2023-02-07 07:48 |
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2023-02-07 04:30 |
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2023-02-03 04:40 |
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Massive three-loop form factors: anomaly contribution
/ Fael, Matteo (CERN) ; Lange, Fabian (KIT, Karlsruhe, TTP ; KIT, Karlsruhe, IAP) ; Schönwald, Kay (Zurich U.) ; Steinhauser, Matthias (KIT, Karlsruhe, TTP)
We compute three-loop corrections to the singlet form factors for massive quarks using a semi-analytic method which provides precise results over the whole kinematic range. [...]
arXiv:2302.00693 ; CERN-TH-2023-012 ; P3H-23-006 ; TTP23-002 ; ZU-TH 05/23.
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38 p.
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2023-02-03 04:39 |
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STE-QUEST: Space Time Explorer and QUantum Equivalence principle Space Test
/ Ahlers, Holger ; Badurina, Leonardo ; Bassi, Angelo ; Battelier, Baptiste ; Beaufils, Quentin ; Bongs, Kai ; Bouyer, Philippe ; Braxmaier, Claus ; Buchmueller, Oliver ; Carlesso, Matteo et al.
An M-class mission proposal in response to the 2021 call in ESA's science programme with a broad range of objectives in fundamental physics, which include testing the Equivalence Principle and Lorentz Invariance, searching for Ultralight Dark Matter and probing Quantum Mechanics..
arXiv:2211.15412 ; CERN-TH-2022-202.
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78 p.
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2023-02-03 04:38 |
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2023-02-03 04:36 |
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A unified effective approach to cosmological perturbations
/ Romano, Antonio Enea (ICRA, Pescara ; CERN)
A unified model independent effective description of cosmological perturbations is derived in terms of two effective quantities, playing the role of effective propagation speeds of curvature perturbations and gravitational waves, encoding the effects of the interaction of perturbations at any order, and inducing a modification of the friction term of the perturbations propagation equation. [...]
arXiv:2301.05679 ; CERN-TH-2023-006.
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24 p, 24 p.
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2023-02-02 04:22 |
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2023-01-27 08:40 |
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2023-01-27 08:14 |
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2023-01-26 09:09 |
A quantum analytical Adam descent through parameter shift rule using Qibo
/ Carrazza, Stefano (Milan U. ; CERN ; Technol. Innovation Inst., UAE) ; Robbiati, Matteo (Milan U. ; CERN) ; Efthymiou, Stavros (Technol. Innovation Inst., UAE) ; Pasquale, Andrea (Milan U. ; Technol. Innovation Inst., UAE)
In this proceedings we present quantum machine learning optimization experiments using stochastic gradient descent with the parameter shift rule algorithm. We first describe the gradient evaluation algorithm and its optimization procedure implemented using the Qibo framework. [...]
arXiv:2210.10787; CERN-TH-2022-168; TIF-UNIMI-2022-20.-
2022-11-13 - 6 p.
- Published in : PoS: ICHEP2022 (2022) , pp. 206
Fulltext: 2210.10787 - PDF; document - PDF;
In : 41st International Conference on High Energy Physics (ICHEP 2022), Bologna, Italy, 6 - 13 Jul 2022, pp.206
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