Zobrazeno 1 - 10
of 1 948
pro vyhledávání: '"Bello, Francesco"'
Autor:
Kakati, Nilotpal, Dreyer, Etienne, Ivina, Anna, Di Bello, Francesco Armando, Heinrich, Lukas, Kado, Marumi, Gross, Eilam
In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been explored in the
Externí odkaz:
http://arxiv.org/abs/2410.23236
The reconstruction of particle tracks from hits in tracking detectors is a computationally intensive task due to the large combinatorics of detector signals. Recent efforts have proven that ML techniques can be successfully applied to the tracking pr
Externí odkaz:
http://arxiv.org/abs/2406.16752
Autor:
Coccaro, Andrea, Di Bello, Francesco Armando, Giagu, Stefano, Rambelli, Lucrezia, Stocchetti, Nicola
Publikováno v:
Mach.Learn.Sci.Tech. 4 (2023) 4, 045040
Experimental particle physics demands a sophisticated trigger and acquisition system capable to efficiently retain the collisions of interest for further investigation. Heterogeneous computing with the employment of FPGA cards may emerge as a trendin
Externí odkaz:
http://arxiv.org/abs/2307.05152
Autor:
Di Bello, Francesco Armando, Charkin-Gorbulin, Anton, Cranmer, Kyle, Dreyer, Etienne, Ganguly, Sanmay, Gross, Eilam, Heinrich, Lukas, Santi, Lorenzo, Kado, Marumi, Kakati, Nilotpal, Rieck, Patrick, Tusoni, Matteo
A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project is aimed to support the development of machine learning algorithms in
Externí odkaz:
http://arxiv.org/abs/2303.02101
Autor:
Di Bello, Francesco Armando, Dreyer, Etienne, Ganguly, Sanmay, Gross, Eilam, Heinrich, Lukas, Ivina, Anna, Kado, Marumi, Kakati, Nilotpal, Santi, Lorenzo, Shlomi, Jonathan, Tusoni, Matteo
Publikováno v:
Eur. Phys. J. C 83 (2023) 596
The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task requiring the use of multiple features and their correlations in the in
Externí odkaz:
http://arxiv.org/abs/2212.01328
Autor:
Di Bello, Francesco Armando, Dreyer, Etienne, Ganguly, Sanmay, Gross, Eilam, Heinrich, Lukas, Kado, Marumi, Kakati, Nilotpal, Shlomi, Jonathan, Soybelman, Nathalie
The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued data is generated conditional on a set of incoming particles. To accel
Externí odkaz:
http://arxiv.org/abs/2211.06406