC, Irvine) ; Kling, Felix (DESY) ; Reno, Mary Hall (Iowa U.) ; Rojo, Juan (NIKHEF, Amsterdam ; Vrije U., Amsterdam) ; Soldin, Dennis (Delaware U.) ; Anchordoqui, Luis A. (Lehman Coll.) ; Boyd, Jamie (CERN) ; Ismail, Ahmed (Oklahoma State U.) ; Harland-Lang, Lucian (Oxford U. ; Oxford U., Theor. Phys.) ; Kelly, Kevin J. (CERN) et al.
High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe Standard Model (SM) processes and search for physics beyond the Standard Model (BSM) [...]
arXiv:2203.05090; UCI-TR-2022-01; CERN-PBC-Notes-2022-001; INT-PUB-22-006; BONN-TH-2022-04; FERMILAB-PUB-22-094-ND-SCD-T.- Geneva : CERN, 2023-01-20 - 413 p. - Published in : J. Phys. G 50 (2023) 030501 Fulltext: blank - PDF; 2203.05090 - PDF; FERMILAB-PUB-22-094-ND-SCD-T - PDF; Fulltext from Publisher: PDF; External links: Fermilab Library Server; eConf
In : 2021 Snowmass Summer Study, Seattle, WA, United States, 11 - 20 July 2021, pp.030501
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6.
Learning New Physics from an Imperfect Machine / d'Agnolo, Raffaele Tito (IPhT, Saclay) ; Grosso, Gaia (CERN ; Padua U.) ; Pierini, Maurizio (CERN) ; Wulzer, Andrea (Padua U. ; ITPP, Lausanne) ; Zanetti, Marco (Padua U.)
We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties as nuisance parameters for hypothesis testing that is routinely employed in high-energy physics. [...]
arXiv:2111.13633.- 2022-03-30 - 52 p. - Published in : Eur. Phys. J. C 82 (2022) 275 Fulltext: 2111.13633 - PDF; document - PDF;
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7.
Storage Rings and Gravitational Waves: Summary and Outlook / Berlin, A. (New York U.) ; Brüggen, M. (Hamburg U.) ; Buchmueller, O. (Imperial Coll., London) ; Chen, P. (Taiwan, Natl. Taiwan U.) ; D'Agnolo, R.T. (IPhT, Saclay) ; Deng, R. (CAS, SARI, Shanghai) ; Ellis, J.R. (King's Coll. London) ; Ellis, S. (IPhT, Saclay) ; Franchetti, G. (Darmstadt, GSI) ; Ivanov, A. (Vienna U.) et al.
We report some highlights from the ARIES APEC workshop on ``Storage Rings and Gravitational Waves'' (SRGW2021), held in virtual space from 2 February to 18 March 2021, and sketch a tentative landscape for using accelerators and associated technologies for the detection or generation of gravitational waves..
arXiv:2105.00992 ; KCL-PH-TH/2021-28 ; CERN-TH-2021-068. - 17 p.
Fulltext
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8.
Learning Multivariate New Physics / D'Agnolo, Raffaele Tito (IPhT, Saclay) ; Grosso, Gaia (U. Padua, Dept. Phys. Astron. ; INFN, Padua ; CERN) ; Pierini, Maurizio (CERN) ; Wulzer, Andrea (U. Padua, Dept. Phys. Astron. ; INFN, Padua ; CERN ; EPFL, Lausanne, LPTP) ; Zanetti, Marco (U. Padua, Dept. Phys. Astron. ; INFN, Padua)
We discuss a method that employs a multilayer perceptron to detect deviations from a reference model in large multivariate datasets. Our data analysis strategy does not rely on any prior assumption on the nature of the deviation. [...]
arXiv:1912.12155; CERN-TH-2019-226.- 2021-01-27 - 21 p. - Published in : Eur. Phys. J. C 81 (2021) 89 Article from SCOAP3: PDF; Fulltext: PDF;
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9.
CEPC Conceptual Design Report: Volume 2 - Physics & Detector / CEPC Study Group Collaboration
The Circular Electron Positron Collider (CEPC) is a large international scientific facility proposed by the Chinese particle physics community to explore the Higgs boson and provide critical tests of the underlying fundamental physics principles of the Standard Model that might reveal new physics. [...]
arXiv:1811.10545 ; IHEP-CEPC-DR-2018-02 ; IHEP-EP-2018-01 ; IHEP-TH-2018-01. - 2018 - 424.
Fulltext
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10.
Learning New Physics from a Machine / D'Agnolo, Raffaele Tito (SLAC) ; Wulzer, Andrea (CERN ; ITPP, Lausanne ; INFN, Padua ; Padua U.)
We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The virtues of neural networks as unbiased function approximants make them particularly suited for this task. [...]
arXiv:1806.02350.- 2019-01-08 - 20 p. - Published in : Phys. Rev. D 99 (2019) 015014 Article from SCOAP3: PDF; Fulltext: PDF;
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CERN Document Server : 527 registres trobats   1 - 10següentfinal  anar al registre:
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1 D'Agnolo, Raffaele
541 D'Agnolo, Raffaele Tito
541 d'Agnolo, Raffaele Tito
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