35 citations · 72 across the 11 of their papers we have counts for
5 papers · 1 filter
Multiple testing for signal-agnostic searches of new physics with machine learning
Gaia Grosso, Marco Letizia
In this work, we address the question of how to enhance signal-agnostic searches by leveraging multiple testing strategies. Specifically, we consider hypothesis tests relying on ma…
Goodness of fit by Neyman-Pearson testing
Gaia Grosso, Marco Letizia, Maurizio Pierini +1
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 o…
Learning new physics efficiently with nonparametric methods
Marco Letizia, Gianvito Losapio, Marco Rando +5
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, n…
Learning New Physics from an Imperfect Machine
Raffaele Tito d'Agnolo, Gaia Grosso, Maurizio Pierini +2
We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds di…
Learning Multivariate New Physics
Raffaele Tito D'Agnolo, Gaia Grosso, Maurizio Pierini +2
We discuss a method that employs a multilayer perceptron to detect deviations from a reference model in large multivariate datasets. Our data analysis strategy does not rely on any…