1 code implementation • 23 May 2023 • Gaia Grosso, Marco Letizia, Maurizio Pierini, Andrea Wulzer
The Neyman-Pearson strategy for hypothesis testing can be employed for goodness of fit if the alternative hypothesis is selected from data by exploring a rich parametrised family of models, while controlling the impact of statistical fluctuations.
no code implementations • 9 Mar 2023 • Gaia Grosso, Nicolò Lai, Marco Letizia, Jacopo Pazzini, Marco Rando, Lorenzo Rosasco, Andrea Wulzer, Marco Zanetti
We here propose a machine learning approach for monitoring particle detectors in real-time.
1 code implementation • 23 Feb 2023 • Andrea Coccaro, Marco Letizia, Humberto Reyes-Gonzalez, Riccardo Torre
Normalizing Flows have emerged as a powerful brand of generative models, as they not only allow for efficient sampling of complicated target distributions, but also deliver density estimation by construction.
no code implementations • 23 Nov 2022 • Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-Gonzalez, Marco Letizia, Anthony L. Caterini
Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors.
no code implementations • 14 Sep 2022 • Paolo Didier Alfano, Marco Rando, Marco Letizia, Francesca Odone, Lorenzo Rosasco, Vito Paolo Pastore
We compare our method with state-of-the-art unsupervised approaches, where a set of pre-defined hand-crafted features is used for clustering of plankton images.
no code implementations • 5 Apr 2022 • Marco Letizia, Gianvito Losapio, Marco Rando, Gaia Grosso, Andrea Wulzer, Maurizio Pierini, Marco Zanetti, Lorenzo Rosasco
We present a machine learning approach for model-independent new physics searches.