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Your search keyword '"A. Pasquali"' showing total 379 results
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379 results on '"A. Pasquali"'

23. Alpha structure of 16O at high excitation energies by 3He+13C nuclear reactions.

Author

Redigolo, Luigi, Lombardo, Ivano, Dell'Aquila, Daniele, Vigilante, Mariano, Ayketin, Mualla, Baldesi, Lucia, Barlini, Sandro, Camaiani, Alberto, Casini, Giovanni, Ciampi, Caterina, Fabris, Daniela, Cicerchia, Magda, Frosin, Catalin, Gramegna, Fabiana, Marchi, Tommaso, Ordine, Antonio, Ottanelli, Pietro, Pasquali, Gabriele, Piantelli, Silvia, and Russo, Marco

Subjects
EXCITATION (Physiology), NUCLEAR reactions, RESONANCE, ANGULAR distribution (Nuclear physics), CELL nuclei
Abstract

This work investigates high energy states in the self-conjugate 16O nucleus, through the analysis of 13C(3He,α)12C reactions in the 1.4 2.2 MeV bombarding energy range. The cross-section of the decay channel in which an α particle is emitted in coincidence with a 12C residual nucleus in the Hoyle state, leading to four α particles in the final channel, has been measured thanks to a low-threshold and high-resolution array. The occurrence of two low-spin resonant states is highlighted by the analysis of angular distributions, with Jπ = 2+ and 3−, at respective energies of ≃ 24.1 and 24.5 MeV. In this energy window, the observed branching ratios for the transition involving the emission of the Hoyle state appear to be way larger than calculations based on the barrier penetration alone, pointing out to the possible occurrence of largely clustered high-energy states in 16O. [ABSTRACT FROM AUTHOR]

Published
2024
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39. Measurements With A Quantum Vision Transformer: A Naive Approach.

Author

Pasquali, Dominic, Grossi, Michele, and Vallecorsa, Sofia

Subjects
MACHINE learning, PARTICLE physics, IMAGE recognition (Computer vision), QUANTUM computing, TRANSFORMER models
Abstract

In mainstream machine learning, transformers are gaining widespread usage. As Vision Transformers rise in popularity in computer vision, they now aim to tackle a wide variety of machine learning applications. In particular, transformers for High Energy Physics (HEP) experiments continue to be investigated for tasks including jet tagging, particle reconstruction, and pile-up mitigation. An improved Quantum Vision Transformer (QViT) with a quantum-enhanced self-attention mechanism is introduced and discussed. A shallow circuit is proposed for each component of self-attention to leverage current Noisy Intermediate Scale Quantum (NISQ) devices. Variations of the hybrid architecture/model are explored and analyzed. The results demonstrate a successful proof of concept for the QViT, and establish a competitive performance benchmark for the proposed design and implementation. The findings also provide strong motivation to experiment with different architectures, hyperparameters, and datasets, setting the stage for implementation in HEP environments where transformers are increasingly used in state of the art machine learning solutions. [ABSTRACT FROM AUTHOR]

Published
2024
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