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Event generation with Sherpa 3
/ Bothmann, Enrico (Gottingen U.) ; Flower, Lois (Durham U., IPPP ; U. Liverpool (main)) ; Gütschow, Christian (University Coll. London) ; Höche, Stefan (Fermilab) ; Hoppe, Mareen (Dresden, Tech. U.) ; Isaacson, Joshua (Fermilab) ; Knobbe, Max (Gottingen U. ; Fermilab) ; Krauss, Frank (Durham U., IPPP) ; Meinzinger, Peter (Durham U., IPPP ; Zurich U.) ; Napoletano, Davide (Milan Bicocca U. ; INFN, Milan) et al.
Sherpa is a general-purpose Monte Carlo event generator for the simulation of particle collisions in high-energy collider experiments. [...]
IPPP/24/67 ; LTH-1385 ; FERMILAB-PUB-24-0748-T ; ZU-TH 51/24,
CERN-TH-2024-171 ; ZU-TH 51/24 ; MCNET-24-17 ; CERN-TH-2024-171 ; arXiv:2410.22148.
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2024-10-25 12:03 |
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$R^2$--Inflation Derived from 4d Strings, the Role of the Dilaton, and Turning the Swampland into a Mirage
/ Antoniadis, Ignatios (Chulalongkorn U. ; Paris, LPTHE) ; Nanopoulos, Dimitri V. (Athens Academy ; Texas A-M ; Athens U. ; HARC, Woodlands ; CERN) ; Olive, Keith A. (Minnesota U.)
Based on a previously derived superstring model possessing a cosmological sector that mimics Starobinsky inflation, we analyze several questions addressed in the recent literature: the generation of an effective $R^2$-term, the stability of the sgoldstino , the modular symmetry of the inflaton potential and the large distance swampland conjecture. [...]
UMN--TH--4402/24 ; FTPI--MINN--24/22 ; CERN-TH-2024-175 ; arXiv:2410.16541.
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2024-10-24 05:33 |
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Hyperparameter Optimisation in Deep Learning from Ensemble Methods: Applications to Proton Structure
/ Cruz-Martinez, Juan (CERN) ; Jansen, Aaron (Netherlands eScience Center) ; van Oord, Gijs (Netherlands eScience Center) ; Rabemananjara, Tanjona R. (Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Rocha, Carlos M.R. (Netherlands eScience Center) ; Rojo, Juan (CERN ; Vrije U., Amsterdam ; NIKHEF, Amsterdam) ; Stegeman, Roy (U. Edinburgh, Higgs Ctr. Theor. Phys.)
Deep learning models are defined in terms of a large number of hyperparameters, such as network architectures and optimiser settings. [...]
CERN-TH-2024-168 ; arXiv:2410.16248.
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