CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation

no code implementations • 28 Oct 2024 • Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka, Benjamin Nachman, Dalila Salamani, David Shih, Anna Zaborowska, Oz Amram, Kerstin Borras, Matthew R. Buckley, Erik Buhmann, Thorsten Buss, Renato Paulo Da Costa Cardoso, Anthony L. Caterini, Nadezda Chernyavskaya, Federico A. G. Corchia, Jesse C. Cresswell, Sascha Diefenbacher, Etienne Dreyer, Vijay Ekambaram, Engin Eren, Florian Ernst, Luigi Favaro, Matteo Franchini, Frank Gaede, Eilam Gross, Shih-Chieh Hsu, Kristina Jaruskova, Benno Käch, Jayant Kalagnanam, Raghav Kansal, Taewoo Kim, Dmitrii Kobylianskii, Anatolii Korol, William Korcari, Dirk Krücker, Katja Krüger, Marco Letizia, Shu Li, Qibin Liu, Xiulong Liu, Gabriel Loaiza-Ganem, Thandikire Madula, Peter McKeown, Isabell-A. Melzer-Pellmann, Vinicius Mikuni, Nam Nguyen, Ayodele Ore, Sofia Palacios Schweitzer, Ian Pang, Kevin Pedro, Tilman Plehn, Witold Pokorski, Huilin Qu, Piyush Raikwar, John A. Raine, Humberto Reyes-Gonzalez, Lorenzo Rinaldi, Brendan Leigh Ross, Moritz A. W. Scham, Simon Schnake, Chase Shimmin, Eli Shlizerman, Nathalie Soybelman, Mudhakar Srivatsa, Kalliopi Tsolaki, Sofia Vallecorsa, Kyongmin Yeo, Rui Zhang

We present the results of the "Fast Calorimeter Simulation Challenge 2022" - the CaloChallenge.

Supervised machine learning techniques for data matching based on similarity metrics

no code implementations • 8 Jul 2020 • Pim Verschuuren, Serena Palazzo, Tom Powell, Steve Sutton, Alfred Pilgrim, Michele Faucci Giannelli

A dataset of invoices from a variety of businesses and organizations was preprocessed with a grouping scheme to reduce pair dimensionality and a set of similarity functions was used to quantify similarity between invoice pairs.

BIG-bench Machine Learning

DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC

1 code implementation • 6 Mar 2019 • Riccardo Di Sipio, Michele Faucci Giannelli, Sana Ketabchi Haghighat, Serena Palazzo

A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC.

High Energy Physics - Experiment High Energy Physics - Phenomenology