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.
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.
1 code implementation • 27 Dec 2019 • Raffaele Tito D'Agnolo, Gaia Grosso, Maurizio Pierini, Andrea Wulzer, Marco Zanetti
It is designed to be sensitive to small discrepancies that arise in datasets dominated by the reference model.
High Energy Physics - Phenomenology High Energy Physics - Experiment
